Volume 23 Issue 1 Article 2 June 2021 Accounting for Sources of Information in Trade Fairs: Evidence Accounting for Sources of Information in Trade Fairs: Evidence from Portuguese Exhibitors from Portuguese Exhibitors Pedro M. da Silva Porto Polytechnic, Porto, Portugal, mendonca.silva@ua.pt José F. Santos Porto Polytechnic, Porto, Portugal, jfsantos@iscap.ipp.pt Victor F. Moutinho Beira Interior University, Covilha, Portugal, ferreira.moutinho@ubi.pt Follow this and additional works at: https://www.ebrjournal.net/home Part of the Business Commons Recommended Citation Recommended Citation da Silva, P . M., Santos, J. F., & Moutinho, V. F. (2021). Accounting for Sources of Information in Trade Fairs: Evidence from Portuguese Exhibitors. Economic and Business Review, 23(1), 15-25. https://doi.org/ 10.15458/2335-4216.1002 This Original Article is brought to you for free and open access by Economic and Business Review. It has been accepted for inclusion in Economic and Business Review by an authorized editor of Economic and Business Review. ORIGINAL ARTICLE Accounting for Sources of Information in Trade Fairs: Evidence from Portuguese Exhibitors Pedro M. da Silva a, *, Jose F. Santos a , Victor F. Moutinho b a Porto Polytechnic, Porto, Portugal b Beira Interior University, Covilh~ a, Portugal Abstract Tradefairsareimportantsourcesofinformationfordecisionmakinginmarketingmanagement.Currently,tradefairs are places where participants share useful data and information, while creating relationships between customers (vis- itors) and suppliers (exhibitors). However, only a limited number of studies have focused on the identification of the sources of information that exhibitors can provide for marketing managers at trade fairs. This study examines the importance of the different types of information resources that can be delivered by exhibitors to managers in order to transfer information about product and market trends. Based on the data from a survey of 172 Portuguese executives from different industries, the theoretical hypotheses are tested, using CFA (Confirmatory Factor Analysis). Consistent with our hypotheses, the results show that Direct Marketing techniques, such as face-to-face contacts and product/servicedemonstrations,areoftenusedbyexhibitors.Informationindigitalformatsanddemonstrationindigital equipment (Digital Marketing) are also used in trade fairs to display information to potential customers. Additionally, theorganizationofparallelevents(EventMarketing)duringatradefairsupplementsthepackageofactivitiesdeveloped by exhibitors to transmit and capture information for their companies. These results provide certain support for the importance of trade fairs in view of being a rich source of market information about not only new technological de- velopmentsofproducts,butalsomajorstrengthsandweaknessesofcompetitors,andfuturemarkettrends,amongother types of information needed for the marketing planning. Keywords: Trade fairs, Information sources, Information exchange, Exhibitors' perspective JEL classification: L81, M41 Introduction T rade fairs represent an opportunity where under the same roof and during a short period of time thousands of potential clients, competitorsandspecialistsgather(Silva,2014).As such,tradefairscanbeanimportanttooltocollect useful data and information about a particular industry (Maskell, 2014). Nevertheless, trade fairs have also been consistently neglected in the marketing research process, especially in gath- eringmarketinginformationand asanelement of theknowledgesharingprocessintheorganization (Zielinski&Leszczynski,2011).Ontheotherhand, Søilen(2010)statesthattradefairsaresomeofthe most effective intelligence sources. For instance, Zielinski and Leszczynski (2011) and Sarmento and Farhangmehr (2016) argue that knowledge is an important element to both visitors and exhib- itors, because knowledge transfer plays the key role in any company's ability to develop and maintain a strategic competitive advantage over time (De Luca & Cano Rubio, 2019). Currently, visitors are changing their habits to- wards wanting to spend less time at trade fairs, while at the same time getting more value and experience in return, as trade fairs have similarities Received 16 November 2019; accepted 16 April 2020. Available online 15 June 2021. * Corresponding author. E-mail addresses: mendonca.silva@ua.pt (P.M. da Silva), jfsantos@iscap.ipp.pt (J.F. Santos), ferreira.moutinho@ubi.pt (V.F. Moutinho). https://doi.org/10.15458/85451.1002 2335-4216/© 2021 School of Economics and Business University of Ljubljana. This is an open access article under the CC-BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/). with retailing (Gilliam, 2015). A valuable experience in trade fairs offers cognitive stimulation, not only resulting in new knowledge, but also strengthening the exhibitor/visitor relationship (Gopalakrishna & Lilien, 2012). In the current context, exhibitors should adopt a dynamic posture of information transfer, since visitors value innovationdacquiring new knowledge, thus strengthening exhibitor- visitor relationships (Sarmento & Farhangmehr, 2016).Consequently,tradefairsneedtobeanalyzed