Dynamic Relationships Management journal CONTENTS Volume 9, Number 2, November 2020 From the President of the Slovenian Academy of Management Jože Kropivšek...................................................................................................................................... 1 Inter-Organizational Relationships Management as a Knowledge Strategy: A Simulation Approach Chulsoon Park...................................................................................................................................... 5 Regional Development in the Era of Industry 4.0 Avigdor Zonnenshain, Gilead Fortuna, Eithan Adres, Ron S. Kenett.................................................... 19 A Multilayer Perceptron Network-Based Analysis to Configure SMES Strategic Entrepreneurship for Sustainable Growth Ardita Todri, Petraq Papajorgji, Francesco Scalera............................................................................... 37 Outbound Open Innovation in Academia: A Systematic Review of the Exploitation Practices and Outcomes in Universities Stephen Ndula Mbieke ........................................................................................................................ 51 A Multi-Informant Assessment of Organizational Agility Maturity: An Exploratory Case Analysis Tomislav Hernaus, Marija Konforta, Aleša Saša Sitar........................................................................... 85 Author Guidelines................................................................................................................................ 105 Aims & Scope The Dynamic Relationships Management Journal is an international, double blind peer-reviewed bi-annual publication of academics' and practitioners' research analyses and perspectives on relationships management and organizational themes and topics. The focus of the journal is on management, organization, corporate governance and neighboring areas (including, but not limited to, organizational behavior, human resource management, sociology, organizational psychology, industrial economics etc.). Within these fields, the topical focus of the journal is above all on the establishment, development, maintenance and improvement of dynamic relationships, connections, interactions, patterns of behavior, structures and networks in social entities like firms, non-profit institutions and public administration units within and beyond individual entity boundaries. Thus, the main emphasis is on formal and informal relationships, structures and processes within and across individual, group and organizational levels. DRMJ articles test, extend, or build theory and contribute to management and organizational practice using a variety of empirical methods (e.g., quantitative, qualitative, field, laboratory, meta-analytic, and combination). Articles format should include, but are not restricted to, traditional academic research articles, case studies, literature reviews, methodological advances, approaches to teaching, learning and management development, and interviews with prominent executives and scholars. Material disclaimer Responsibility for (1) the accuracy of statements of fact, (2) the authenticity of scientific findings or observations, (3) expressions of scientific or other opinion and (4) any other material published in the journal rests solely with the author(s). The Journal, its owners, publishers, editors, reviewers and staff take no responsibility for these matters. Information for Readers Dynamic Relationships Management Journal (ISSN 2232-5867 - printed version & ISSN 2350-367X - on-line version, available in (full text) at the DRMJ website) is published in 2 issues per year. For ordering the printed version, please contact the editor at matej.cerne@ef.uni-lj.si. Call for papers The Dynamic Relationships Management Journal (DRMJ) is inviting contributions for upcoming issues. The manuscript can be submitted per e-mail to the editor (matej.cerne@ef.uni-lj.si). Before the submission, authors should consult Author Guidelines. There is no submission or publication fee. Open Access statement This is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access. Please read »Copyright / licensing conditions« statement for addition info about legal use of published material. Copyright / licensing conditions Authors of the articles published in DRMJ hold copyright with no restrictions and grant the journal right of first publication with the work. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.) Articles published in DRMJ are licensed under a Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0; http://creativecommons.org/licenses/by-nc/4.0/). Under this license, authors retain ownership of the copyright for their content, but allow anyone to download, reuse, reprint, modify, distribute and/or copy the content for NonCommercial purposes as long as the original authors and source are cited. FROM THE PRESIDENT OF THE SLOVENIAN ACADEMY OF MANAGEMENT Assis. Prof. Dr. Jože Kropivšek University of Ljubljana Biotechnical Faculty Dear reader, This spring, new members of the executive board of the Slovenian Academy of Management, who will provide the activities of the Academy in the future and guide its further development, were elected at the Electoral Assembly. Before introducing them (us) in more detail, I would like to express my special thanks to the previous President of the Academy, Professor Dr. Tomaž Čater, for his contribution to the operation and development of the Academy. During his mandate, a new website of the Academy was launched. It represents an important communication platform that presents all the main activities of the Academy transparently and attractively, including the online publication of journals and proceedings to ensure their wider reach and potential impact. This is undoubtedly very important for the authors of the articles in these publications. I am also grateful to all members of the former Board of the Academy, who each individually and collectively contributed to the recognition and development of the individual activities within the Academy. Special thanks go to the first President and founder of the Association, Professor Dr. Rudi Rozman, to whom the solid foundations and key program orientations of the Academy and its visibility in academia and business are attributed. As already mentioned, the new Executive Committee took over the leadership functions in the Academy in Spring 2020, just during the first wave of the Covid-19 pandemic. Special circumstances considerably prolonged the appointment of functions and the transfer of activities from the former management. However, the new Executive Committee became operational in May 2020, while the formal recognition of the President took place in August 2020, when the transition was completed. The new Executive Committee consists of five members, two of whom were already members in the previous mandate. They are Associate Professor Dr. Matej Černe from the School of Economics and Business at the University of Ljubljana, who was appointed as Vice President of the Academy, and Assistant Professor Dr. Nina Tomaževič from the Faculty of Administration of the University of Ljubljana, who continues to work as the Secretary of the Academy. The new members of the Executive Committee are: Associate Professor Dr. Polona Šprajc from the Faculty of Organizational Sciences of the University of Maribor, who has been appointed to the new position of Public Relations of the Academy, Rebeka Žgalin Koncilja from the School of Economics and Business of the University of Ljubljana, who has taken over the position of Treasurer of the Academy, and myself, Assistant Professor Dr. Jože Kropivšek from Biotechnical Faculty of the University of Ljubljana as President of the Academy. The Academy's Supervisory Board remained unchanged from the previous mandate, with Dr. Vojko Toman from Slovenian Intellectual Property Office as President and Professor Dr. Borut Rusjan from the School of Economics and Business at the University of Ljubljana and Assistant Professor Dr. Milena Alič from ALZIT d.o.o. as members. There are no revolutionary changes planned in the key activities and main dedications of the Slovenian Academy of Management. The Academy continues with its dedication towards uniting academics, researchers, and experts from the field of management in the Republic of Slovenia and broader. The Academy will continue to act following its mission and thus organize conferences and other events, publish academic and professional literature in the field of management and organize other education, training, and research activities. Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 1 One of the first challenges we faced immediately after taking over the management of the Academy was the new situation dictated by the pandemic measures of Covid-19. Therefore, we had to cancel our academic conference entitled "Integrating organizational research: individual, team, organizational and multilevel perspectives", for which the Organizing Committee, led by Dr. Aleša Saša Sitar with strong support from colleagues from the University of Zagreb, has already received a sufficient number of high-quality papers. The decision was to postpone it until the next year, and we are currently planning to hold it in Bled in June 24-25 2021. This period is also very demanding for communication and maintaining contacts with members of the Academy and the wider community. We have decided to approach this more systematically, and the first step was the establishment of the Academy PR. Thus, we will use all the possibilities offered by the Academy's website, while at the same time look for other ways to make "virtual" contacts to exchange opinions and messages via social networks, especially on Linkedln. One of the main priorities of the new management is to expand the membership and ensure closer cooperation among members, so we are planning several measures in this respect. One of them is enrolling young graduates immediately after graduation, which could bring fresh ideas and thus provide the development of the Academy. I take this opportunity to invite all readers of the Dynamic Relationships Management Journal to become a member of the Academy. This invitation is extended to everyone - Slovenian and foreign researchers, as well as experts and practitioners in the field of management and related fields. The more of us, the more the Academy will be able to create as a community, which will multiply the benefits and new opportunities for everyone and enrich each of us. Let me briefly present the main activities of the Academy. Most of them are already well established and will just be continued, whereas some will be upgraded. One of the main activities of the Academy is the organization of scientific conferences, from which one is international, and the other more local. International scientific conferences are primarily focused on the presentation of the latest academic findings in a specific area of management, on the exchange of opinions, and on establishing links among the participants. The purpose of the Slovenian scientific conference is to connect the Slovenian professional public with researchers, i.e. to transfer knowledge into practice, which is the fundamental goal of the Academy. A similar role of knowledge transfer and exchange is played by the two journals, whose development is successfully managed by both editors. The international journal of the Academy, i.e. Dynamic Relationships Management Journal, is managed by Associate Professor Dr. Matej Cerne as an editor-in-chief. The quality of the journal is proven by its inclusion in the Scopus database and a rising number of manuscripts that are being submitted from all over the world. We would like to receive even more high-quality articles and manuscripts that put the dynamic relationships at the centre of their interest to directly address the aim and scope of the journal, so we would again like to ask academics and professionals to submit the outcomes of their research. Manuscripts can include literature reviews, theoretical contributions as well as qualitative and/or quantitative research. Soon, the editorial process will be transitioned online, including on-line submission. The Academy will also continue with the publication of the Slovenian journal titled "Izzivi managementu" (Management Challenges). It is more practically oriented and dedicated mostly to helping managers at their everyday work. Besides managers, the journal's targeted readers are also academics who wish to learn more about the practical aspects of management and related areas. The journal's editor remains Assistant Professor Dr. Lidija Breznik. For the future, we plan to organize more forms of socializing, with the emphasis on formal and less formal debates and/or the exchange of opinions, knowledge, and experience. I would like to mention our regular activity "debate evenings", which is organized by Assistant Professor Dr. Nina Tomazevic. The current situation and the circumstances caused by the pandemic force us to look for new, mainly virtual ways and media to ensure their execution. One of the possibilities is certainly the transition to the hybrid or full online form of debate evenings using any of the Virtual Meeting Platforms and/or the introduction of thematic socializing via social networks. All those interested in this way of socializing, 2 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 2 I am already inviting to join us and help to shape the first steps towards building this platform. Although the main direction of the Academy and its activities will remain unchanged, we plan to grow and expand the activities in the (near) future, with a focus on quality, and on improving its accessibility. This is only possible by increasing the number of active members and membership in general, including through expansion abroad. We will be very pleased with all the initiatives we will receive from you, dear readers, and especially with your commitment and dedication to the activities of the Academy. We are also open to more constructive cooperation with related societies and associations in the search for synergy effects. The pandemic and with it associated measures of isolation cause many challenges to establish and maintain contacts, but I am convinced that in the Academy we will successfully continue to maintain our connections also through the rational and imaginative use of all modern technological possibilities. This will help us to survive as individuals and, above all, to move forward as a community that transcends physical boundaries. This opens up entirely new possibilities for the Academy in its further development. Finally, I have the pleasant duty to present some highlights of the new issue and invite you to read it. I am confident that everyone will find something interesting and useful in this issue of the journal. It contains five articles covering a range of different topics, research approaches, and levels of analysis. The first article was written by Chulsoon Park and focuses on the management of inter-organizational relations, thus fitting directly into the narrow framework of the DRMJ. Based on an agent-based model and the theory of the organizational learning curve, the author has shown that the knowledge performance of organizations can be changed by the way the structural factors of an egonetwork are managed. The second model was created by a team consisting of Avigdor Zonnenshain, Gilead Fortuna, Eithan Adres, and Ron S. Kenett, who focused on regional development in the Industry 4.0 era. The paper presents the theoretical foundations of an integrated approach that includes an assessment using the Industrial Maturity tool for Advanced Manufacturing (IMAM), applied to the case of the Galilee region. The third paper included in this issue was co-authored by Ardita Todri, Petraq Papajorgji, and Francesco Scalera who analyzed the close interaction between organizational networking and financial mechanisms of growth and sustainable growth of SMEs operating in Albania. The authors used multivariate regressions and multilayer artificial neural perceptron networks to assess the growth of SMEs and promote their sustainable growth process using the age of the firm, which is divided into the start-up, grown, and maturity phases. The fourth paper was written by Stephen Ndula Mbieke and presents a systematic literature review of the literature on Outbound Open Innovation in the academic world. The author analyzed the literature in 42 academic journals and 118 articles specifically dealing with this research topic. This review is the first to systematically analyze the literature in terms of the financial benefits that universities derive from technology transfer and how income can best be generated. Finally, the fifth paper included in this issue was co-authored by Tomislav Hernaus, Marija Konforta, and Aleša Saša Sitar and provides a multi-informal assessment of agility maturity from an organizational perspective. The authors used Organizational Agility Maturity Model within a case study of an oil company to determine whether and to what extent there was an agreement between management and employees (informants) on the assessment of agility across different hierarchical levels. With the desire to cooperate within the Academy, and to create new content for the Dynamic Relationships Management Journal, I wish you many new scientific and professional insights. Stay healthy! Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 3 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 4 Vol. 9, No. 2, 5-18 doi:10.17708/DRMJ.2020.v09n02a01 INTER-ORGANIZATIONAL RELATIONSHIPS MANAGEMENT AS A KNOWLEDGE STRATEGY: A SIMULATION APPROACH Chulsoon Park Department of Business Administration, Sookmyung Women's University, Seoul, Korea cspark@sookmyung.ac.kr - Abstract - Firms absorb knowledge from their partners, make it their own, and use it for innovation. The knowledge performance of a firm embedded in an inter-organizational network can vary depending on how concentrated its ties are and the number of direct ties. This study used an agent-based model and the organizational learning curve theory as basis to show that the knowledge performance of firms can be modified by the way in which the structural factors of an ego network are managed. In particular, the concentration of tie strength decreases the average level of a firm's knowledge profile; that is, a firm's knowledge level decreases when it has strong ties with a particular firm and weak links with others. The number of direct ties, the so-called node degree, increases the diversity of knowledge in the long run. The cumulative knowledge reduction effect of the concentration of tie strength varies depending on the network type. In a random network, the average knowledge reduction effect is mitigated by a high absorptive capacity, whereas the reduction effect is strengthened in a scale-free network. A knowledge strategy is presented to assist firms in effectively accumulating knowledge toward sustainable growth. Keywords: inter-organizational network, concentration of tie strength, node degree, knowledge performance, agent-based model 1. INTRODUCTION Knowledge is a source of technological innovation. A firm obtains knowledge through its inter-organizational networks. Firms innovate not only by their own internal research and development but also by acquiring skills, knowledge, and information from other firms through partnerships (Choi, 2020). In particular, firms in rapidly developing industries, such as the biotechnology and information and communications industries, strive to secure resources and reduce uncertainty through a variety of cooperative relationships, such as strategic alliances, consortiums, and joint ventures (Hoffmann, 2007). Firms drive innovation through a distributed process based on knowledge flows across organizational boundaries, so-called open innovation (Ches-brough and Bogers, 2014). According to the relational view (Dyer & Singh, 1998), business-to- business relationships can be an important component of a firm's competitive advantage and can lead to better performance. To successfully implement a firm's strategy, it is not possible to rely solely on one relationship. Strategies for accessing a variety of external resources through partnerships in different ways with different partners can be useful. How a set of relationships, rather than one relationship, is created and managed determines a firm's knowledge performance (Hoffmann, 2007). Identifying the relationship between network structure and innovation performance has been a major concern for management. A knowledge-sharing network that facilitates knowledge exchanges between a central firm and its allied partners can be a source of competitive advantage for a firm (Dyer & Hatch, 2004). The type of network relationship appropriate for a firm has been debated widely be- Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 5 Chulsoon Park: Inter-Organizational Relationships Management as a Knowledge Strategy: A Simulation Approach cause maintaining relationships with multiple partners can be costly (Lavie, 2007). Following Ahuja (2000), this study defines an inter-organizational tie as a voluntary arrangement between independent organizations to share knowledge. The influence of tie strength on knowledge performance has been discussed mainly at a dyad level. If the trust and communication frequency between two firms is high, they are said to be connected by a strong tie. A strong tie facilitates the flow of sensitive and highlevel information (Rowley, Behrens & Krackhardt, 2000), but a weak tie allows access to new and diverse information (Hansen, 1999). However, in the ego network of a firm composed of multiple ties, weak and strong connections exist together. If there are multiple ties together, how does the distribution of the relationships relate to knowledge performance? To our knowledge, few studies have revealed the relationship between tie strength distribution and knowledge performance in the presence of multiple ties. This study focuses on the concentration of a firm's tie strength when several ties exist and identifies the relationship between the concentration and knowledge performance. This study investigates how the structural factors of an ego network affect knowledge performance. Specifically, it argues that knowledge performance can vary depending on tie-strength concentration and the number of direct ties. To this end, an organizational learning model, in which knowledge is exchanged through a network, was built as an agent-based model. Each firm is set to accumulate knowledge by developing knowledge internally and by absorbing knowledge externally in situations in which multiple knowledge domains exist. A simulation revealed that the higher (lower) the tie strength concentration, the lower (higher) is the average level of knowledge. If the number of direct ties is large, the diversity in knowledge domains increases. The average reduction effect of the tie-strength concentration and the increase effect of changes in the number of direct ties vary depending on the network topology or a firm's absorptive capacity. The contributions of this study are as follows. First, we identified the relationship between structural factors and knowledge performance. We developed a dynamic model that comprehensively considers firm-, relationship-, and network-level fac- tors to clarify the relationship between structural factors and performance in various environments. Second, we present an inter-organizational relationships management framework as a knowledge strategy. Based on the relationship between structural elements and knowledge performance, we provide practical implications by presenting a relationship management plan that fits the objective pursued by each firm. This paper is organized as follows. Section 2 summarizes previous research related to this study, and Section 3 presents an agent-based model for knowledge diffusion in an inter-organizational network. Section 4 analyses the experimental results. Section 5 discusses the results and presents a knowledge strategy framework. Finally, Section 6 summarizes the findings and outlines the limitations and the direction of future research. 2. LITERATURE REVIEW Phelps, Heidl, and Wadhwa (2012) defined knowledge networks as networks consisting of nodes, which is the repository of knowledge. The nodes can be either firms or individuals that create, search, assimilate, and exploit knowledge. The performance of the knowledge network varies according to various factors in the network (Al-Jabri & Al-Busaid, 2018). Phelps et al. (2012) classified structural, relational, nodal, and knowledge properties as the main elements. Structural elements relate to how the relationships are connected—where they are located in the network, how they are connected with directly connected partners, what kind of relations exist among the partners, and what form the whole network takes. These structural factors can affect knowledge performance. Node degree is the number of direct ties of an incident to a node (Borgatti, Everett & Johnson, 2013). In studying the relationship between node degree and performance, Ahuja (2000) argued that the higher the number of direct ties, the higher is the innovation performance. A large number of direct links can lead to higher innovation performance due to knowledge sharing, complementarity, and economies of scale. Burt (1992) proposed the concept of a structural hole and argued that if the focal firm's partners were not connected with each other, the informa- 6 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 tion power of the focal firm would be higher. Empirical studies have shown that structural holes improve knowledge performance (Baum, Calabrese & Silverman, 2000; McEvily & Zaheer, 1999), whereas other studies have found that without structural holes, innovation improves (Ahuja, 2000; Schilling & Phelps, 2007). Chen, Zhang, Zhu, and Mu (2020) suggested that the impact pattern of the network positions of organizations on their performance likely varies with the network structure and composition in different inter-organizational contexts. Specifically, they argued that the node degree and structural hole of the research institute respectively affect the performance in an inverted U-shaped manner and in a positive linear manner in the homogeneous university-researcher collaboration network, but have different relationships in the other types of collaboration networks. In addition, the whole network topology can affect the firm's knowledge performance. Network topology refers to a structure of how firms are connected. Typical network topologies include random (Erdos & Renyi, 1959), small-world (Watts & Strogatz, 1998), and scale-free (Barabasi & Albert, 1999) networks. A random network refers to a network in which nodes are randomly connected. A regular network refers to a network that is regularly connected to its partners. A small-world network can be constructed by creating a regular network and randomly selecting a small number of links and connecting them to other nodes. A scale-free network is a network in which the degree distribution of nodes follows a power law. The diversity of information can be increased by becoming a "small world" because there is a shortcut between dense groups (Schilling & Phelps, 2007). Using an agent-based model, Kim and Park (2009) argued that small-world networks are more efficient in diffusing knowledge than are regular or random networks. Relational elements refer to the type of relationship each node has. A representative example is tie strength. The relationship between two firms is classified as strong or weak based on the tie strength. In a relationship with a strong tie, firms frequently communicate with each other based on trust, intimacy, and reciprocity, whereas in a relationship with a weak tie, firms are remote from each other or occasionally communicate and exchange information (Capaldo, 2007; Granovetter, 1973). Based on the level of intimacy and reciprocity, two firms with a strong tie can share more sensitive information and tacit knowledge than those with weak ties (Granovetter, 1973; Marsden, 1984). Strong ties, as a medium for reliable information delivery, promote the flow of a stream of advanced information and refined knowledge (Rowley et al., 2000). However, an advantage of a weak tie is that it enables access to new and diverse information (Hansen, 1999). Franco and Esteves (2020) argued that weak ties between clusters-groups connected by strong ties—play an important role in knowledge transfer among inter-cluster networks. Studies conducted from a social capital perspective state that links with other firms positively affects a firm's knowledge performance (Carey, Law-son & Krause, 2011). Cousins, Handfield, Lawson, and Petersen (2006) argued that enhancing social relationships between suppliers and buyers contribute to the formation of relational capital, making communication between firms smoother. Dyer and Singh (1998) argued that ties between two firms lead to investments in idiosyncratic assets, which promotes the flow of knowledge. Furthermore, they emphasized that this increase in investment and the facilitation of knowledge flows develop into a self-enforcing structure that further strengthens the tie between the two. Idrees, Vasconcelos, and Ellis (2018) argued that a cooperative-competitive tension of dyadic relationships facilitated knowledge sharing between five-star hotels. Nodal properties refer to a firm's own characteristics. For example, a firm's high absorptive capacity (Cohen & Levinthal, 1990) facilitates the easy absorption of knowledge from partners (Zhao & Anand, 2009). Xie, Wang, and Zeng (2018) found that absorptive capacity mediated the relationship between inter-organizational knowledge acquisition and firms' innovation performance. Lastly, knowledge performance can vary according to various properties of knowledge. Codified knowledge is more likely to diffuse (Simonin, 1999), and complex and tacit knowledge is difficult to absorb, which can be alleviated by frequent communication (McEvily & Marcus, 2005). According to Balle, Steffen, Curado, and Oliveira (2019), managerial knowledge can be transferred in more alternative ways than technical knowledge. Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 7 Chulsoon Park: Inter-Organizational Relationships Management as a Knowledge Strategy: A Simulation Approach 3. MODEL The knowledge diffusion model sets a firm as one agent, and each agent corresponds to a node in the knowledge network. Nodes are connected to each other by ties. The diffusion of knowledge occurs between firms linked by a tie. One tie could be a purchase contract, joint research, or joint development. This knowledge diffusion model is based on the work of Kim and Park (2009), but is extended to various network topologies and modified in knowledge acquisition logic. The network topologies considered in this simulation are random, small-world, and scale-free networks. It is assumed that all firms are connected as one network, which means that there are no isolated firms. A scale-free network is made using a preferential attachment, as proposed by Barabasi and Albert (1999). The preferential attachment method starts from one link and adds a node with a fixed number of links (PA-degree) to connect them. When a new node is added to an existing node, it is added probabilistically in proportion to how many links the existing node has. The organizational learning theory was developed by Argote and colleagues, and many empirical studies have been conducted based on it (Argote, 2013; Argote, Beckman & Epple, 1990; Epple, Argote & Devadas, 1991; Epple & Argote, 1996; Epple, Argote & Murphy, 1996). Based on those previous studies, this study models the way in which a firm accumulates knowledge assets based on the organizational learning curve equation suggested by Epple et al. (1991). A firm's knowledge assets are represented by a single knowledge profile (KP), and a knowledge profile consists of multiple knowledge domains. It is assumed that all companies build knowledge in a knowledge profile consisting of the same D knowledge domains. Each firm accumulates knowledge in two ways. One is through research and development inside the firm itself, and the other is by absorbing the knowledge of partners tied with the firm. Based on Epple et al.'s (1991) organizational learning curve equation, the equation for accumulating knowledge is as follows: kid,t = «iKi(jit + frmax [Kjdjt - Kldt, 0] (1) where kid t is the increment of knowledge accumulated in knowledge domain d at time t by firm i, and Kid t is the cumulative level of knowledge accumulated in knowledge domain d at time t by firm i. The first term on the right-hand side is the knowledge gained through research and development inside the firm; a, denotes a firm's internal innovation capability, which is the capability obtained through internal research based on the firm's accumulated knowledge. The larger oq is, the greater is the internal research capability that firm i can create by using existing accumulated knowledge. In Equation (1), Aj is the coefficient of the effect of the learning curve of firm i. The larger Aj is, the greater is the learning ability that can be generated through existing knowledge. The second term on the right-hand side is the other source from which firms can build their knowledge and absorb knowledge of partners connected to them for their own knowledge enhancement; Pi is firm i's absorptive capacity (Cohen & Levinthal, 1990). If the partner firm's knowledge concerning the knowledge domain is greater, the focal firm absorbs the knowledge gap multiplied by Pj. Among the partner firms that are connected to the firm, firm j is probabilistically selected to absorb such knowledge. The probability pjj that firm i selects partner firm j as a source of knowledge is made proportional to the tie strength as follows: (2) where Sjj refers to the tie strength of firms i and j, and N(i) is the set of partners directly connected to firm i. However, some of the knowledge of a firm disappears or becomes obsolete over time (Epple et al., 1996). Thus, the cumulative level of knowledge of firm i, considering the depreciation of this knowledge, is aiK^t + Pimax [Kjd>t - Kid,t, 0] where 6 denotes the depreciation rate of knowledge, which is the rate at which knowledge becomes obsolete from the cumulative knowledge in the previous period. In industries with rapid innovation and change, the value of 5 is relatively large, and in industries in which technology has reached maturity, the value is relatively small. Equation (3) states that the knowledge of firm i at time t + 1 decreases at the depreciation rate of the cumulative knowledge at 8 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 the previous time, increases in proportion to the internal capability of the company, and finally increases by absorption of knowledge outside the firm. The equation encompasses the entire life cycle of knowledge by including two sources of knowledge growth and the depreciation of knowledge. The explanatory variable, tie-strength concentration, is measured by Herfindahl-Hirschman Index (HHI). The concentration of firm i's tie-strength is defined as follows: HHI, - SjeN(i) sij (4) The HHI has a maximum value of 1, and the larger the value, the more concentrated is the tie-strength. Another explanatory variable—node degree—is defined as the number of direct ties connected to each node (Newman, 2010). The dependent variables are KPMean and KP-Stdev. KPMean is the arithmetic mean of all knowledge domains in a knowledge profile, and KPStdev is the standard deviation, as shown in the following equations: KPMeanit = ¿£g=1Kidit (5) (6) The network used in this model consists of 100 nodes. The parameters used in the model are designated as random variables, as summarized in Table 1, with reference to Kim & Park (2009), to allow for the heterogeneity of firms. Fifty repetition experiments were performed on one network topology. Simulations were performed up to 10,000 ticks, at which the cumulative knowledge of all nodes was stable. Short-term (100 ticks) and long-term (10,000 ticks) data were collected. The agent-based model presented in this study was implemented using NetLogo 6.1.1 (Wilensky, 1999), and the simulation experiment used the BehaviorSpace tool built into NetLogo. 