throughaperspectivethatintegratesbothtradeand knowledge (Li & Bathelt, 2017), as trade fairs can be a quite decisive information source for marketing research purposes and at the same time critical for the innovation and knowledge creation processes (Bathelt, 2017; Sarmento & Sim~ oes, 2019). Under these circumstances, it is interesting to investigate how exhibitors are delivering informa- tion in a trade fair environment. Accordingly, the purpose of the present study is to identify, classify and evaluate the relative importance of the sources of information that exhibitors use at trade fairs to exchange information about new products, compet- itors and market trends. The focus is on the exhibi- tors'ratherthanthevisitors’perspective.Toaddress the aforementioned purpose, a survey based on an online questionnaire was administered to a sample ofPortuguesetradefairsexhibitorswhohadbythen participated in several trade fairs around the world. In the following sections of the paper, we first presentthetheoretical backgroundofthe sources of informationtransferinatradefaircontext.Next,we describe the methodology used and report the re- sults of our empirical examination. The last two sections discuss the results of the study and present the conclusions that include some limitations and suggestions for future research. 1 Theoretical background Trade fairs and exhibitions, including activities related to business with focus on commercial oper- ations, are classified as “business and trade events” (Getz, 2012). In a traditional perspective, trade fairs are events that bring together a group of suppliers, distributors and related services in a single place and at an exact period of time, where they then display their products and/or services in physical exhibitions under the guidance of a particular organizer (Black, 1986). The definition of Kirchgeorg, Springer, and Kast- ner (2010) of trade shows is “market events of a specific duration, held at regular intervals, at which a large number of companies present the main product range of one or more industry sectors”. It should be noted that the terms trade fair, trade show and exhibition are often used interchangeably (Bettis-Outland et al., 2012), because “the term ‘trade show’ is regarded as a synonym for fairs, trade fairs and exhibitions” (Kirchgeorg, Springer & Kastner;2010).Forconsistency, theterm “tradefair” is used in this paper. Currently, trade fairs are more than a simple marketing tool (Silva, 2014), as they are a privileged placefortheinteractionbetweenthebuyer(visitors) and the seller (exhibitors) (Sarmento et al., 2014). Further, trade fairs are also used to develop per- sonal relationships (Kirchgeorg, Jung, & Klante, 2010) and reduce the physical, social and techno- logical distance between buyers and sellers, thus facilitating learning and inter-firm cooperation (Ling-Yee, 2006). Commonly, trade fairs are a workspace where participants search and share in- formation about the trends in the industry and the market (Bathelt, 2017; Rinallo et al., 2010; Rittichai- nuwat & Mair, 2012; Sarmento & Sim~ oes, 2019; Smith et al., 2003). Information sharing activities are intrinsically related to the practice of searching and using in- formation (Pilerot & Limberg, 2011) which further impacts the activities of the partners involved (Sonnenwald, 2006). In the existing literature, we find a mixture of the terms “information sharing” and“knowledgesharing”.Indeed,Savolainen(2017) argues that the terms are largely similar and can be used interchangeably. 1.1 Trade fair as space of information exchange Business information spring up in trade fairs and can be delivered by both attendees and exhibitors. The information can be acquired through a rich variety of media, from printed information, human embodied information, to observation and personal contacts (Keegan, 1989). In fact, it is the personal contacts that are at the heart of interaction between people in a trade fair. The relationship ability has a significant direct impact on the intention to share information (Al-Busaidi & Olfman, 2017). Also, the characteristics of the recipient person influence the motivation to share information (Zhang & Jiang, 2015). The strong impact of technological tools on the effectiveness of exchanging information between individuals, companies or organizations (Hedge- beth, 2007)in fluences the ability of managers to optimize their decision making (Harrison et al., 2015). As argued by Hassan et al. (2017), in the transfer of information, it is necessary to also value the role of the individual (i.e. skills, relations, etc.) 16 ECONOMIC AND BUSINESS REVIEW 2021;23:15e25 (Hassan et al., 2017), because the interaction be- tween technological and intellectual resources is essential for organizational survival (Heisig et al., 2016). The process of information acquisition de- pends on the personal initiatives that need to be done by the individual, respecting the structure of the organization (Hassan et al., 2017). In particular, trade fairs are spaces for inter-organizational re- lationships andvalue creation (Locatelli etal.,2019), where companies establish business relationships and generate learning experiences