4. RESULTS A hierarchical regression analysis was performed, estimated by the following equations: KPMean, = fn + fta, + £/?, + + &HHI, + p/iiiXff; + PsHHIiXPi + P^HHIiXAi KPStdevl ~ ßQ + ft«; + ß2ßi + ß3Ät + ß^Degreei + ßsDegreeiY-cii + ß6Degreeixßi + /^Degree; xA; (8) The standardized coefficients and significance level of each variable obtained as a result of the regression analysis are summarized in Tables 2 and 3. Table 1: Parameters for simulation. Parameter Description Value or Distribution «i Knowledge development capability of firm i ~U[0,amax] max Maximum value of at 0.002 ßi Absorptive capacity of firm i ~U[0,ßmax] ßmax Maximum value of ßt 0.2 Kid. 0 Initial value of knowledge domain d of firm i ~U[0,Kmax] Kmax Maximum value of Kid 0 0.1 h Learning rate of firm i ~U[0,Amax] ^max Maximum value of 0.05 S Depreciation rate of knowledge 0.001 SU Tie strength of firm i and j ~U[ 0,1] KDnum Number of knowledge domains 10 PA-degree Number of links created by one node in preferential attachment 3 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 9 Chulsoon Park: Inter-Organizational Relationships Management as a Knowledge Strategy: A Simulation Approach Table 2: Results of the hierarchical regression analysis for KPMean Ticks = 100 Dependent Variable = KPMean Topology Random Small-World Scale-Free Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Alpha 0.590 *** 0.591 *** 0.636 *** 0.636 *** 0.584 *** 0.584 *** Beta 0.564 *** 0.564 *** 0.495 *** 0.496 *** 0.529 *** 0.530 *** Learning -0.028 *** -0.029 *** -0.051 *** -0.052 *** -0.019 * -0.019 * HHI -0.050 *** -0.051 *** -0.034 *** -0.035 *** -0.048 *** -0.050 *** HHIxAlpha 0.004 0.011 -0.036 *** HHIxBeta -0.010 -0.021 * -0.038 *** HHIxLearning -0.031 *** 0.005 0.012 Adj. R2 0.677 0.678 0.635 0.635 0.636 0.639 F 2619.323 *** 1503.098 *** 2172.542 *** 1243.896 *** 2187.799 *** 1265.444 *** F change 5.454 *** 2.716 * 13.586 *** Ticks = 10,000 Dependent Variable = KPMean Topology Random Small-World Scale-Free Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Alpha 0.311 *** 0.312 *** 0.321 *** 0.321 *** 0.301 *** 0.301 *** Beta 0.410 *** 0.410 *** 0.331 *** 0.331 *** 0.403 *** 0.404 *** Learning 0.014 0.013 0.005 0.005 0.018 0.017 HHI -0.045 *** -0.047 *** -0.021 + -0.021 + -0.025 * -0.027 * HHIxAlpha 0.013 0.001 -0.043 *** HHIxBeta 0.025 * -0.006 -0.035 ** HHIxLearning -0.045 *** 0.006 0.000 Adj. R2 0.270 0.272 0.205 0.205 0.258 0.261 F 463.465 *** 268.300 *** 323.770 *** 184.982 *** 436.041 *** 252.961 *** F change 6.164 *** .151 6.822 *** Notes: Standardized coefficients are presented. ***, **, *, and + denote significance at the 0.1%, 1%, 5%, and 10% levels, respectively. For the dependent variable KPMean, Model 1 included only internal development capability (Alpha), absorptive capacity (Beta), learning curve effect (Learning), and HHI; Model 2 added interaction terms between HHI and other variables. In the short term (100 ticks), Model 1 had significant coefficients for all variables in all topologies. In particular, Alpha and Beta were positive, and Learning and HHI were negative. This confirms that HHI has the effect of decreasing the average of KP. Model 2, which added interaction terms, had different results depending on the network topology. In the random network, the coefficient of HHIiXAi was significant and negative (= -0.031, p < 0.001). This means that HHI reduces the average of KP, but the higher the learning rate, the stronger is the effect. In the small-world network, the coefficient of/////¡x/?; was significant and negative ( % = -0.021, p < 0.05). This 10 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 means that the HHI's KP average reduction effect is enhanced as the absorptive capacity increases. In the scale-free network, the coefficients of /////¡xa:; and HHIiXfa were significant and negative (= -0.036, p < 0.001; % = -0.038, p < 0.001). This confirms that HHI's KP average reduction effect can vary depending on the internal development and absorptive capacity. In short, the results indicate that the short-term KP average level decreases as the HHI increases, and that the moderating effect of the firm's capabilities differs depending on the topology. The results for 10,000 ticks (long term) were as follows. First, the results differed from those in the short term in that the learning curve effect was not significant. The reduction effect of HHI still was sig- nificant in the long term, although marginally significant in small-world networks. Unlike the results in the short term, the moderation effect of absorptive capacity appeared in the random network, in which the coefficient of in the long term was positive and significant 0.025, p < 0.05). This means that in the long term, HHI's KP average reduction effect can be mitigated by the absorptive capacity. Figure 1(a), drawn according to the guidelines of Cohen, Cohen, West, and Aiken (2002), shows how the KP reduction effect of HHI is affected by a high (average + standard deviation), average, and low (average - standard deviation) level of the moderating variable. If the absorptive capacity is large, the reduction effect is mitigated. In the scale-free net- Figure 1: The moderation effect of absorptive capacity in the long term Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 11 Chulsoon Park: Inter-Organizational Relationships Management as a Knowledge Strategy: A Simulation Approach work, the short- and long-term scenarios had almost similar effects. In particular, the coefficient for the moderating effect of absorptive capacity was significant and negative. This means that the higher the absorptive capacity, the stronger is the reduction effect of HHI. This is confirmed in Figure 1(b). In firms with low absorptive capacity, HHI's KP average reduction effect may lead to an increase effect on the KP average. This would mean that firms with low absorptive capacity are not significantly affected by the high concentration of relationships in the scale-free networks. For KPStdev, in the short term (100 ticks) the coefficients of Alpha, Beta, and Degree were significant in Model 1, which considered only main effects. The coefficients of Alpha and Beta were positive, Table 3: Results of the hierarchical regression analysis for KPStdev Ticks = 100 Dependent Variable = KPStdev Topology Random Small-World Scale-Free Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Alpha 0.390 *** 0.390 *** 0.243 *** 0.243 *** 0.371 *** 0.371 *** Beta 0.306 *** 0.307 *** 0.386 *** 0.386 *** 0.244 *** 0.245 *** Learning -0.011 -0.011 -0.001 -0.001 -0.011 -0.011 Degree -0.119 *** -0.119 *** -0.032 * -0.034 ** -0.131 *** -0.131 *** DegreexAlpha 0.000 0.014 -0.005 DegreexBeta 0.029 * -0.027 * 0.014 DegreexLearning 0.019 0.009 0.008 Adj. R2 0.262 0.263 0.202 0.203 0.219 0.219 F 445.677 *** 256.142 *** 317.686 *** 182.663 *** 351.386 *** 200.992 *** F change 2.790 * 2.301 + .584 Ticks = 10,000 Dependent Variable = KPStdev Topology Random Small-World Scale-Free Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Alpha -0.433 *** -0.433 *** -0.445 *** -0.445 *** -0.392 *** -0.391 *** Beta 0.286 *** 0.287 *** 0.207 *** 0.207 *** 0.270 *** 0.271 *** Learning 0.007 0.007 -0.001 -0.001 -0.001 -0.002 Degree 0.051 *** 0.052 *** 0.038 ** 0.038 ** 0.083 *** 0.084 *** DegreexAlpha -0.047 *** -0.020 + -0.014 DegreexBeta 0.025 * -0.003 0.050 *** DegreexLearning 0.024 * -0.009 0.016 Adj. R2 0.267 0.270 0.248 0.248 0.227 0.230 F 457.135 *** 265.350 *** 412.803 *** 236.396 *** 368.872 *** 214.149 *** F change 7.322 *** 1.141 6.288 *** Notes: Standardized coefficients are presented. ***, **, *, and + denote significance at the 0.1%, 1%, 5%, and 10% levels, respectively. 12 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 and the coefficient of node degree was negative and significant in all topologies. This confirms that various knowledge domains are learned evenly in the early stages, because the number of direct relationships is much higher. In the random network, the larger the absorptive capacity, the more the reduction effect on the KP standard deviation of the node degree was mitigated, whereas the reduction effect was strengthened in the small-world network. As time passed, the reduction effect on the KP standard deviation of the node degree changed to an increase effect. The coefficients of the node degree all changed to positive and were significant. In other words, the more connected firms are, the more diverse their knowledge base becomes. In the random and scale-free networks, the increase effect was strengthened by the absorptive capacity. These results are confirmed by Figures 1(c) and 1(d). 5. DISCUSSION 5.1 Tie-strength concentration and node degree Firms' decision-making and behavior are affected by how much they depend on their resources and their constraints (Pfeffer & Salancik, 2003). If only a small number of firms in a network have access to resources, their dependence on resources is intensified (Pfeffer & Salancik, 2003). The deeper the dependence on resources, the higher is the interdependence between firms (Burt, 1983). Interdependence between firms enhances the strength of ties. In ties that have been strengthened, knowledge can be effectively transferred with little effort. Especially in the case of tacit or complex knowledge, it is easy to communicate when there are strong ties (Uzzi, 1997). However, strong ties also can cause two firms to become stuck (Lechner, Frankenberger & Floyd, 2010), fall into collective blindness (Nahapiet & Ghoshal, 1998), or become complacent (Villena, Revilla & Choi, 2011), which may hinder the acquisition of knowledge. Moreover, when there is only a limited range of knowledge, knowledge that can be learned from a partner with whom a firm has a strong tie is quickly exhausted. In other words, if firms communicate frequently with each other, new knowledge that can be learned from partners inevitably will decrease, as knowledge is learned before it is accumulated inter- nally and becomes part of the capabilities of the firm. Meanwhile, if the tie strength is not concentrated and is distributed evenly, the partner firms have time to accumulate knowledge by developing their internal capabilities. Therefore, the less concentrated the tie strength, the greater the cumulative knowledge of a firm becomes. This finding is consistent among all network topologies. However, the moderating effect of absorptive capacity varies depending on the network topology. In a random network, the reduction effect of concentration is alleviated, but in a scale-free network, the reduction effect is strengthened further. This result occurs due to the characteristics of the network topology. Compared with random networks, scale-free networks have a hub-and-spoke structure, so one firm is likely to be connected to a hub. Firms with high absorptive capacity depend more on the knowledge profile of the hub than do firms with low absorptive capacity. As a result, the reduction effect of the tie-strength concentration is further enhanced. A direct tie can have a positive effect on knowledge performance and a negative effect as well. The larger the number of direct ties, the more likely it is that knowledge will be exchanged with various firms, which would enable a firm to broaden its knowledge profile to various domains (Ahuja, 2000; Owen-Smith & Powell, 2004). However, maintaining too many relationships may cost more than the benefit generated from it (Rothaermel & Alexandre, 2009). With regard to achieving a knowledge profile that encompasses multiple domains, various sources exist for knowledge accumulation. In the short term, diversity in knowledge domains is low as a firm connects with multiple sources, but in the long term, the diversity of knowledge increases. In the setting of the experiment, all firms start with only one knowledge domain which is randomly chosen. In the short term, the more a firm is connected with multiple partners, the more it can accumulate knowledge stocks in diverse knowledge domains, so the deviation among knowledge domains decreases. As time passes, each firm can increase exponentially the knowledge level of some specific knowledge domains according to its internal innovation capability and learning curve effect (Epple et al., 1991). In firms which are more connected with these various partners in terms of knowledge profile, the deviation among knowledge domains increases. This Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 13 Chulsoon Park: Inter-Organizational Relationships Management as a Knowledge Strategy: A Simulation Approach phenomenon has been confirmed by several empirical studies about strategic alliances in the biotechnology industry (e.g., Xu & Cavusgil, 2019; Zhang, Baden-Fuller & Mangematin, 2007). These results help resolve the conflicting results regarding node degree and performance. Whereas some researchers (e.g., Ahuja, 2000) argued that the higher node degree made its innovation performance greater, others (e.g., Rothaermel & Alexandre, 2009) suggested that increasing reliance on partners has a negative effect on knowledge performance. The present finding suggests that the number of direct ties with suppliers has positive or negative effects, which can change depending on the period. This was revealed by comparing the short-term and long-term results in the regression analysis. The results indicate that in the beginning, the greater (lesser) the number of direct ties, the lesser (greater) is the knowledge diversity, and over time, this knowledge diversity increases (decreases). 5.2 Relationship management as a knowledge strategy A firm can design and manage two structural elements to create its knowledge profile. The following knowledge strategy framework can be considered. In the long run, if a firm wants to increase its overall knowledge and focus on a specific field at the same time, it could benefit by maintaining evenly distributed ties with other firms and by expanding the number of its direct ties (Figure 2, top left). In the case of high-tech products, in which multiple knowledge fields are applied in a complex manner, such as electric vehicles, this strategy is suitable because it is important to focus on knowledge about a specific field while simultaneously developing related technologies. In the case of a mature industry, such as a gasoline-powered vehicle, a high level of knowledge must be accumulated evenly in various knowledge fields. Therefore, it is beneficial to manage relationships with fewer direct ties at low concentration (Figure 2, bottom left). In the case of a high-tech product, such as a personal mobility device, superiority in a specific technology is necessary. In the case of products that require a relatively low level of technology, it is necessary to maintain numerous direct ties and focus on major partners to manage relationships (Figure 2, top right). Lastly, if a product requires a relatively uniform skill, such as a bike, but do not need a very high level of skill, it is appropriate to manage relationships with fewer direct ties and focus on specific partners (Figure 2, bottom right). Figure 2: Relationship management as a knowledge strategy Low High Tie-Strength Concentration 6. DISCUSSION AND CONCLUSION This study contributes theoretically to the knowledge management field as follows. First, it examined the knowledge performance of firms embedded in an inter-organizational network by considering various factors. In the context of inter-organizational network, knowledge transfer and inter-organizational learning is a recent topic that is expanding (Marchiori & Franco, 2020). Most previous studies of network structure and knowledge performance are empirical studies, because it is very difficult to measure the knowledge performance of a firm, especially the ego network, which is a combination of complex factors (Gulati, 1998). This study overcame the disadvantages of empirical analysis by establishing an agent-based model based on the organizational learning theory and by obtaining and analysing vast amounts of data through simulations using such a model. Second, the complex 14 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 mechanism concerning knowledge performance was exemplified using a dynamic model that includes network-, relationship-, and firm-level factors that affect knowledge performance. By using an agent-based model suitable for modeling emergent phenomena caused by the interactions among various factors, multiple factors were considered to identify the moderating effect. The findings of this study provide insightful implications for practitioners. First, the findings provide implications for relationship management. This study helps firms design their own knowledge strategies for their targeted knowledge profiles by expounding on the implications of the number and strength of direct ties that firms can create and maintain. Second, we propose a strategic framework for firms to manage their knowledge profiles by identifying the number of direct ties that can be managed directly, the concentration of tie strength, and their relationship with knowledge performance. A firm has structural features that it can control and network characteristics that it cannot manage. This study helps knowledge managers to establish knowledge strategies by suggesting structural network factors—tie-strength concentration and node degree—that firms can directly manage for knowledge management. Third, this study revealed that the relationship between structural factors and performance can vary depending on the situation, such as the network topology, a firm's capability, and the length of time (Ahuja, 2000; Capaldo, 2007; Duys-ters & Lokshin, 2011; Rowley et al., 2000). By examining the moderation effect of absorptive capacity and network topology on the knowledge performance of a firm, knowledge managers can understand that the effectiveness of the knowledge strategy may differ depending on the firm's own situation and the structure of the industry. To conclude, it can be said that a firm's knowledge performance can be a driving force for innovation. Firms produce knowledge internally, but they also absorb it from the outside. Firms are embedded in inter-organizational networks, and they absorb and utilize external knowledge. This study examined the relationship between the structural factors of a firm and knowledge performance by extending the organizational learning model into a network. We examined the relationship between two structural factors—tie-strength concentration and number of direct ties—and the average knowledge level and standard deviation of the knowledge profile. The results indicate that the more concentrated the tie strength, the lower is the average level of a firm's knowledge profile. The number of direct ties influences the standard deviation of the knowledge profile, resulting in a negative (positive) effect in the short (long) term. In the long term, the effect of increasing the KP standard deviation of the node degree is strengthened when the absorptive capacity is large. This study has the following limitations and future research directions. First, the cost of maintaining and managing a relationship was not considered. As the results of this study suggest, exchanging knowledge with multiple partners inevitably is costly. By conducting a cost-benefit analysis of lowering the concentration of relationships and its utility, it is expected that an effective knowledge development strategy can be established. Second, among the factors that can affect the performance of knowledge, the characteristics of the knowledge being diffused were not considered. There may be differences in the transfer of tacit and explicit knowledge. This study did not include the forms of advanced knowledge that can be delivered only through strong ties. In future research, more sophisticated results can be expected if the type of knowledge transferred is considered. Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 15 Chulsoon Park: Inter-Organizational Relationships Management as a Knowledge Strategy: A Simulation Approach EXTENDED SUMMARY/IZVLEČEK Podjetja od svojih poslovnih partnerjev pridobivajo različna znanja, ki služijo kot izhodišče za različne inovacije. Ali bo podjetje pridobljeno znanje učinkovito in uspešno uporabilo je odvisno od števila, moči in neposrednosti povezav med podjetjem in različnimi poslovnimi partnerji. Raziskava temelji na modelu agenta ter teoriji organizacijske krivulje učenja. Slednja dokazuje, da je učinkovitost uporabe znanja v organizaciji možno uravnavati preko strukturnih dejavnikov prej omenjenih povezav med podjetji. Močne medorganizacijske povezave namreč znižujejo učinkovitost uporabe znanja; to pomeni, da se raven znanja v podjetju zmanjša v primeru močnih povezav z določenim podjetjem ter hkrati šibkimi povezavami s preostalimi podjetji. 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Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 17 Chulsoon Park: Inter-Organizational Relationships Management as a Knowledge Strategy: A Simulation Approach Wuyts, S. & Dutta, S. (2014). Benefiting from alliance portfolio diversity: The role of past internal knowledge creation strategy. Journal of Management, 40(6), 1653-1674. Xie, X., Wang, L. & Zeng, S. (2018). Inter-organizational knowledge acquisition and firms' radical innovation: A moderated mediation analysis. Journal of Business Research, 90, 295-306. Xu, S. & Cavusgil, E. (2019). Knowledge breadth and depth development through successful R&D alliance portfolio configuration: An empirical investigation in the pharmaceutical industry. Journal of Business Research, 101, 402-410. Zhang, J., Baden-Fuller, C. & Mangematin, V. (2007). Technological knowledge base, R&D organization structure and alliance formation: Evidence from the biopharmaceutical industry. Research Policy, 36(4), 515-528. Zhao, Z. J. & Anand, J. (2009). A multilevel perspective on knowledge transfer: Evidence from the Chinese automotive industry. Strategic Management Journal, 30(9), 959-983. 18 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 Vol. 9, No. 2, 19-36 doi:10.17708/DRMJ.2020.v09n02a02 REGIONAL DEVELOPMENT IN THE ERA OF INDUSTRY 4.0 Avigdor Zonnenshain, Gilead Fortuna, Eithan Adres, and Ron S. Kenett The Samuel Neaman Institute for National Policy Research, Center for Industrial Excellence, Technion, Haifa, Israel - Abstract - Regional development is a complex challenge for policymakers in government, business, and industrial leadership. The Fourth Industrial Revolution, labeled Industry 4.0, has created integrated opportunities for a circular economy involving actors from different society strata. This paper presents an integrated approach that combines conventional strategic planning methods with tools adapted to the Fourth Industrial Revolution. The regional development methods discussed here are demonstrated with a real-life case study from a major regional development project initiated by policymakers. The integrated approach presented here lists opportunities and challenges in regional development applications with interest to both researchers and policy makers. The case study is from the North of Israel, which also is called the Galilee. The Galilee is considered a geographical and social peripheral region in Israel, and, as such, it creates significant complexities and challenges for regional development policymakers. As a peripheral region, the Galilee suffers from major weaknesses such as low income, low productivity, poor services, and negative migration, especially of young populations. The paper presents the theoretical foundation of an integrated approach which includes an assessment using the Industrial Maturity for Advanced Manufacturing (IMAM) tool developed at the Samuel Neaman Institute, Technion, Israel. The IMAM scale assesses the maturity and ability of industrial companies to adapt and implement innovative and advanced manufacturing technologies and processes. We suggest that an integrated approach combining a strategic plan and an IMAM assessment can be replicated in other industrial zones. Keywords: fourth industrial revolution, strategic program, industrial maturity, regional development, SWOT analysis, innovation and productivity 1. INTRODUCTION As value and production chains become more trans-territorial in the era of globalization and Industry 4.0, the regional development level of analysis gains more salience. Regional development is a complex challenge that needs to address multiple interrelated goals. Traditionally, the main economic measures driving regional development, at both the national and the regional levels, are the GDP and GDP per capita. In contemporary developed societies characterized by growing income inequality, these measures may not provide an accurate assessment of the situation in which most people find themselves and of societal well-being (Stiglitz, Sen, and Fitoussi, 2010). This observation also applies to regional disparities which lead to the resurgence of regional economics, processes of development, growth, and sustainability. Consequently, careful attention should be given to long-term factors such as education, health services, welfare, and research and development (R&D) investments at the regional level. Advanced technologies also may play a major role in bridging the interregional well-being gap by advancing proximity - physical and virtual (Capello and Nijkamp, 2009). An essential vehicle for regional development is proper policy measures, such as moving jobs to region with high unemployment; indirect measures, including better infrastructure, stimulating R&D and innovations, improving education, and providing an attractive environment (housing, recreation, sport, culture); direct measures such as financial compensation, soft loans, low land prices, favorable energy con- Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 19 Avigdor Zonnenshain, Gilead Fortuna, Eithan Adres, Ron S. Kenett: Regional Development in the Era of Industry 4.0 tracts, etc.; and strict direct measures such as relocation of governmental institutions (van Dijk, Folmer, and Oosterhaven, 2009). Recent directions in theories of regional economics shed light on two main approaches: more realism, and more dynamics (Capello, 2008). We stress the following trajectories: understanding endogenous factors that support regional competitiveness (industrial specialization, infrastructure, location, en-trepreneurship, realistic economic clusters, agglomeration economies, transportation costs, human resources, etc.); knowledge, which is embedded in human capital, as an endogenous driving force to development; and nonlinear trajectories of development (Krugman, 1991; Romer, 1986; Lucas, 1988; McCann and van Oort, 2009; Minerva and Ottaviano, 2009; Faggian and McCann, 2009). Due to measurability difficulties, general modelling logic entails static assumptions. The focus on the "representative" firm and on pecuniary economies, while ignoring dynamic nature of externalities such as human capital and technological spillovers, underpins the critique of models (McCann, 2005; Fingleton and McCann, 2007; McCann and van Oort, 2009). Moreover, careful attention should be given to institutions and their relationships with knowledge. The role of institutions, and more specifically efficient institutions, in economic development is paramount (North, 1990; Aghion and Howitt, 1998, Helpman, 2004). Considering the dynamic environment of the 21st century and the Fourth Industrial Revolution, the breaking down of barriers between national economies make the region a fundamental basis of economic and social life (Fischer and Nijkamp, 2009). Therefore, the variance among regions should be considered, and for better assessment of potential regional development, it should be recommended to critically use generaliz-able predictions based on representative models. Hence, geographical thinking should be intertwined with economic analysis, and pinpointing the need for dynamic measuring tools is unquestionable. This dynamism gives central importance to entrepreneur-ship among processes affecting regional growth. Entrepreneurship encourages innovative activity and always involves economic risk taking, which nonlin-early leads to development and growth (Acs, 1994; Audretsch, 2004; de Groot et al., 2004). To bridge gaps between theories and practice, we follow Cuadrado-Roura (2001), who identified seven attributes of succeeding regions in terms of development and growth: 1. The presence in a region of a group of medium-sized cities together with a large city. 2. The presence of medium- to high-educated labor, preferably with moderate wages. 3. Physical proximity to major markets and large urban centers together with access to new ideas. 4. Availability of business services such as consulting, advertising, financing, etc. 5. A facilitating local authority with well-developed strategies and leadership. 6. A positive social environment facilitating cooperation among institutions and organizations. 7. Many small and medium-sized industries easily enabling knowledge spillovers, as opposed to the dominance of a few large firms. This article introduces a comprehensive integrated methodology for regional development and implements it in a case study. The article is structured as follows: Section 2 presents a survey of different models, the third section introduces the integrated methodology for regional development, Section 4 discusses the case study of Northern Israel, and the last section concludes with a discussion. 2. THEORETICAL BACKGROUND 2.1 Competitive advantage and the clusters approach The importance of connections among branches of businesses and industries gained the interest of economists in the 1970s (Czamanski, 1974, 1976). These connections among manufactures are called "value chains" and the geographic concentration of manufactures which are creating and operating relationships among them are called "geographic clusters." Analysis of these networks among businesses and manufacturers is used to build and to calculate matrices which are the basis for conventional analysis methods such as "input-output," the gravity model (Haynes and Fotheringham, 1984), and the Diamond Model (Porter, 2000, 2003; Delegdo, Porter, and Stern, 2014). 20 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 Figure 1: The Input-Output Model Adapted from Leontief and Strout, 1963 The "input-output" model (Figure 1) developed by Leontief and Strout (1963), represents the flow of money in an economy, primarily through connections between industries, i.e., the extent to which different industries are buying from and/or selling to one another in a geographic region. An "input-output" model also accounts for such factors as government spending, housing spending, investments, imports, and exports, all of which help provide a full picture of what is happening in an economy. This model was used to assess the economic impact of Teva and Intel on the economy of Israel (Fortuna, Neev, and Freeman, 2014; Fortuna et al, 2018). The gravity model (Figure 2) demonstrates the general form of spatial interaction encompassing any movement over space that results from a human process. It includes journeys to work, migration, information and commodity flows, student enrolments and conference attendance, the utilization of public and private facilities, and even the transmission of knowledge. Gravity models are the most widely used types of interaction models. They consist of mathematical formulations used to analyze and forecast spatial interaction patterns. The gravity model as a concept is of fundamental importance to modern scientific geography because it makes explicit and operational the idea of relative as opposed to absolute location. Figure 2: Illustration of the Gravity Model The shorter the distance between two locations, and the greater the mass of either or both locations (like size of population or attractive economy), the greater the economic pull and economic attraction between the locations Adapted from Haynes and Fotheringham, 1984 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 21 Avigdor Zonnenshain, Gilead Fortuna, Eithan Adres, Ron S. Kenett: Regional Development in the Era of Industry 4.0 The strategic analysis based on Porter's (1990) Diamond Model (also known as the Theory of National Competitive Advantage of Industries) is a diamond-shaped framework (Figure 3). It explains why certain industries are competitive internationally, whereas others are not, and why some companies perform consistent innovations, compared to others. Porter argues that any company's ability to compete in the international arena is based mainly on an interrelated set of location advantages that certain industries in different countries possess, namely firm strategy, structure and rivalry, factor conditions, demand conditions, and related and supporting industries. Figure 3: The Porter Diamond Model for Cluster Development Porter's Diamond Adapted from Porter, 2018 The Diamond Model of Porter describes the main benefits of the regional clusters to the competitiveness of the businesses in the clusters and to the economic growth of the region: • Productivity can be improved through the availability of common resources such as an experienced workforce, shared knowledge, and information. • Innovation can be nurtured through sharing ideas and innovative technologies. • New businesses can be created through promoting the business environment and ecosystem. • Positive spillovers across complementary economic activities can provide an impetus for agglomeration: the growth rate of an industry within a region may be increasing in the "strength" (i.e., relative presence) of related industries. • Industries located in a strong cluster register higher employment and patenting growth. Regional industry growth also increases with the strength of related clusters in the region and with the strength of similar clusters in adjacent regions. • There is evidence of complementarity between employment and innovation performance in regional clusters: both the initial employment and the patenting strength of a cluster have separate positive effects on the employment and patenting growth of the constituent industries. • New regional industries emerge where there is a strong cluster. These findings are consistent with multiple types of externalities arising in clusters, including knowledge, skills, and input-output linkages. 3. AN INTEGRATED METHODOLOGY FOR REGIONAL DEVELOPMENT The model described in the case studies is an integrated framework for policy making in regional development. The framework is depicted in Figure 4. This framework includes four stages: assessment, development, deployment, and lessons learned. The assessment stage can combine quantitative methods such as the gravity model, the input-output model, IMAM (see Section 4.4), and qualitative models such as SWOT (see Section 4.2). The development stage includes several elements of regional development like innovation and en-trepreneurship, human resources development, collaboration and partnerships, and industrial parks. The deployment stage consists of several initiatives such as developing infrastructure change drivers and growth driver engines, deploying Industry 4.0, advanced manufacturing and engineering technologies, and supporting economic clusters. These initiatives are presented in the context of case studies. 22 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 Figure 4: Integrated Framework for Regional Development 3.1 The Role of Entrepreneurship in Regional Development Entrepreneurship is a dynamic, ever-changing entity in constant interaction with its ecosystem. It begins with an idea sparked by the identification of an opportunity within the abilities of the business team. The main challenge lies in identifying an opportunity and approaching the matching market by materializing this idea into business success in which the outcome may be different from the initial idea. The essence lies in finding that opportunity, exciting the market and raising demand, planning the scenario, and delivering a winning solution, while managing the risk presented by uncertainty. This entity exists in a challenging ecosystem consisting of highly competitive market conditions and requirements, such as the need for faster development and delivery of new and differentiated products, services, value, and ever-growing customer expectations. The ecosystem, and its regional cultural support and empathy for innovation, are key factors in the emergence of entrepreneurships. If it forms a sustainable local ecosystem, it can accept, absorb, and nourish exceptional and nonconservative concepts, approaches, and operations. This tendency, combined with the practice of appreciating calculated risk-taking and tolerance to failures, forms an encouraging incubator for various initiatives. These are only a handful of the issues to be considered. A mindset of dealing with a complex situation in a dynamic ecosystem is required, and often timing is of the essence. One should not disregard all relevant aspects of all factors involved, including human factors. Reasoning the main systemic components and carefully planning how to use a systemic concept may vastly increase the chances for the success of entrepreneurship initiatives in the region, which supports and advances entrepreneurship at the system level and the practical level. Entrepreneurship in system development was discussed in detail by Katz (2020). 3.2 The Role of Innovation and Creativity in Regional Development Managing the innovation and creativity process also demands a holistic approach at the regional level. The idea generation lifecycle includes the following major milestones: focus selection, ideas generation, harvesting, assessing ideas, treatment, and ideas implementation. As indicated in Porter's model (Porter, 2000, 2003) innovation is nurtured through the sharing of ideas and innovative technologies in the cluster ecosystem. It happens on the micro level through personal connections and communications, and on the macro level through collaborations of companies and institutes. As mentioned subsequently, the theory of the strength of weak ties (Granovetter, 1973; Bakshy et al, 2011) explains the barriers to and the enablers of innovating and sharing ideas in regional periphery, through connections on the micro level. 