and customer engagement (Sarmento & Sim~ oes, 2019). Trade fairs are a unique opportunity for partici- pants(meaningbothexhibitorsandvisitors)tomeet and communicate face-to-face with third parties (Sarmento et al., 2015). This process involves inter- action between participants and fulfills a human need to communicate and socialize (Kitchen, 2017). Face-to-facecontactwithpotentialclientsanddirect competitorsisoneofthemostimportantreasonsfor the exhibitor to invest in trade fairs (Kellezi, 2013). Bettis-Outland et al. (2010) and Bettis-Outland et al. (2015) created the Return on Trade Show Informa- tion (RTSI) to measure the tangible and intangible benefits that the exhibitor accrues as a result of using market information that is acquired or asso- ciated with participation in trade fairs. ForMaskelletal.(2006)tradefairsare“temporary hubsthatstimulateprocessesofknowledgecreation and dissemination”. Face-to-face communication at trade fairs is obviously the differentiating factor (Sarmento et al., 2015) that allows for increased transparency and mobilizes knowledge or solutions (Ibert, 2007; Maskell, 2014). Currently, visitor behavior at trade fairs is not characterized by providing “fun, fantasies and feel- ings” that are usually used as motivators in other events, however, at trade fairs there isanincreasing trend in entertainment activities as a means of sharing information (Jensen, 1999; Søilen, 2010)and cognitive experiences (Kitchen, 2017). Rittichainu- wat and Mair (2012) identify that one of the main motivations of visitors to visit trade fairs lies exactly in the acquisition of information. Consequently, Rittichainuwat and Mair (2012) divide trade fairs visitors into two groups. Thefirst of the two groups, designated by “Shoppers”, is characterized by the main motivation of acquisition. For this group, the exposed product is what really matters to acquire satisfaction and define the future intention to buy (Sarmento & Farhangmehr, 2016). The other group is called “Total Visitors”, of which the main moti- vation is the search for recent information in the industry (Rittichainuwat & Mair, 2012). These visi- tors are people who usually participate in parallel activities, such as seminars and workshops, and want to be always informed about new market trends. This type of visitors is interested in visiting trade fairs that are an intense and memorable human experience, which is able to satisfy a wide spectrum of expectations (Sarmento & Farhang- mehr, 2016). Tradefairsgenerallyfacilitatefivemajorexchange functions: transactional (sales), informational (in- formation sharing), social (relational), symbolic and cultural (Tafesse & Skallerud, 2015). Despite the hasty,fluidandhighlydynamicnatureoftradefairs, the information sharing that takes place at these events plays a significant role as a singular process which fosters learning of both customers and sup- pliers (Reychav, 2009). In fact, at trade fairs, the information transfer between human beings involves extensive commu- nication (Albino, 2004; Søilen, 2010) and currently the main motivations of trade fairs visitors are cognitive and relational in nature (Han & Verma, 2014; Kirchgeorg, Jung, & Klante, 2010; Kitchen, 2017;Rinallo et al.,2010;Whitfield & Webber, 2011). Thisexplainstradefairsasaverypowerfulsourceof information (Bathelt & Schuldt, 2010; Zielinski & Leszczynski, 2011), as it is the trade fairs environ- mentthatgeneratesarichamountofaggregatedata about an industry, market and competitors. Kozak (2006) highlights intelligence information about competition,whileTafesseetal.(2010)definehowto collectcompetitiveintelligence.Consequently,there is a vast occurrence of the terms “trade fairs intel- ligence” or “exhibit intelligence” in the existing literature (Ratajczak, 2007; Søilen, 2010). The organizations in general look for knowledge components from external partners (Benkler, 2006). For example, the “main actors” of trade fairs are simultaneously the visitors, exhibitors and orga- nizers (Lin et al., 2015) and the vast majority of visitors are not the purchasing decision makers of the companies, but the people who are likely to be useful to the exhibitor (Blythe, 2010). Therefore, trade fairs allow sharing information among orga- nizers, exhibitors (competitors), visitors (potential customers, partners, suppliers), sponsors, etc. (Maskell,2014)andprovideaninsightintoindustry, markets, products/services, technology trends (Borghinietal.,2006;Maskell,2014)andinnovations (Bathelt, 2017). 1.2 Sources of information in a trade fair Gębarowski and Wia_ zewicz (2014) present as the main sources of information during a trade fair namely (i) face-to-face conversations at the stands, ECONOMIC AND BUSINESS REVIEW 2021;23:15e25 17 (ii) demonstrations of exhibits, (iii) printed adver- tising materials (leaflets, brochures, catalogues, folders, etc.), (iv) Promotional materials on elec- tronic devices(applicationsontradefair attendants’ mobiledevices,communicationviasocialmedia)(v) tradefaircatalogues,(vi)tradefairwebsite,and(vii) additional events prepared by organizers during trade shows (contests, seminars, conferences, etc.). From the aforementioned factors, the one that stands out is the power of face-to-face contacts with thousands of potential customers, competitors and industry experts under one roof (Kellezi, 2013; Sar- mentoetal.,2015).Simeoneetal.