3.3 The Role of Human Resources in Regional Development The main source for successful regional development is the human resources who act and work in the region. There is a need for businesspeople, man- Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 23 Avigdor Zonnenshain, Gilead Fortuna, Eithan Adres, Ron S. Kenett: Regional Development in the Era of Industry 4.0 agers, engineers, and workers who have the knowledge and expertise which fit the jobs of the businesses in the region. This may be called the regional intelligence. In addition, the regional institutes for training, such as academia and vocational programs, can fill the gaps of knowledge in the region through life-long training. The managers and workers not only should have knowledge for the jobs in the region, but also should have leadership and creativity skills. Creative workers have been shown to have a direct and an indirect impact on regional innovation (Sleuwae-gen and Boiardi, 2014). Creative workers have an impact on innovation that is differentiated from the presence of regional intelligence, as measured by the availability of human capital. In addition, in peripheral regions it is important to create regional loyalty and identification with the regional vision and goals. Such regional human resources development programs may be created. 3.4 The Role of Collaboration, Partnership, and Industrial Parks in Regional Development Collaboration and partnership among peoples, companies, and institutes in the region are essential drivers for successful regional development. This happens through the spreading and sharing of ideas and innovations. Industrial parks are excellent place for collaboration and partnership of the companies located in the park, through the leadership of the industrial park management. 4. CASE STUDY FOR REGIONAL DEVELOPMENT: DATA AND APPLICATION OF THE INTEGRATED METHODOLOGY 4.1 Background data Following the proposed integrated methodology for regional development, a real case study of a strategic initiative to advance the geographical area of Northern Israel was conducted. The initiative involved a major strategic regional planning program followed by a focused assessment of organizational maturity in terms of Industry 4.0 implementation. The assessment was based on the IMAM scale described in Section 4.4 (Adres, Kenett and Zonnen-shain, 2020). The methodologies and approaches described here can be adapted to other regions and industrial zones, and therefore provide a generic approach to the development of regions with industrial parks. The first step was to collect and assess relevant data on Northern Israel, which is a heterogeneous area in terms of industrial activity, population structure, socioeconomic status, and educational aspects. Table 1 compares employment and salaries levels in this region to those of other regions in Israel. Table 1 shows that 16.5% of Israel's population lives in the North, and 14.7% of the employees in Israel work in the North. The unemployment rate in the North region is the highest in Israel, 8%, compared to the average of 6.2%. In addition, salaries Table 1: Employment and Salaries in the North Region Relative to Other Regions in Israel Region Percentage of populati on Employment rate Par ticipation rate Unemployment rate Average compensation per job in the industry Central District 2 4.2% 27 .5% 70 .2% 5.1% 115.6% Tel Aviv District 16.4% 18 .9% 67 .3% 5.2% 94.1% North District 16.5% 14. 7% 58. 0% 8.0% 79.7% Adapted from Central Bureau of Standards, 2013 24 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 in the North are relatively low, only 79.7% of the national average. The Northern region of Israel is home to a large portion of Israel's industry - about 34%. However, most of this industry is traditional, and its productivity and export rates are relatively low. Industry in the North is strong in metals (38%), electronics (35%), and food (35%) (Figure 5). There is migration from the North to the central regions of Israel, with high rates of migration among young people. The Israeli Ministry of Economy, in a joint program with the Samuel Neaman Institute, identified five strategic goals for improving the economic conditions in Northern Israel (Israeli Ministry of Economy, 2014). These goals are: 1. Growth of the economic system in the North; 2. Improvement of the socioeconomic status of the population in the North; 3. Advancing the joint economy of the Arab and Jewish populations in the North; 4. Exploiting the potential of the Arab population as a growth advantage; and 5. Reversing the negative migration from the North and attracting strong populations. The first two goals relate to the entire population in the North of Israel. Goals 3 and 4 relate to the Arab population and its potential for the growth of the North. The fifth goal presents a challenge to achieve better employment figures, a better business environment, higher industrial productivity, and improved quality of life indicators. This aims at reversing the migration trends and attracting a stronger and younger population to the North. 4.2 SWOT Analysis and Assessment of the North The SWOT (strengths, weaknesses, opportunities, threats) assessment and analysis of the Northern region was driven by the data collected and based on inputs solicited from 80 individuals with leadership positions in different areas, representing various positions within the government, industry, municipality, education, healthcare, academia, and NGOs. On-site visits were organized to several industrial plants and municipalities in order to receive first-hand impressions of and information about opportunities and barriers in the North. This provided both quantitative and qualitative data that were combined with past programs and reports that discussed the economic and social con- Figure 5: Percentage of Israel's Employees Who Work in the North and Haifa in Several Industrial Sectors Adapted from Central Bureau of Standards, Industrial Review, Board 29, 2013 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 25 Avigdor Zonnenshain, Gilead Fortuna, Eithan Adres, Ron S. Kenett: Regional Development in the Era of Industry 4.0 ditions in the North of Israel. All this information led to several discussions and roundtable brainstorming sessions that produced the SWOT of the North. The main findings of the SWOT were as follows. Strengths: • Large portions of the traditional industries are located in the North, and they have significant growth potential. • The population in the North is diversified, with good qualities and historical connections to the North. • The Arab population in the North is well educated. • The basic relationships among the different cultures are good. • The North of Israel is green, beautiful, and holy to Christians; hence it attracts both local and foreign tourism. Weaknesses: • The North lacks large vertically integrated companies with effective human resources growth. • The North lacks sufficient professional work force. • The North is a periphery far from the nation's decision makers. • Many municipalities lack substantial cooperation. • The city of Haifa is not perceived as and is not acting as the capital city of the North. • The Jewish and Arab economies in the North lack integration. • Investment in innovation in the North is relatively low. Opportunities: • There are many traditional and classical industries in the North with a potential for growth through innovation and productivity improvement. • There is a good basis for life science clusters with many active companies (more than 290), eight hospitals with research capabilities, and several good research institutes in tes area. • The Arab population in the North has a growing number of experts, workers, and students in the area of life sciences. • There is a good basis for clusters that focus on water, with many active companies (more than 300) and several research institutes. • The daily relationship between Arabs and Jews is good and offers an opportunity for joint development. • Recent substantial government investments in public transportation and roads in the North, as well as roads connecting the North to the Center, may promote businesses development and housing opportunities. • The new deep-water seaport in Haifa presents an opportunity to develop the economy of the North. • There is potential for an international airport in the North, which may positively impact the economy of the North. • The ultra-orthodox population in the North is growing as a community, with good qualities of work and study. • The North is green and attracts ethnic and nature tourism. Threats: • The absence of substantial economic growth in the North in the next few years will encourage the young population's tendency to leave the region. 4.3 A Strategic Analysis of Northern Israel A strategic analysis of the Northern region of Israel was conducted in "The North Project" (Zonnenshain, Fortuna and Dayan, 2015). The program studied the economic system of the North from various facets - industry, services, academia, municipalities, education, healthcare, transportation, large companies, small companies, different sectors of the population, etc. This also was based on the systemic approach of the Systems Engineering methodology (Zonnenshain and Shtauber, 2015) The program followed the integrated framework for regional development presented previously: assessment, development, and deployment. The assessment stage included the SWOT analysis and the IMAM scale (Adres, Kenett, and Zonnenshain, 2020). The development stage included identifying infrastructure change drivers and developing 26 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 growth drivers' engines. In the deployment stage, practical plans for deployment were prepared with the relevant partners and stakeholders. 4.3.1 Identifying infrastructure change drivers This study proposes four change drivers designed to impact the infrastructure in the North in nonlinear paths: 1. Moving specified technological industries and plants of the Israeli Defense Forces (IDF) from the central part of Israel to the North. This move can add at least 2,000 job opportunities for technical and logistic staff. In addition, it will create 10,000 jobs for suppliers and subcontractors. 2. Deepening and upgrading the port in Haifa so that it can accommodate large modern containers ships. The government is investing about NIS 6 billion through 2021 to build this port. It is planned that this port will employ about 7,000 people in various positions in various industries. 3. Building an international airport in Ramat David. This move will create thousands of employment opportunities, both for the construction and the operation of this airport. This airport can change the status of the North region for international business and tourist communities. 4. Leveraging the transportation revolution in the North to develop new business and housing areas along the recently constructed railroads and along the new major routes in the North. Based on the transportation-oriented development (TOD) methodology, we propose several suggestions for developing businesses and housings centers. 4.3.2 Developing growth drivers' engines The study also proposes several growth drivers that do not represent "business as usual." These growth drivers, aimed at creating employments opportunities, are: 1. Building and advancing an industrial scientific cluster in the area of life sciences. This cluster includes a full ecosystem of manufacturing plants, academic institutions, research institutes, start-ups, incubators, hospitals, and labs, all with advanced capabilities in life sciences. In the North region and in the Haifa area, there are impressive assets of manufacturing plants (representing more than 300 companies) and research institutes that use state-of-the-art technology in the life sciences. In addition, an impressive number of the Arab population have knowledge, experience, and expertise in this area. The North Project program proposes that there should be a national policy in Israel to advance the life sciences in the North of Israel. Furthermore, it is proposed that this policy should include support for international medical tourism in the North. 2. Building and advancing industrial-scientific water cluster. Similar to the life sciences cluster, it is proposed to build in the North an industrial-scientific ecosystem in the field of water. The North also has assets of manufacturing plants and research institutes. The Arab sector also can be integral in this cluster, with engineers, researchers, technicians, workers, and laboratory assistants. 3. Advancing innovation and productivity in the classical industry in the North. The North has a relatively large numbers of classical and traditional industries (34% of all classical industry sales, and about 115,000 employees), but the productivity, salaries, and export rates are relatively low. Innovation and excellence are recommended to improve productivity. The North Project program proposed specific tools for productivity and competitiveness improvements, such as investing in research and development, advancing automation, introducing advanced manufacturing, developing industrial parks that are oriented toward innovation and en-trepreneurship, etc. 4. Better integrating the Arab sector in the Northern economy to create a common economy. Upgrading the economy of the Arab sector is one of the most important and crucial challenges in the North Project program. It is proposed to improve the Arab sector by creating a common economy with the Jewish population. This program includes specific steps for creating the common economy, such as improving the socioeconomic situations of Arabs in the North, Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 27 Avigdor Zonnenshain, Gilead Fortuna, Eithan Adres, Ron S. Kenett: Regional Development in the Era of Industry 4.0 including initiating dialogue among the two populations to advance trust, create collaborative industrials parks, advance the employment of Arabs with academic degree in quality roles, encourage Arabs to join high tech organizations, and integrate Arabs into the life sciences and water cluster initiatives. This part of the program is prepared with representatives of the Authority for Economic Development of Minorities in the Prime Minister Office. 5. Advancing tourism in the North. Annual tourism revenue in the North is about NIS 10 billion. The tourism industry offers various employment and businesses opportunities to different populations. The North Project program explored several alternatives for developing tourism in the North and chose ethnic and cultural tourism, which demonstrates the highest possible revenue and future opportunities for sound investment. Furthermore, as mentioned previously, it is proposed to advance medical tourism as part of the life sciences cluster. As mentioned previously, it relates to building an international airport in the North to further advance tourism development. 6. Integrating the ultra-orthodox population in the North economy. Currently, 130,000 ultra-Orthodox Jews live in the North. This population may double in 15 years through natural growth and immigration. The North Project program proposed several tools and steps for developing a productive and quality ultra-Orthodox population in the North. These steps include, for example, academic and technical education, and integrating this community into the life sciences and water clusters. 7. Advancing innovation in the North. As mentioned previously, introducing innovation through industry and other enterprises in the North is a key factor for the success of the economy system. The Innovation Center in the Technion proposed, as part of this project, a holistic program to introduce innovation in the North. The program will include specific steps, such as education of leadership for innovation, teaching processes for innovation, creating collaboration between academia and industry, initiating pilot projects for innovation in the industries through advanced manufacturing, etc. 8. Advancing small and medium enterprises (SME) in the North. Small and medium enterprises are an important part of the North's economy. Through meetings with SME owners, and through national reviews, it was found that there are a significant number of barriers to the development and the success of SME in the periphery of the North. The theory of the strength of weak ties (Granovetter, 1973; Bakshy, Rossen, Marlow and Adamic) explains the barriers to innovating and sharing ideas in the periphery. It is very difficult for SMEs to survive in the economic and business environment of the Northern periphery. Therefore, special government and municipal help and support for the SMEs in the North are proposed, such as offering special loans, providing business-consulting services, lowering the burden of unnecessary regulations, and providing accessibility for purchase by government and public institutions. 4.3.3 Deployments plans The aforementioned joint strategic program was developed during 2014-2015 and concluded with several practical deployment's plans (Israeli Ministry of Economy and Samuel Neaman Institute, 2015). These plans were the basis for several major decisions and actions of the Government of Israel for upgrading the North of Israel. Some of the actions were already initiated, such as building a new port in Haifa with deep water, improving the transportation system in the North of Israel, upgrading the economy of the Arab sector, and advancing innovation in the industries through advanced manufacturing (see Section 5). 