(2017)andStevens (2005) enhance the role of design through artifacts, sketches, visual representations or prototypes, doc- uments, all in order to translate ideas, knowledge, theoretical and technical requirements into formats that can bemoreeasilyunderstoodandappreciated by various stakeholders at trade fairs. Sarmento et al. (2015), in turn, highlight seminars or the orga- nization or social events scheduled before, during and after trade fairs as essential elements for so- cialization and information sharing between participants. The Cheng (2014) study shows that knowledge processes are embedded in the informal social interaction (organizers, exhibitors and visitors) that takes place at trade fairs. The knowledge is created by observing and interpreting the trade fair envi- ronment and other actors within the same envi- ronment space. In addition, the use of information technology tools at trade fairs has a significant impact on the achievement of the trade fair's ob- jectives; nevertheless, the results vary according to the levels of professional experience (Singh et al., 2017). Therefore, the most traditional means of trans- mitting information, such as face-to-face contact, product/service demonstrations, product testing and distribution of documentation (e.g. catalogs, leaflets, product information papers) are still very much used by exhibitors. However, new forms of informationtransmission,suchasdigitalequipment and the organization of events/activities at trade fair, are emerging. As stated by Proszowska (2018), currently contact should be more intensive and involve other channels and communication tools. The most important reasons for a company to show at trade fairs are the following: i) reinforce- ment of market presence (international markets), ii) chance to find new ideas and test new products during the event, iii) strengthening relationships withcurrentandpotentialclients,andiv)improving the company's image and reputation (Santos & Mendonça,2014).Currently,theexhibitorsvaluenot onlythe implementation ofsales programs,but also cognitive actions and a relational marketing perspective (Blythe, 2010). This trend is already underway, for example, Shereni et al. (2018) state in their study that exhibitors disapprove weak sharing of information, bad time management and slow internet connectivity at trade fairs (digital commu- nication). For example, mobile marketing is a sig- nificant global trend with huge growth potential, and Prenzel (2010) shows that this digital channel can be an excellent asset of information transfer for organizers, exhibitors and visitors. Indeed, a study by Dexperty (2015) reveals that digital trans- formation is changing the way companies are per- forming at trade fairs. Theheterogeneityandthenumberofcontactsthat trade fairs allow (Kellezi, 2013) together contribute tothe overalleffectivenessoftrade fairsas atoolfor sharing information. Therefore, trade fairs are clearly a tool for acquiring information for exhibi- tors, namely about potential customers (visitors). Bettis-Outland et al. (2018) illustrate the interface between emotional intelligence, trust and learning in a trade fairs context. Emotional intelligence is defined as “the ability to perceive and express emotion, assimilate emotion in thought, understand and reason with emotion, and regulate emotion in the self and others” (Mayer et al., 2000). As such, observing potential customers (visitors) andotherexhibitors(competitors)arebasicformsof information acquisition. However, the number of the tools available to be used by exhibitors to implement these goals that are traditionally involved in trade fairs, namely observing customers and competitors, is increasing (Proszowska, 2018). Similar to Gębarowski and Wia_ zewicz (2014),we propose the hypotheses that serve the purpose of grouping the sources for information acquisition during a trade fair. Direct Marketingintegrates all one-to-onecontact items designed not only for immediate response, but also for cultivating lasting relationships (Kotler & Keller, 2015). Face-to-face conversations at trade fairs stands are frequently used at trade fairs as a medium that transmits information about the pre- sentedproducts/services(Gębarowski&Wia_ zewicz, 2014). H1. Exhibitors will use Direct Marketing for infor- mation acquisition during the trade fair. Regarding the first hypothesis, digital Marketing includes the use of digital tools and devices such as television sets, mobile phones and electronic bill- boards (Dodson, 2016). In addition, promotional material on electronic devices, such as applications 18 ECONOMIC AND BUSINESS REVIEW 2021;23:15e25 on trade fair attendants’ mobile devices and communication via social media, and the trade fair website are commonly used at trade fairs as a tool for sharing information about participants, prod- ucts/services, innovations, among others (Gębar- owski & Wia_ zewicz, 2014). H2. Exhibitors will use Digital Marketing for infor- mation acquisition