4.4 Industry 4.0 - Maturity Assessment Model Development During the last decade, industry in advanced economies has experienced significant changes in its engineering and manufacturing practices, processes, and technologies. These changes have the potential to create a resurgence in the engineering and manufacturing activities. This phenomenon is often referred to as the Fourth Industrial Revolution or Industry 4.0. It is based on advanced man- 28 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 ufacturing and engineering technologies, such as massive digitization, big data analytics, advanced robotics and adaptive automation, additive and precision manufacturing (e.g., 3D printing), modeling and simulation, artificial intelligence, and the nanoengineering of materials (Pai, 2014). This revolution presents challenges and opportunities for the systems, manufacturing, and process engineering disciplines. Several authors have discussed approaches to assess organizational readiness to advanced manufacturing challenges (McKinsey & Company, 2016; President's Council of Advisors on Science and Technology, 2014; PwC, 2016; Shuh et al., 2017). Under Industry 4.0, systems have access to large types and numbers of external devices, and to enormous quantities of data, which must be analyzed through advanced data analytics (Kenett and Shmueli, 2016; Kenett, Zonnenshain, and Fortuna, 2018). To help companies make progress on the roadmap toward Industry 4.0, we developed a questionnaire-based tool that assesses the current maturity level of a specific or a group of industrial companies and highlights a set of focused areas for the companies to pursue in an effort to deploy Industry 4.0 methods. We call this the model of Industrial Maturity for Advanced Manufacturing (IMAM). The next section is an introduction to IMAM. More details were given by Adres, Kenett, and Zonnenshain (2020) and Zonnenshain et al. (2018). The IMAM scale helps companies to assess their strengths and weaknesses and to prepare an improvement plan. It also provides companies with a tool for evaluating their actual improvements and achievements and serves as an effective benchmarking tool. The IMAM framework consists of an assessment tool based on the Software Engineering Institute's Capability Maturity Model Integration (CMMI) approach. It is specifically designed for assessing the maturity level of a company in the area of advanced manufacturing and engineering. The CMMI maturity level assessment in systems and software development was presented by Kenett and Baker (2010). The IMAM scale was established and validated using an accepted methodology (De Winter, Dodou, and Wiernga, 2009; De Vellis, 2012; Adres, Vashdi, and Zalmanovitch, 2016). The content of the IMAM was validated by an international experts' survey. The IMAM is a multidimensional latent construct constructed at the basic level with 14 identified application areas (subdimensions) which are relevant for advanced manufacturing and engineering. For each subdimension, we developed a self-report questionnaire, based on statements (items) measured on a five-point Likert-type scale. The 11th subdimension concerns information and knowledge management and builds on the information quality framework presented in Kenett and Shmueli (2016) and Reis and Kenett (2018). We added a concluding item stating, "It may be said that in general that the advanced manufacturing status of our company is in level..." (1-5 on a Likert scale). The dimensions are: 1. Strategy and long-term planning for advanced manufacturing 2. Human resources for advanced manufacturing 3. Communication with customers and the market 4. Processes in manufacturing 5. Processes in engineering 6. Business processes 7. Processes in maintenance 8. Logistics processes 9. Processes in the supply chain 10. Processes in product life cycle 11. Information and knowledge management 12. Processes in cyber assurance 13. Investment in infrastructure and equipment 14. Actual improvement outcomes and results In each area, several possible actions and activities can be considered by companies aiming at the advanced maturity level. Statistical analysis of these 14 subdimensions showed that they converge into four higher-level dimensions: (1) value chain; (2) infrastructure; (3) monitoring and control processes; and (4) engineering processes. These four dimensions statistically converge to the IMAM scale, which is an individual-level characteristic that can be understood as a single industrial organizational construct, reflecting the competence and maturity for advanced manufacturing implementation. More details on the IMAM model are given in Chapter 22 of Kenett, Swarz, and Zonnenshain (2020). Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 29 Avigdor Zonnenshain, Gilead Fortuna, Eithan Adres, Ron S. Kenett: Regional Development in the Era of Industry 4.0 4.4.1 Analysis of IMAM assessments from companies in Northern Israel Self-assessment and IMAM scores of 15 industrial companies in Northern Israel are presented in Figure 6. We approached the industrial companies in the North of Israel based on our efforts and experience from the North Project. Figure 7 shows the subdimensions of the Infrastructure dimension. Figure 8 shows the value chain subdimensions. Figure 9 shows the subdimensions of monitoring and control processes. Figure 10 shows the engineering scores. Figure 6: General Self-Assessment and IMAM Score í-geneííl Adapted from Zonnenshain, Adres, Fortuna, and Kenett, 2018 Figure 7 : Infrastructure Subdimension Scores Adapted from Zonnenshain, Adres, Fortuna, and Kenett, 2018 30 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 Figure 8 Value Chain Subdimension Scores I Product life cycle Adapted from Zonnenshain, Adres, Fortuna, and Kenett, 2018 Figure 9: Monitoring and Control Subdimension Scores i Maintenance ■Cvber «Improvement Adapted from Zonnenshain, Adres, Fortuna, and Kenett, 2018 Figure 10: Engineering Scores 1 2 3 A 5 6 7 8 9 10 11 12 13 14 15 Adapted from Zonnenshain, Adres, Fortuna, and Kenett, 2018 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 31 Avigdor Zonnenshain, Gilead Fortuna, Eithan Adres, Ron S. Kenett: Regional Development in the Era of Industry 4.0 Figure 11: Scores of the Four Dimensions of the Self-Assessment Adapted from Zonnenshain, Adres, Fortuna, and Kenett, 2018 Figure 11 compares the four dimensions for the 15 companies. The means, medians, and standard deviation for the general self-assessment; IMAM score; and the four dimensions are presented in Table 2. Self-assessment may be affected by overcriticism on the one hand, and by social desirability on the other hand. However, all means, and medians were less than the mid-point, 3, except for engineering. This indicates a need for general improvement in the industry; hence, this finding calls for planning a national policy. To analyze the data, we used a methodology and tools developed to highlight areas for improvement and areas of excellence (Kenett and Salini, 2011). A basic element in this analysis is the computation of the proportion of 1 and 2 ratings, labelled Bot1+2, and the proportion of 5 ratings, labelled Top5. The analysis shown subsequently builds on two standard statistical methods, control charts for proportions (p-charts) and analysis of variance (ANOVA) with Student's t-tests for paired comparisons, controlled for multiple comparisons. More details of this analysis were given by Kenett and Zacks (2014). Table 2: Descriptive Statistics of IMAM Responses General self-assessment IMAM score Value Chain Infrastructure Monitoring and control Engineering N Valid 14 15 15 15 15 14 Mean 2.29 2.8998 2.8174 2.6728 2.9311 3.1488 Median 2.00 2.7328 2.7500 2.7125 2.8667 3.0833 Std. Deviation 0.994 0.74718 1.04668 0.96802 0.82522 1.10852 Range 3 2.72 3.27 3.56 2.93 3.50 Minimum 1 1.89 1.41 1.41 1.33 1.50 Maximum 4 4.61 4.68 4.97 4.27 5.00 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 32 17311511 Based on this analysis of 15 respondents, representing industry in Northern Israel, overall strengths and weaknesses were identified for the companies which participated in the survey (Table 3). The IMAM maturity level assessment helps to achieve two major goals in efforts to implement advanced manufacturing. Goal 1: Assessing the organizational maturity level of a specific company and positioning its level on a 1-5 maturity ladder. IMAM also helps companies design an advanced manufacturing program based on its strengths and weaknesses, and it helps the company assess progress along the maturity ladder. Goal 2: Identification of regional strengths and weaknesses in the dimensions of advanced manufacturing. This can be done at the regional level, but also at the national level and for different industrial sectors. Based on the findings of this survey, there is awareness in the industrial companies in North of the importance of advanced manufacturing for the success of these companies. However, previously this awareness did not drive most of the companies in the region to develop a strategy and long-term planning for advanced manufacturing. We claim that this conservative attitude may risk the survivability of traditional companies in the North of Israel. It is proposed that the integrated framework for regional development can support and promote the Industry 4.0 implementation in the industries in the region. 5. DISCUSSION AND CONCLUSION This paper introduced an integrated framework for regional development. This framework includes four main components: assessment, development, deployment, and lessons learned. As a case study for this framework, we presented a joint strategic program for the growth of the Northern region of Israel which was conducted for the Ministry of Economics during 2014-2015. This program proposes four change drivers, designed to change the infrastructure in the North in nonlinear paths. This study also proposes several growth drivers that do not present "business as usual" and that are aimed at creating employment opportunities. It led to several decisions and actions of the Government of Israel; some of these actions already have been initiated. The additional component of the integrated methodology for regional development addresses the Industry 4.0 implementation efforts which were conducted in industrial companies in Northern Israel. It is based on the Industrial Maturity for Advanced Manufacturing scale, which assesses the maturity and ability of industrial companies to adapt and implement innovative and advanced manufacturing technologies and processes. The findings from 15 companies from the North of Israel are presented. These findings were used to validate the IMAM scale, and to reveal the strengths and weaknesses of the industries in the north of Israel.Both case studies demonstrated the framework for regional development in Northern Israel. The combination of a strategic program, which gave a wide and long-term perspective, with the Industry 4.0 implementation based on IMAM assessment, which is focused on a mid-term perspective, represent the proposed integrative methodology approach. It helped to pool together stakeholders from government, business, industry and academia, including various associations and NGOs. The IMAM assessment gave individual managers of industrial organizations specific feedback that can impact their annual plan. The combination of a strategic program and an Industry 4.0 implementation based on IMAM assessment can be replicated in other regions. It can Table 3:Strengths and Weaknesses of Industrial Group with Respect to Industry 4.0 Implementation Strengths Weaknesses Communication with the customers and the market Strategy and long-term planning for advanced manufacturing Engineering processes Human resources for advanced manufacturing Processes in the supply chain Processes in maintenance Information and knowledge management Processes in product life cycle Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 33 Avigdor Zonnenshain, Gilead Fortuna, Eithan Adres, Ron S. Kenett: Regional Development in the Era of Industry 4.0 be combined with deployment planning initiatives and middle-of-the-road pulse-taking evaluations. This paper demonstrated successful implementation of four theoretical approaches and practical tools: the input-output model, through the direct economic activities of the companies in the region, through the derived economic input in the chain of suppliers and the induced economic impact in the region; the gravity model, by journey-to-work, migration of families and peoples, and flows of information and commodity; Porter's Diamond Model for developing clusters in the areas of life sciences and water engineering by productivity improvement through the availability of common resources, nurturing innovation through sharing advanced ideas and technologies, and creating and supporting new businesses through promoting doing business environment and friendly ecosystem; and applying SWOT by advancing the strengths of the region like the traditional industries and the well-educated Arab population. These four approaches are inte- grated through four practical stages of an integrated framework for regional development: assessment, development, deployment. and lessons learned. These approaches were successfully applied for the case study of the Northern region of Israel. The implementation process includes elements such as innovation, creativity, and entrepreneurship, such as the Industry 4.0 initiative; infrastructure change drivers and growth engines; and promotion of the human resources. All are important for the development and execution of improvement program in the Northern region of Israel. The program for developing the North of Israel presents initiatives which demonstrate the bridging of gaps between theories and practice for the benefits of this region, such as the presence of a large city (Haifa) with a group of medium sized cities; the presence of medium- to high-educated labor; positive social and economic environment facilitating collaboration among peoples, institutions, and organizations; etc. EXTENDED SUMMARY/IZVLEČEK Regionalni razvoj predstavlja kompleksen izziv za oblikovalce politik na vodilnih vladnih, poslovnih in industrijskih položajih. Četrta industrijska revolucija oziroma Industrija 4.0 je ustvarila integrirane priložnosti za razvoj krožnega gospodarstva, ki vključuje akterje iz različnih družbenih slojev. Raziskava predstavlja celostni pristop, ki združuje običajne metode strateškega načrtovanja z orodji četrte industrijske revolucije. Obravnavane metode regionalnega razvoja so prikazane s pomočjo študije resničnega primera razvojnega projekta, katerega namen je spodbuditi regionalni razvoj. V raziskavi predstavljeni integriran pristop navaja priložnosti in izzive regionalnega razvoja, zanimive tako za raziskovalce kot oblikovalce politik. Študija primera izhaja iz področja na severu Izraela, imenovanega Galileja. Galileja velja za geografsko in socialno obrobno regijo v Izraelu in kot taka predstavlja izziv za oblikovalce politik regionalnega razvoja. Kot obrobna regija Galileje trpi zaradi mnogih pomanjkljivosti kot so nizki dohodki, nizka produktivnost, slabe storitve in negativne migracije zlasti mladega prebivalstva. V raziskavi so predstavljena teoretična izhodišča celostnega pristopa ter ocena slednjega, pridobljena na podlagi orodja Industrial Mat mature for Advanced Manufacturing (IMAM), razvitega na Institutu Samuel Neaman, Technion, Izrael. 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Zonnenshain, A., Fortuna, G., and Dayan, T. (2015). Upgrading the economic system in the North of the country. Zonnenshain A., Adres E., Fortuna G. and Kenett R. (2018). Assessing the Maturity Level of the Industry for Advanced Manufacturing: The IMAM Model Haifa Israel: Samuel Neaman Institute. 36 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 Vol. 9, No. 2, 37-50 doi:10.17708/DRMJ.2020.v09n02a03 A MULTILAYER PERCEPTRON NETWORK-BASED ANALYSIS TO CONFIGURE SMES STRATEGIC ENTREPRENEURSHIP FOR SUSTAINABLE GROWTH Ardita Todri Faculty of Economics, University of Elbasan ardita.todri@gmail.com. Petraq Papajorgji Faculty of Engineering, Canadian Institute of Technology petraq@gmail.com Francesco Scalera Faculty of Economics, University of Bari roby_sca@virgilio.it. - Abstract - This study analyzed the close interaction among organizational networking and financial mechanisms of growth and sustainable growth of SMEs operating in Albania. Data on 120 SMEs for 2017-2018 were analyzed using multivariate regressions and multilayer perceptron artificial neural networks. Initially, the data were analyzed using multivariate regression analyses to find the correlation between firms' growth measured by three different indicators: return on equity, return on assets and business size. In this approach, growth takes into consideration a firm's liquidity, its operational efficiency, and leverage indicators in addition to organizational characteristics. The results obtained during the initial phase were fed to the multilayer perceptron artificial neural networks model to evaluate SMEs growth and further their sustainable growth process by using the age of the firm, classified into start-up, grown, and matured stages. The model results showed that SMEs in the start-up stage assume a risk-taker approach toward sustainable growth. In the grown stage, they implement a market-timing strategy in selecting investments toward a sustainable growth perspective. Those in matured stage replicate the liberal managerial style of the SMEs in start-up stage, but employ a less aggressive strategy. Keywords: SMEs, strategic entrepreneurship, sustainable growth 1. INTRODUCTION This paper identified and evaluated the interaction among factors impacting small and medium-sized enterprise (SME) development toward a sustainable growth process. The existing literature shows that the growth process of SMEs is determined by the owner/manager personal and managerial approach (Baldwin, 1994; Frank & Goyal, 2009; Sarwoko & Frisdiantara, 2016; Neneh, 2020). In addition, the literature considers various approaches to SME development based on growth models, social psychology of business owners/managers, and financial performance issues, but no studies have considered the transition process from growth to a sustainable growth. The presented approach considers SMEs as a heterogeneous group, taking into account their size, age, equity origins, organizational philosophy, and Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 37 Ardita Todri, Petraq Papajorgji, Francesco Scalera: A Multilayer Perceptron Network-Based Analysis to Configure SMES Strategic Entrepreneurship for Sustainable Growth business strategies. This study does not consider them as closed and separate systems, and does not neglect the significance of networking and organizational mechanisms in their promotion and sustainable growth. There is a lack of studies that consider the strong relationships existing between organizational characteristics and financial aspects of SMEs during the transition process from growth to a sustainable growth. SMEs are a very relevant part of the economic prosperity of a country and are considered as the backbone of the economy. Thus, it is very important from a theoretical and practical point of view to undertaking a deeper analysis that will help understand the factors influencing their wellbeing. Such an analysis should determine the factors impacting SME growth and indicate how to create a smooth transition versus a sustainable growth process. Thus, it is of high priority to select and use efficient tools that will determine SMEs' situation, and, based on these findings, to define the correct path for sustainable growth. The presented approach is based on SMEs' financial aspects in close interaction with their organizational philosophy. This research study initially addressed the SME growth market measured through return on assets (ROA), return on equity (ROE), and business size (BoS) (Lee & Tsang, 2001; Naranjo, 2004; García-Teruel & Martínez-Solano, 2010; Czarnitzki & Hottenrott, 2011). Furthermore, in this approach the growth takes into consideration firm's liquidity, its operational efficiency, and leverage indicators in addition to organizational characteristics (Table 1) by using multivariate regression analyses. Next, based on the results of multivariate regression analyses, a multilayer perceptron artificial neural network (MLP-ANN) model is designed and used to specify and evaluate the factors influencing SMEs sustainable growth, measured by firms' age, classified as start-up, grown, and matured. To test our approach, we considered the Albanian market. The 2018 Statistical Register of SMEs (SRS) data show that SMEs account for 99% of total businesses, 81% of total employment, and approximately 67% of business turnover. The potential contributions of this paper to the existing literature are as follows. First, this study ad- dresses SME growth and sustainable growth issues considering the close interaction among organizational networking and financial mechanisms. This is a novelty of this study. Second, a multilayer percep-tron artificial neural network analysis maps sets of input data onto a set of appropriate SMEs output classified in three different growth stages. In the current literature, these models are used to measure only SMEs' performance and creditworthiness. Thus, this study provides a novel utility of these models. Third, this paper presents a valuable model that can be used by SMEs to organize internal information to define their sustainable growth strategies. The rest of the paper is presented as follows. A literature review reviews existing studies on the subject. Section Methods shows the research context, data used for the analyses, and the scientific approach; section Discussion presents the results obtained by this study; and the last section, Conclusions, presents the findings of this study. 2. LITERATURE REVIEW Growth of small and medium enterprises is difficult to achieve because of the complexity of the phenomenon according to extensive studies (Czarnitzki & Hottenrott, 2011; Michna, 2007, Abdelaziz, Alaya & Dey, 2018). Sarwoko & Frisdiantara (2016) defined SMEs' growth philosophy as a set of owner's/manager's personal characteristics, or as their personal approach. The definition also includes the way in which strategic decisions are made; this could be referred to as a managerial approach. The growth measurement process uses indicators such as sales, profit, assets, equity, and their derivatives (Lee & Tsang, 2001; Naranjo, 2004; García-Teruel & Martínez-Solano, 2010; Czarnitzki & Hottenrott, 2011). In this context, many researches have shown that SMEs' liquidity management is their major challenge. This issue is complex because liquidity is managed day by day in order to meet business short-term obligations due to agency1 and asymmetry2 issues 1 Agency problems in SMEs occur when managers are delegated by owners to act according their interests. This relation inherently creates conflicts of interest in respect of each individual benefit clue. 38 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 (Gopinath, 1995; Chittenden, Hall & Hutchinson, 1996; Chow & Fung, 2000; Berger & Udell, 2005; García-Teruel & Martínez-Solano, 2008; García-Teruel & Martínez-Solano, 2010). Good management of business short-term obligations may positively impact SMEs business growth. Nowadays, SMEs try to balance the liquidity management process with operational efficiency and leverage. Studies show a positive relationship between cash management, inventory (INV) turnover, trade credit practices, and profitability (Baños-Caballero, García-Teruel & Martínez-Solano, 2010; García-Teruel & Martínez-Solano, 2010). In addition, SMEs' efficiency and sustainability mainly depends on good working capital management (WCM) practices (Kubícková & Soucek, 2013; Hyz, Stavroulakis & Kalandonis, 2017; Abimbola & Kolawole, 2017). Studies have proven that there is a non-linear relationship between the variables examined by demonstrating that there is a non-monotonic relationship between working capital level and firm profitability (Czarnitzki & Hottenrott, 2011). The same studies make clear that the liquidity management strategy is a crucial element in the survival and further growth of SME businesses. Other studies (e.g., Michna, 2007; Marom, Lussier & Sonfield, 2019; Barwinski, Qiu, Aslam & Clauss, 2020) show that SME survival in a risky and competitive environment requires innovation, and that innovation requires new knowledge. Some studies (Chittenden, Hall & Hutchinson, 1996; Jordan, Lowe & Taylor, 1998; Hall, Hutchinson & Michaelas, 2000; Booth, Aivazian, Demirguc-Kunt & Maksimovic, 2001) used as performance measures the determinants of capital structure. Those studies explain that debt management practices serve as integral parts of financial strategies applied to SMEs. Financial strategies logically affect the ability of the SMEs to grow. Furthermore, small businesses carry different types of debt depending on the services or products delivered (Mazzarol, Reboud & Clark, 2015). Normally, to correctly manage business debts, it is crucial to appropriately estimate current debts, minimum 2 An asymmetric information situation occurs when one of the parties involved in economic transaction possesses more information than the other (i.e., a buyer vs. a seller). Under these circumstances it can be deduced that almost all economic transactions involve information asymmetries. payment schedules, and respective interest rates. The success or failure of a firm depends even on the ability to secure adequate funding, among other issues (Derelioglu & Gürgen, 2011). Smith (2013) showed that the insolvency of many SMEs depends not only on the owner's underperformance, but also on the underperformance of other sectors of the business. Therefore, owners'/managers' poor debt management or lack of financial management is the main cause of financial problems in SMEs (Jindrichovska, 2013). Reasonably, a serious issue is the maintenance of an optimal capital structure ensuring guaranteed and sustainable growth. Many studies (e.g., Frank & Goyal, 2009; Salder, Gilman, Raby & Gkikas, 2020) have shown that some firm-specific factors that affect SMEs' capital structure and growth are firm size, profitability, tangibility, debt amount, growth, and volatility. Other factors that should be considered are industrial/environmental characteristics. The organizational characteristics and the managerial decision-making process also are known to have a decisive influence on SMEs growth. For example, managerial skill, the competence of leadership style, employee commitment, administrators' and owners' gender, and equity origin could affect SME growth (see Shrader, Mulford & Blackburn, 1989; Baldwin, 1994; Frank & Goyal, 2009; Neneh, 2020). Kazanjian (1988) showed that sustainable SMEs growth occurs in different stages measured by life-cycle periods or the age of the firm (FA). The stages are (1) the business conception and development, (2) commercialization related to business start-up, (3) growth, and (4) stability. In the growth stage, sales and market share are increased, and that requires that SMEs must consider organizational arrangements such as increasing human resources or equipment to deal with growth. The stability stage is characterized by profitability, internal control, and consolidation of a base for future growth. In addition, an important aspect to consider is the integration of owners'/managers' behavioral, social, and psychological contexts in the firm growth philosophy. Studies such as Amit, MacCrimmon, and Oesch (1996) have found that both economic and psychological attributes are associated with businesses in the start-up stage to generate growth. How- Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 39 Ardita Todri, Petraq Papajorgji, Francesco Scalera: A Multilayer Perceptron Network-Based Analysis to Configure SMES Strategic Entrepreneurship for Sustainable Growth ever, according to Blatt (1993), newly registered businesses do not seek immediate growth for their businesses. On the other hand, Orser, Hogarth-Scott, and Wright (1998a & 1998b) showed that the decisions to reach growth derive from a variety of motivations, including the owner's perception of growth and their values. The experience demonstrates that SMEs' growth it is impacted by business environment conditions. The business environment is a factor that also influences SMEs growth. Due to environmental conditions such as competitiveness and changing market dynamics, SMEs' growth is uncertain (Baum & Locke, 2001; Street & Cameron, 2007). SMEs growth is a function not only of the financial performance of the businesses (Cragg & King, 1989; Belcourt, Burke & Lee-Gosselin, 1991; Covin & Slevin, 1991; Epstein, 1993, Sarwoko, Surachman & Armanu, 2013). Another important element of SMEs' growth performance is the interrelationship between planning, market timing-oriented strategies, characteristics of owners/managers, and growth philosophy. However, past research, focused on the co-integration of SMEs' organizational characteristics and financial performance, toward sustainable growth in corresponding stages has not specifically addressed this issue in a holistic manner. The novelty of the present research is its insight into SME growth in a multidisciplinary context. This study explores various elements of business growth, such as the gender psychology of business owners/managers, entrepreneurship strategy, and relevant financial aspects to ensure business continuity and sustainable growth. 3. METHODOLOGY 3.1 Research context SMEs were classified as micro, small, and medium enterprises, taking into account the number of employees and annual turnover. Micro businesses have fewer than nine employees and annual turnover of less than €81,600; small businesses have 10-49 employees and annual turnover of less than €408,160; and medium-sized businesses have 50-249 employees and annual turnover of less than €2,040,800. The 2018 statistics show that local busi- nesses constitute 96%, joint ventures (foreign and local businesses) account for approximately 1%, and foreign businesses represent approximately 3% of SMEs operating in the country. During 2018, women owned 25.7% of total active enterprises. 3.2 Data This research study used a sample containing 120 SMEs data pertaining to 2017-2018 from the National Registration Centre (NRC) and the Credit Registry of the Bank of Albania (CRBA) databases. The selected SMEs reported and documented in detail their financial data in both databases. Most of the SMEs' organizational characteristics were retrieved from the National Registration Centre, and only the borrowers' status records were retrieved from the Credit Registry of the Bank of Albania. In addition, all financial indicators used in this analysis refer to National Registration Centre data (Table 1). The organizational characteristics analyzed (Table 1) emphasize the development philosophy of the SMEs operating in the Albanian business environment [i.e., Administrator Gender (AG), Business Ownership, Equity Origin (EO), Ownership Gender (OG), and Borrowers' Status (BS)]. The financial indicators analyzed pertaining to liquidity (current assets, inventory turnover ratio (ITR), inventory, and short-term assets/debts], operational efficiency [gross profit margin (GPM), net profit margin (NPM), asset turnover (AT), and return on equity), and leverage [long-term debt (LTD), long-term debt/equity ratio (LTDER), total leverage ratio, and interest coverage ratio] evaluate the business capabilities linked to organizational characteristics which ensure SMEs' growth and further their sustainable growth. 3.3 Variables and analytic techniques To examine the SMEs' growth and their sustainable growth, this study considered their organizational characteristics and financial aspects at 95% confidence level. This study used various growth indicators, such as ROE, ROA, BoS and FA. Several models, such as mul-tivariate regression models and the artificial neural network based on a multilayer perceptron classification also was used. Except for ROE, all the variables 40 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 (ROA, BoS, and FA) used to examine the SMEs' growth and sustainable growth pertained to growth area. ROE was used as a growth measure because it is the operational efficiency indicator most in line with growth referring to SMEs size (Naranjo, 2004). In the first phase of this research study, a test was designed to determine a direct relationship between organizational characteristics and financial indicators of growth (measured in terms of ROE, ROA, and BoS). Three different multivariate regressions which use ROE, ROA, and BoS were developed: 1. ROEit = a + p x organizational characteristicsit + Y x financial indicatorsit + £ it (1) 2. ROAit = a + p x organizational characteristicsit + Y x financial indicatorsit + £it (2) 3. BoSit = a + p x organizational characteristicsit + Y x financial indicatorsit + £it (3) In these regressions ROE, ROA, and BoS were considered as the dependent variables. Other variables, organizational and financial (Table 1), were considered as the explanatory variables. In the second phase of this study, an artificial neural network based on a multilayer perceptron classification was designed and implemented analyzing the results obtained during the first phase. The MLP neural network used the age of the firm as the SME sustainable growth indicator. SME sustainable growth was classified in three different stages: startup, pertaining to businesses with 0-5 years of activity; grown businesses, with 6-15 years of activity; and matured, with more than 15 years of activity. The age variable was used with a dual purpose; it captured the effects of SME growth, and it measured the expansion into different business development stages. The MLP model was used to map sets of input data onto a set of appropriate output: FA(start-up;grown;matured)it = f(W{organizational characteristics; financial indicators}it) (4) Such an approach enables modeling the influence of beliefs, efforts and the implemented business strategies (their correlated effects not directly measured in the first phase of this research) on SMEs sustainable growth. Their impact on SMEs sustainable growth was analyzed using the multilayer perceptron network results. 4. RESULTS 4.1 Multivariate regression analyses The first step of this analysis evaluated whether a direct relationship exists between SME growth measures and the independent variables examined (organizational characteristics and financial aspects) at 95% confidence level. The first model employed was a multivariate linear regression which used ROE as a SME growth measure. The same model was used for the second evaluation, which used ROA as the growth measure. The third model employed was a multivariate log-linear model in which the growth measure was a function of BoS (ln total assets). The first model results (Table 2) confirm that the independent variables which influence ROE at the 95% confidence level are GPM, NPM, AT (operational efficiency area); LTDER (leverage area), and short-term debt (STD) (liquidity area). These variables can predict ROE volatility with approximately 99.6%. Note that the presence of multicollinearity issues are indicated by significant direct correlation between variables. Statistically this was confirmed from the variance inflation (VIF) value, which in every case was higher than 1. These results are the main reason why the organizational characteristics variables were excluded from examination in this multivariate linear regression analysis. In addition, results showed that the residuals of the model were negatively correlated. Thus, the model indicated heteroskedasticity issues, meaning that residuals were not normally distributed (n = -2.12 x 10-15; 5 = 0.892). Therefore, a different examination was performed to better explain ROE in terms of a SME growth measure. The second multivariate linear regression model indicated that the variables that were statistically significant at 95% for ROA prediction are NPM (operational efficiency area), total leverage ratio (LEV) (leverage area), collateral value (CV) (growth area), OG mixed, and BS performing (organizational characteristics) (Table 2). These variables can predict only 57.1% of ROA volatility. The VIF value was higher than 1, which confirms the presence of multicollinearity issues between the examined variables. On the other hand, the residuals confirm a positive correlation (DW =1.781). Their distribution Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 41 Ardita Todri, Petraq Papajorgji, Francesco Scalera: A Multilayer Perceptron Network-Based Analysis to Configure SMES Strategic Entrepreneurship for Sustainable Growth was heteroskedastic (n = -6.73 x 10-16; 5 = 0.893). These numbers confirm that the relationship between the examined variables was not linear. The third examination related to SMEs growth measure was performed using a multivariate log-linear model in which the dependent variable was BoS (ln total assets) (Table 2). In this case, the data showed that the variables that had a statistical significance at the 95% confidence level for BoS are AG mixed, EO foreign (organizational characteristics), INV (liquidity area), LTD (leverage area), and CV (growth area). However, they can predict only 56.7% of BoS volatility; thus the presence of multicollinear-ity issues in the model (VIF > 1) was confirmed. The residuals had a positive correlation (DW = 1.645), and their distribution was heteroskedastic (n = -5.84 x 10-16; 5 = 0.763), confirming that the relationship between the examined variables was not linear. The Pearson correlation also confirmed a weak correlation between the three variables examined as SMEs growth measures (ROE vs. ROA = 0.018; ROA vs. BoS = -0.116, and BoS vs. ROE = -0.143). There was a correlation between organizational characteristics, and a correlation between financial aspects of SMEs (Table 2). In addition, the data showed a correlation between organizational characteristics and financial aspects of SMEs. The multicollinearity, heteroskedasticity, and non-linearity issues between the variables and the model errors themselves from the multivariate regression models proved that these models are not adequate to measure SMEs' growth due to the complexity (Coenders & Saez, 2000). Thus, individual and correlated effects of the analyzed factors on the matter cannot be correctly evaluated. This means that this analysis should go deeper and use other tools to explain the complex relationships among elements of the study phase. Thus, a more complex examination able to adequately evaluate all the variables' correlations and derived issues toward SME sustainable growth is needed. This study used the age of the firm as a variable to measure SMEs' sustainable growth during the three firm development stages (start-up, pertaining to businesses with 0-5 years of activity; grown businesses, with 6-15 years of activity, and matured, with more than 15 years of activity). 