during the trade fair. Observation of the competitor's stands and the behavior analysis of potential customers may require good memory and/or extensive notes, but it is nevertheless a useful tool for collecting data in various situations (Kawulich, 2012). In a trade fair context, as it comprises visitors, exhibitors and or- ganizers, all these three players can collect data from each other (Cheng, 2014; Proszowska, 2018). H3. Exhibitors will use Observation for information acquisition during the trade fair. Event Marketing is defined as the marketing disci- pline focused on face-to-face interaction via live events (Preston, 2015). It relates to holding or attending events at trade fairs, such as seminars, workshops,etc.Tradefairsareaneventinthemselves (Silva,2014),buttheyalsoallowtheholdingofparallel events that favor commercial, social, formative and informative exchanges (Gębarowski & Wia_ zewicz, 2014;Sarmento,Farhangmehr&Sim~ oes;2015). H4. Exhibitors will use Event Marketing for infor- mation acquisition during the trade fair. 2 Methodology An online survey was used to collect data. The questionnaire was elaborated based on the objec- tives of the research and a review of the literature, including professional experiences of the re- searchers involved. 2.1 Questionnaire A structured questionnaire was devised to collect the required data. The questionnaire had 12 items about the information transfer channelsused bythe exhibitors at the trade fair. The questionnaire ends with a question about the frequency or experiences of respondents with participating in international trade fairs. Theitemsusedarebasedontheliteraturereview. All items are measured using a five-point Likert scale (1 ¼ totally disagree, 5 ¼ strongly agree). However, before applying the questionnaire, the researchersrequestedtheopinionofmanagerswith longexperienceinthefieldoftradefairs,inboththe role of exhibitors and organizers, in order to eval- uate the pertinence of the questions and items. Experts consider the items relevant (see Table 1). A data analysis was then performed, using a sta- tistical package (SPSS, version 24 and AMOS, version 20). 2.2 Sample The population used in this study is composed of exhibitorsthathavebeenengagedinB2Btradefairs at least in the last year. In the process of the con- struction of the database, exhibitors fromfive major PortugueseB2Btradefairs,namelyEMAF(Exponor, Porto), Portojoia (Exponor Porto), Concreta (Expo- nor, Porto), Maquitex (Exponor, Porto) and Tektonica (FIL, Lisbon), were included. The completed database contained 1850 marketing and/ or sales director contacts. Wethenappliedthequestionnairethroughanon- line platform between December 2018 and January 2019 and obtained 172 useable questionnaires, cor- responding to a response rate of about 9%. The sample integrated very experienced exhibitors, as almost50%oftherespondentsparticipatedinseveral tradefairsannually.Thedataanalysiswasperformed usingSPSS24andAMOS20. 3 Data analysis 3.1 Descriptive statistics Table2presentsthemean,standarddeviationand the minimum and maximum values. According to the table, the face-to-face contacts (M ¼ 4.53) are widelyhighlightedbyexhibitors.Theexhibitorsalso emphasize the offer of documentation (M ¼ 4.31), the observation of visitors (M ¼ 4.24) and the observation of competitors (M¼ 4.08). The least sources of information indicated by the exhibitors are holding events (M¼ 3.05) and infor- mation gathering from the presence at events developed by trade fair organizers, competitors, etc. (M¼ 3.08). 3.2 Exploratory factor analysis AVarimaxrotated factorial analysis was usedand revealed four major factors that explain 62.88% of the total variance of the items. The value of com- monalities ranges from 0.56 to 0.71. The overall ECONOMIC AND BUSINESS REVIEW 2021;23:15e25 19 Kaiser-Meyer-Olkin (KMO) sample adequacy mea- sureis0.703forthesetofvariableswhich,according to the defined criteria, should be considered acceptable (Hair et al., 2010). Table 3 shows the results of the principal component analysis for each of the twelve items. It canbeseenthatfourfactors emerge.Thefirstfactor is a combination of different methods to capture information, mainly by Direct Marketing (product demonstrations and tests, printed information and personal contacts). The Direct Marketing factor is therefore represented by four item loadings. The second factor includes the information obtained by digital support or equipment, where the Digital Marketing factor is represented by two items. The third factor is observation and includes three items related with the observation of competitors, cus- tomers, visitors and the organizers. Finally, the fourth factor encompasses three items and relates with the type of events that occur in a trade fair promoted by the trade fair organization and the competition. The factors found (Direct Marketing, Digital Marketing, Observation and Event Marketing) revealanacceptableinternalconsistency(Tavakol& Dennick, 2011). Cronbach's alpha ranges from 0.566 forObservationto0.689forDirectMarketing,which can be considered of moderate reliability (Hinton et al., 2014). Table 4 provides the averages and standard de- viations of the items corresponding to each construct. It is appropriate that all items related to the same construct have similar mean scores, otherwise they may be removed, making the study more reliable (Weisberg, 1992). That said, (Y3) “Product testing” and (Z4) “Orga- nization observation” items are removed, because they have significantly different average scores against the other items of the same construct. The item (Z3) “Information gathering from trade fair organizers, seminars, competitors, etc.” is also removed to increase the reliability of the scale. Consequently, the values of the Cronbach's alpha change are for Direct Marketinge0.632, Digital Marketinge0.604, Observatione0.506, and Event Marketinge0.598, which anyway maintain a mod- erate reliability (Hinton et al., 2014). 4 Hypotheses testing To confirm the factors found in the exploratory factorial analysis and evaluate the convergent and Table 1. Summary of questionnaire items and sources. COD. ITEMS SOURCE Y1 Face-to-Face contact Kellezi, 2013; Gębarowski & Wia_ zewicz, 2014; Sarmento et al., 2015 Y2 Product/Service demonstrations Gębarowski & Wia_ zewicz, 2014; Simeone et al., 2017; Stevens, 2005 Y3 Product testing Gębarowski & Wia_ zewicz, 2014; Sarmento et al., 2015; Simeone et al., 2017; Stevens, 2005 Y4 Documentation offerdCatalogs, leaflets, product sheets … Gębarowski & Wia_ zewicz, 2014; Simeone et al., 2017; Stevens, 2005 Y5 Information offer in digital format (pen-drives, CD, mobile, …) Shereni et al., 2018; Prenzel, 2010; Dexperty, 2015 Y6 Holding additional eventsdworkshops, seminars, lectures, etc. Gębarowski & Wia_ zewicz, 2014; Sarmento et al., 2015 Y7 Demonstrations in digital equipmentdlaptops, plasmas, mobile, touch monitors, … Shereni et al., 2018; Prenzel, 2010; Dexperty, 2015 Z1 Competitor observation Cheng, 2014; Kozak, 2006; Proszowska, 2018; Tafesse Korneliussen & Skallerud, 2010 Z2 Observation of customer/visitors behavior Cheng, 2014; Proszowska, 2018 Z3 Information gathering from trade fair organizer, seminars, competitors, etc. Shereni et al. (2018) Z4 Organization observation Cheng, 2014; Proszowska, 2018 Z5 Participation in parallel events seminars, lectures, workshops, etc. Gębarowski & Wia_ zewicz, 2014; Sarmento et al., 2015 Source: Own elaboration. Table 2. Descriptive statistics. Item code N Mean Standard Deviation Minimum Maximum Y1 172 4.53 0.556 3 5 Y2 172 4.27 0.659 2 5 Y3 172 3.86 0.969 1 5 Y4 172 4.31 0.744 1 5 Y5 172 3.20 1.173 1 5 Y6 172 3.05 1.199 1 5 Y7 172 3.74 1.173 1 5 Z1 172 4.08 0.709 2 5 Z2 172 4.24 0.537 2 5 Z3 172 3.08 1.087 1 5 Z4 172 3.84 0.731 2 5 Z5 172 3.41 1.025 1 5 Source: Own elaboration. 20 ECONOMIC AND BUSINESS REVIEW 2021;23:15e25 discriminant validity of the factors, we proceed to a second-order Confirmatory Factor Analysis. Thesecond-order Confirmatory FactorAnalysis is a composite of common factor configuration (Van Riel et al., 2017), thus allowing us to test the 4 fac- tors, namely Direct Marketing, Digital Marketing, Observation and Event Marketing, that might be part of a composite of 4 information acquisition tools (factors) that exhibitors can use at trade fairs. InFig.1,weseethefourfactorsolutionsubmitted to the second-order Confirmatory Factor Analysis using AMOS. This approach was applied to examine the dimensionality of each construct and also to test the model fit of the four constructs. Based on the criteria defined by Hair et al. (2010), Byrne (2010),andKline (2011), the model reveals a satisfactory fit to the data (see Table 5). It should be noted that the sample of the study contains only 172 observations, while Kline (2011) suggests a necessary minimum of 200 cases or ob- servations. Obviously, it would be desirable to have more cases, but as we are facing a preliminary study, 172 observations are, in our view, adequate forfurtheranalysis.Moreover,wedefendthatsmall samplesshouldnotbeneglected,forexample,some recent authors, such as Harrington et al. (2013) and Sideridis et al. (2014) value smaller samples, how- ever, determining sample size requirements for the structural equation model always requires a careful and deliberate assessment of the specific model in question (Harrington et al., 2013). Despite these limitations, the confirmatory factor analysis shows adequate support for the model and, at the same time, allows us to test the hypotheses. The results of the direct effects reveal that the “Exhibitor Information Acquisition Tools” has a positive and significant impact on the constructs “Direct Marketing” (b ¼ 0.443, p < 0.000), “Digital Marketing” (b¼ 0.851, p < 0.000), and “Event Mar- keting” (b¼ 0.758, p < 0.000). The first hypothesis (H1) is validated, suggesting that Direct Marketing is used at trade fairs to culti- vate lasting relationships, as mentioned by Kotler and Keller (2015), and to have face-to-face contacts to transfer information about the products/services (Gębarowski & Wia_ zewicz, 2014). Nevertheless, the weight of the coefficient beta also tells us that this source of information is the least preferred among executives. The second hypothesis (H2) is also confirmed, meaning that Digital Marketing is used frequently Table 3. Principal Component Analysis