4.2 Multilayer perceptron networks analysis Multilayer perceptron artificial neural networks are computational models able to model complex relationships between inputs/independent variables and outputs/dependent variables (Nabney, 2002). The multilayer perceptron classification used in this research classified the interaction between inputs (organizational characteristics and financial indicators) in three SME development stages: (1) start-up; (2) grown; and (3) matured. It calculated the ordinary and numerical variables outcomes and their observed nonlinearities easily by using a hidden layer with one unit and evaluated the direct relationship that existed between examined variables. The explanatory/input variables included in the MLP network analysis were ROE, ROA, and BoS (previously used as SME growth measures), in addition to all other variables previously mentioned pertaining to organizational characteristics and financial business areas. In supervised learning, the MLP class of neural networks manages a set of training samples used to infer a classifier to predict a correct output value (Zhang, 2000). The MLP model confirmed that the overall percentage of incorrect predictions in the composition of testing and training sample was about 1.5%. This demonstrated that the model is statistically valid at the 95% confidence level. The receiver operating characteristic (ROC) curve analysis (Figure 1) proves that the analysis fairly classified the output in the start-up stages (ROC area = 0.694). The classification of output in the grown and matured stages was very good (ROC areas = 0.803 and 0.788, respectively). The same results were obtained using a lift chart (Figure 2). In approximately 50% of cumulative cases, most businesses were in the grown stage, 30% were in the matured stage, and the remaining 20% were classified in turns as start-up, grown, and matured. The MLP hidden layer activation function was a hyperbolic tangent, whereas the final activation function was the softmax function. The MLP statistics show that the SME organizational characteristics which had a normalized impact over 30% on SME sustainable development phases were AG (male), OG (male), OG (female), and BS performing. In addition, in terms of SME financial aspects, the variables with 42 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 a normalized impact over 30% on SME sustainable development stages were GPM, AT, and ROE in the operational efficiency area); LTDER, LTD, and LEV (leverage area); CA, ITR, short-term assets (STA), and INV (liquidity area); and ROA, BoS, and CV (growth area). All the variables considered in the analysis had a statistically significant impact on SME sustainable growth classification, as measured by the age of firm (FA). 5. DISCUSSION AND CONCLUSION Regression analyses of SME growth using as dependent variables ROE, ROA and BoS do not produce valid outcomes. This is attributed to the existence of multicollinearity, heteroskedasticity, and non-linearity issues between the variables and the model errors. This is why ROE (an operational efficiency indicator) used as a SME growth measure is not affected by the organizational characteristics. In this case, only the financial aspects influence SMEs' growth. When ROA and BoS (growth area indicators) are used as SME growth measures, the organizational characteristics significantly impact them in addition to financial aspects. It is a novelty of this study to approach the evaluation of SMEs sustainable growth operating in Albania using an MLP. The literature recommends using MLPs for SME performance and creditworthiness evaluation (Derelioglu & Gurgen, 2011; Abde-laziz, Alaya & Dey, 2018). This study makes a novel contribution by using an MLP to evaluate SME growth and sustainable growth. The MLP was used to accurately identify the factors (organizational characteristics and financial indicators) influencing SME sustainable growth stages. The MLP helped to identify factors impacting SME growth in a complex business environment (Table 3). The MLP classifier showed that the SME organizational characteristics with a greater influence on the business philosophy of SMEs in the start-up stage were the administration gender (female/male); the business ownership, in cases in which it is divided from administration issues; equity origin (foreign and joint ventures); ownership gender (male and mixed cases); and the business classification in non-per- forming status (see variables' correlation signs in Table 3). Furthermore, the financial indicators which impacted the SMEs' growth in this stage were ITR, INV, STA, and STD (liquidity area); GPM and NPM (operational efficiency area); ICR, LEV and LTD (leverage area); and CV and ROA (growth area). The maintenance of all the previously mentioned financial indicators at high levels represents value added for the SMEs for sustainable growth in the start-up stage. The opposite also can be confirmed: maintaining financial indicators at low levels negatively affects SMEs' sustainable growth (Table 3). From the organizational philosophy point of view, the analysis demonstrated that SMEs in the grown stage mainly were administrated by mixed genders, and the business owners were involved in the business administration process (Table 3). Data showed that female business ownership patterns in this stage are decisive. The correlation statistics were significantly negative in cases in which SMEs were in the grown stage and owned by females. Furthermore, the businesses in the grown stage repaid loans according to schedule. In terms of the financial aspects, grown businesses prefer to maintain low levels of CA (liquidity area), AT, and ROE (operational efficiency area), LTDER (leverage area), and BoS/assets growth (growth area). The increase of the remaining financial indicators, such as ITR, GPM, and NPM, is maintained to assure continuous progress. This study found similarities between SMEs in the matured stage those in the start-up stage (Table 3). These businesses maintained lower levels of ITR, INV, STA, and STD (liquidity area); GPM and NPM (operational efficiency area); ICR, LEV, and LTD (leverage area); and CV and ROA (growth area) than did those in the start-up stage. Furthermore, in terms of organizational aspects, these businesses implemented strategies to expand their portfolio activity. Thus, their philosophy is open-minded toward administration issues, separation of each activity and the respective management duties, and foreign direct investments. Joint ventures bring additional experiences in the national market, even in the majority of cases in which the business owners are males. Another important element is that these businesses also may be classified as non-performing in terms of loan repayment schedules. This classifi- Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 43 Ardita Todri, Petraq Papajorgji, Francesco Scalera: A Multilayer Perceptron Network-Based Analysis to Configure SMES Strategic Entrepreneurship for Sustainable Growth cation is imposed by the banks, but nevertheless a business may perform well in its activities. The businesses borrowers' status from banks is evaluated considering their worst repayment schedule case. This means that as different loans are granted (pertaining to different activities undertaken from these businesses), bankers use the contamination evaluation rule to evaluate their entire loan performing status. The business philosophy statistics showed that SMEs in the start-up stage face agency issues as the owners (male/mixed partnership cases) delegate to skilled managers (of female/male gender) the business management process. This is true mainly for foreign and joint ventures businesses. These businesses configure the daily activities as open organizations. Investing in a liberal management style and assuming a risk-taker approach vis-a-vis the financial aspects means that their business strategy for growth is aggressive. Furthermore, in this stage, SMEs explore as much as possible all the internal capabilities expressed in terms of knowledge toward innovation in order to survive in such a competitive business environment. Because of the competitive business environment, SMEs in the start-up stage try to maintain a balanced approach toward liquidity, operational efficiency, and leverage management, seeking a rapid growth process which can result in sustainable growth. Another approach is that one pursued by the grown SMEs, which seem to adapt the business needs to specific organizational arrangements. In this stage, the owners mainly are females directly involved in the business management process. The administration process is facilitated by trusting some specific issues to skilled managers, although the decision-making process remains centralized. These businesses continuously invest in assets and profitability growth, supported by long-term funding. This behavior is present specifically in the most profitable business areas, which correspond to market timing strategy. In this way, they achieve growth and further progress. Matured SMEs instead prefer to foster growth continuously; thus, they continuously increase liquidity, operational efficiency, and leverage indicators by diversifying portfolio activities and trying at the same time to benefit as much as possible from the situational market circumstances. In general, they pursue an aggressive managerial business style. They centralize the decision-making process in separate business areas, in which skilled managers are responsible for growth. Their progress at this stage is safe, but the owners control the benchmarks for future growth strategies. The presented model is a good example of how SMEs define better financial and internal organizational policies to reach their growth and sustainable growth goals. This study also affirmed that female ownership in each business development stage, independently of invested equity origin, represents added value. In particular, partially/fully female-owned initiatives should be supported with dedicated training and facilitated with specific fiscal instruments, especially when SMEs are in the start-up stage and deal with innovation issues. However, it is widely accepted that the business evolution dynamicity should be monitored continuously to initially help businesses pass the potentially delicate stages. Furthermore, there is a need to support the growth of the entire national economy. This study examined factors influencing growth and sustainable growth of SMEs in Albania, which are considered to be the backbone of the national economy. It enriches the existing literature in three different ways. First, the study addressed SME growth and sustainable growth issues considering the close interaction among organizational networking and financial mechanisms. This is a novelty of this study. Second, a multilayer perceptron artificial neural network analysis mapped sets of input data onto a set of appropriate SME output classified in three different growth stages. In the current literature these models are used to measure only SME performance and creditworthiness. Thus, this study provides a novel utility of these models. Third, this paper presents to SMEs a valuable model that can be used to organize internal information to define their sustainable growth strategies. Using a sample of 120 SMEs operating in the Albanian market, growth was measured through return on assets, return on equity, and business size. 44 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 In this approach, growth took into consideration a firm's liquidity, its operational efficiency, and leverage indicators in addition to organizational characteristics by using multivariate regression analyses. Based on the results of multivariate regression analyses, a multilayer perceptron artificial neural network model was designed and used to specify the factors influencing SME development stages, measured by firms' age, classified as start-up, pertaining to businesses with 0-5 years of activity; grown businesses, with 6-15 years of activity; and matured, with more than 15 years of activity. The MLP-ANN model easily calculated the ordinary and numerical variables' outcomes and their observed nonlinearities using a hidden layer with one unit and evaluated the direct relationship between the examined variables. The explanatory/input variables included in the MLP network analysis were ROE, ROA, and BoS, previously used as SME growth measures, in addition to organizational and financial variables. The MLP data confirmed that the overall percentage of correct predictions in the composition of testing and training sample was about 98.5%. This demonstrates that the model is statistically valid. The empirical findings of this research confirmed that SMEs in the start-up stage assume a risk-taker approach toward sustainable growth. In the grown stage, they implement a market-timing strategy in selecting investments with a sustainable growth perspective. Businesses in the matured stage replicate the liberal managerial style of SMEs in the start-up stage, but employ a less aggressive strategy. The presented model is a good example of how SMEs define better financial and internal organizational policies to reach their growth and sustainable growth goals. This study also affirmed that female ownership in each business development stage, independently of invested equity origin, represents added value. In particular, partially/fully female-owned initiatives should be supported with dedicated training and facilitated with specific fiscal instruments, especially when SMEs are in the start-up stage and deal with innovation issues. However, it is widely accepted that the business evolution dynamicity should be monitored continuously in order to initially help businesses pass the potential delicate stages. Furthermore, there is a need to support the growth of the entire national economy. EXTENDED SUMMARY/IZVLEČEK Avtorji so v prispevku analizirali tesno interakcijo med organizacijskim povezovanjem in finančnimi mehanizmi rasti ter trajnostne rasti malih do srednje velikih podjetji v Albaniji. Podatki o 120 malih in srednjih podjetjih za obdobje 2017-2018 so bili analizirani z uporabo multivariatnih regresij in modela nevronskih mrež, imenovanega večplastni perceptron. Sprva so bili podatki analizirani s pomočjo multivariatne regresijske analize. Namen slednje je bil potrditi korelacijo med rastjo podjetij, kar je bilo merjeno s tremi različnimi kazalniki: donosnost kapitala, donosnost sredstev in velikost podjetja. Pri oceni rasti podjetja se je v tem primeru poleg organizacijskih značilnosti upoštevala tudi likvidnost podjetja, njegova operativna učinkovitost in kazalniki vzvoda. Rezultati, pridobljeni v začetni fazi, so bili vključeni v model umetnih nevronskih mrež, s pomočjo katerega so avtorji želeli pridobiti oceno rasti malih do srednje velikih podjetji. Nadalje so avtorji želeli preveriti tudi njihovo trajnostno rast. Slednje je temelilo na starosti podjetja, ki je vključevala tri možnosti: zagonsko (start-up) obdobje, obdobje rasti in zrelo obdobje. Rezultati modela so pokazali, da mala in srednje velika podjetja v zagonskem obdobju sprejemanjo bolj tvegan pristop doseganja trajnostne rasti. Po drugi strani, podjetja v obdobju rasti vlagajo v trajnostno rast na podlagi trženske časovne strategije. Podjetja v fazi zrelosti uporabljajo bolj liberalni slog vodenja. Slednje je podobno strategiji malim in velikim podjetjem v začetni fazi vendar s to razliko, da je strategija podjetji v zreli fazi manj agresivna. Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 45 Ardita Todri, Petraq Papajorgji, Francesco Scalera: A Multilayer Perceptron Network-Based Analysis to Configure SMES Strategic Entrepreneurship for Sustainable Growth REFERENCES Abdelaziz, F. B., Alaya, H. & Dey, P. K. (2018). 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Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 47 Ardita Todri, Petraq Papajorgji, Francesco Scalera: A Multilayer Perceptron Network-Based Analysis to Configure SMES Strategic Entrepreneurship for Sustainable Growth APPENDICES Appendix 1 Table 1: Summary of research variables Variable Measurement Abbrev Administrator gGender Administrator's gender (female = 0, male = 1, and mixed genders = 2) AG g 1 Business ownership Business owner (administrator = 0, no administrator = 1) BO N <Û Equity origin Business equity origin (national = 0, foreign = 1, and joint ventures = 2) EO c çç Ownership gender Ownership gender (female = 0, male = 1, and mixed gender ownership = 2) OG O « Borrower status Borrower status (non-performing + 30 due days = 0, performing 0-29 due days = 1) BS £ Current assets Short-term assets/Short-term debts CA S o Inventory turnover ratio Cost of goods sold/Average inventory ITR .c Inventory End of year inventory INV Ss i3 Short-term assets Cash + trade securities portfolio + receivable accounts + inventory STA Short-term debts Payable accounts, short-term loans STD S2 o H—> Gross profit margin Gross profit/Net sales GPM •sl Net profit margin Net profit/Net sales NPM ti CD Assets turnover (Net profit + interest expenses)/Average equity AT ^ o Return on equity Net profit/Average equity ROE £ S 8 "¡3 .c Long-term debt/equity ratio Long-term debt/equity ratio LTDER Interest coverage ratio Earnings before interest and taxes/Interest expenses ICR a g 0J Total leverage ratio Total debts/Total assets LEV 0J ■—1 Long-term debts End-of-year long-term debts LTD £ Collateral value End-of-year market collateral value CV ■S 8 .c Age of firm Analysis period/business registration period (start-up (0-5 years0 = 0; growth (6-15 years) =1; maturity (>15 years) = 2) FA -c H—> IS o Return on assets Net profit/Average assets ROA (J Business size ln(total assets) BoS "O c Source: NRC and CRBA data Appendix 2 Table 2: Summary of multivariate regressions models Model no. Significant variables at 95% R2 Residuals correlation (1 - DW/2) Heteroskedasticity (n; 6) Multicollinearity (VIF) 1. ROE GPM, NPM, AT, LTDER, STD 0.996 2.719 (-2.12 x 10-15; 0.892) VIF > 1 2. ROA NPM, LEV, CV, OG mixed, BS performing 0.571 1.781 (-6.73 x 10-16; 0.893) VIF > 1 3. BoS AG mixed, EO foreign, INV, CV, LTD 0.567 1.645 (-5.84 x 10-16; 0.763) VIF > 1 48 Dynamic Relationships Management Journal, Vol. 9, No. 2, November 2020 Appendix 3 Table 3: MLP model parameter estimates Input variables Values Input variables Values Input variables Values CA -0.701 CV 0.741 EO foreign 0.162 ITR 0.859 STA 0.375 EO mixed 0.262 GPM 0.573 STD 0.065 OG female -0.861 NPM 0.113 LTD 0.645 OG male 1.027