with VARIMAX rotation. ITEM CODE ITEMS COMPONENTS Direct Marketing Digital Marketing Observation Event Marketing Y1 Face-to-Face contact 0.630 0.247 0.205 0.239 Y2 Product/Service demonstrations 0.789 0.148 0.134 0.191 Y3 Product testing 0.727 0.063 0.132 0.400 Y4 Documentation offerdCatalogs, leaflets, product sheets … 0.622 0.443 0.121 0.096 Y5 Information offer in digital format (pen-drives, CD, mobile, …) 0.085 0.786 0.065 0.112 Y6 Holding additional eventsdworkshops, seminars, lectures, etc. 0.292 0.505 0.090 0.523 Y7 Demonstrations in digital equipmentdlaptops, plasmas, mobile, touch monitors, … 0.136 0.765 0.047 0.162 Z1 Competitor observation 0.073 0.106 0.774 0.076 Z2 Observation of customer/visitors behavior 0.216 0.086 0.706 0.188 Z3 Information gathering from trade fair organizer, seminars, competitors, etc. 0.309 0.372 0.301 0.527 Z4 Organization observation 0.082 0.135 0.644 0.416 Z5 Participation in parallel events seminars, lectures, workshops, etc. 0.155 0.125 0.040 0.783 Cronbach's alpha 0.689 0.604 0.566 0.586 Note: Items measured on a 5-point Likert scale. Kaiser-Meyer-Olkin measure of sampling adequacy 0.703; Bartlett's test of sphericity (172.51, p < 0.000). Table 4. Items analysis. Construct Item code N Mean Standard Deviation Direct Marketing Y1 172 4.53 0.556 Y2 172 4.27 0.659 Y3 172 3.86 0.969 Y4 172 4.31 0.744 Digital Marketing Y5 172 3.20 1.173 Y7 172 3.74 1.173 Observation Z1 172 4.08 0.709 Z2 172 4.24 0.537 Z4 172 3.84 0.731 Event Marketing Y6 172 3.05 1.199 Z3 172 3.08 1.087 Z5 172 3.41 1.025 Source: Own elaboration. ECONOMIC AND BUSINESS REVIEW 2021;23:15e25 21 at trade fairs as a new information and communi- cation technology to exchange digital information about the exhibitor and its products/services (Gębarowski & Wia_ zewicz, 2014). The highest value of the coefficient beta in this case shows the importance of this tool to both exhibitors and executives. The fourth hypothesis (H4) is supported, as Event Marketingisoftenusedbytheorganizationoftrade fairs in order to promote more proactively the interaction between exhibitors and visitors through parallels events (seminars, workshops) that favor commercial, social, formative and informative ex- changes (Gębarowski & Wia_ zewicz, 2014; Sarmento et al., 2015). The second highest value of the coefficient beta shows that trade fair organizations are increasingly dynamic in creating events and developinganewapproachtothetraditionalwayof organizing trade fairs. The third hypothesis (H3) is, on the other hand, not validated, as the direct effect of “Exhibitor In- formation Acquisition Tools” on “Observation” is not significant (b¼ 0.158, p < 0.643). In short, H1, H2 and H4 are corroborated, while the data do not support H3 for lack of statistical relevance, which means that sources of information from competitor and customer/visitor behavior observationseemtobelessimportantforexhibitors. Nevertheless, these results demonstrate the multi- disciplinary nature of trade fairs, as these groups of information allow for multiple functions (e.g. face- to-face contacts, product demonstrations, attending events). The results also show that most exhibitors use trade fairs to develop contact with visitors throughdirect,digitalandeventmarketing,inorder to transfer and capture information about new products, technologies, industry, market, competi- tion, etc (Maskell, 2014). Indeed, trade fairs are a unique and crucial platform for presenting in- novations(Bathelt, 2017),information transfer about products/services (Borghini et al., 2006; Maskell, 2014), collecting information about market changes (Kozak,2006;Maskell,2014),technology(Borghiniet al., 2006; Maskell, 2014), and last but not least, to collect information about competitors (Kellezi, 2013; Fig. 1. Second-order Confirmatory Factor Analysis (standardized estimates). Source: Own elaboration. Table 5. Summary of the goodness of the fit for the model. Measures Cut off points (a) Results Chi-square (x 2 ) Smaller to 0 33.66 Degree of freedom (df) 23 X 2 /Df 1-5 1.4463 Goodness of fit index (GFI) 0.90 0.960 Tucker Lewis Index (TLI) 0.90 0.931 Adjusted goodness of fit index (AGFI) 0.90 0.956 Comparative fit index (CFI) 0.95 0.921 Incremental fit Index (IFI) 0.90 0.958 Root mean squared error of approximation (RMSEA) 0.08 0.051 (a) Hair et al. (2010), Byrne (2010), Kline (2011). 22 ECONOMIC AND BUSINESS REVIEW 2021;23:15e25 Maskell, 2014). Trade fairs are places that facilitate the exchange of ideas between experts, offering along new knowledge, relationships and in- novations, all within and outside the respective in- dustry sectors, and where exhibitors can search and find solutions to problems and optimize decision making (Harrison et al., 2015; Ibert, 2007; Maskell, 2014). In addition, innovation could be a conse- quence of the engagement and learning processes that result from attending trade fairs (Sarmento & Sim~ oes, 2019). 5 Conclusion and limitations The findings of this research confirm that trade fairs are consistently used as important physical spaces for information exchange between visitors, exhibitors and trade fair organizers. Sources of in- formation, such as face-to-face contacts and prod- uct/service demonstrations (Direct Marketing), are indeed important for exhibitors. Information in digital formats and demonstration in digital equip- ment are in fact used at trade fairs to display in- formation to potential customers. Additionally, the organization of parallel events (Event Marketing) during the trade fair supplements the package of activities developed by exhibitors to transmit and capture information for their companies. The present study is one of a few recent attempts toidentifythesourcesofinformationthatexhibitors useattradefairstoexchangeinformationaboutnew products, competitors and market trends. It is perhaps the first empirical study to adopt the ex- hibitor's rather than the visitor's perspective. The results help to provide support for the strategies that exhibitors might use at trade fairs and the best ways to exchange information with visitors via Direct Marketing, Digital Marketing, and Event Marketing. At any rate, these strategies need to be formulated, planned and implemented, depending on the type of the trade fair (consumer/profes- sional), industry/activity sector, and also visitors. Despite the results obtained, this study is limited by firstly, the small size of the sample that restricts the generalization of the research, and secondly, the itemsusedforeachconstructthatrequireanupdate. However,thisstudyisapreliminaryapproachtothe topic that envisages the identification of the main sources of information in the context of trade fairs. Future studies should in any case focus on the development of the information transfer mecha- nismsusedduringtradefairs,aswellasonhowand in what circumstances such knowledge is transferred. References Al-Busaidi, K. A., & Olfman, L. (2017). Knowledge sharing through inter-organizational knowledge sharing systems. VINE.JournalofInformationandKnowledgeManagementSystems, 47(1), 110e136. Albino, V. (2004). Organization and technology in knowledge transfer. Benchmarking: An International Journal, (6), 584e600. Bathelt, H. (2017). Trade fairs and innovation. In Harald Bathelt, Patrick Cohendet, Sebastian Henn, & Laurent Simon (Eds.), The elgar companion to innovation and knowledge creation. Cheltenham: Edward Elgar (Chapter 31, pp. 509e522). Bathelt, H., & Schuldt, N. (2010). International trade fairs and global buzz, Part I: Ecology of global buzz. European Planning Studies, 18(1), 1957e1974. Benkler, Y. (2006). The wealth of networks: How social production transforms markets and freedom. New Haven: Yale University Press. Bettis-Outland, H., Borders, A., & Johnston, W. (2015). Return on tradeshowinformation:Acomparisonofexhibitorandvisitor perspectives. In H. Spotts (Ed.), Marketing, technology and customer commitment in the new economy. Developments in mar- keting science: Proceedings of the academy of marketing science. Cham: Springer (pp. 289e289). Bettis-Outland, H., Cromartie, J., Johnston, W., & Borders, A. (2010). The return on trade show information (RTSI): A con- ceptual analysis. Journal of Business & Industrial Marketing, 25(4), 268e271. Bettis-Outland,H.,Johnston,W.,&Wilson,D.(2012).Usingtrade show information to enhance company success: An empirical investigation. Journal of Business & Industrial Marketing, 27(5), 384e391. Bettis-Outland, H., Monica, D., & Guillory, M. (2018). Emotional intelligence and organizational learning at trade shows. Jour- nal of Business & Industrial Marketing, 33(1), 126e133. Black,R.(1986). The trade show industry: Management and marketing career opportunities. East Orleans, MA: Trade Show Bureau. Blythe, J. (2010). Trade fairs as communication: A new model. Journal of Business & Industrial Marketing, 25(1), 57e62. Borghini, S., Golfetto, F., & Rinallo, D. (2006). Ongoing search among industrial buyers. Journal of Business Research, 59(1), 1151e1159. Byrne, B. (2010). Structural equation modeling with AMOS: Basic concepts, application and programming (2nd ed.). New York: Taylor & Francis. Cheng,H.(2014).Internationalfashiontradeshowsasknowledge creation platforms for Finnish microenterprises. Master's thesis. In International Design Business Management (IDBM). DepartmentofManagementandInternationalBusiness;Aalto University, School of Business, Finland. De Luca, P., & Cano Rubio, M. (2019). The curve of knowledge transfer: A theoretical model. Business Process Management Journal, 25(1), 10e26. Dexperty.(2015).Blogbymessefrankfurt for thedigitalbusiness. In Study digital business transformation brief information, Frank- furt, 23 September. Available at: https://connected. messefrankfurt.com/en/. Dodson, I. (2016). The art of digital marketing: The definitive guide to creating strategic, targeted, and measurable online campaigns. Hoboken NJ: John Wiley & Sons. Gębarowski, M., & Wia_ zewicz, J. (2014). Contemporary trade shows as a place of knowledge sharing about tourism products. In Human capital without borders: Knowledge and learning for quality of life, proceedings of the management, knowledge and learninginternational conference 2014(pp.25e27). June 2014, Portoro_ z. Available at: https://ssrn.com/ abstract¼2706205. Getz,D.(2012). Event studies: Theory, research and policy for planned events (Events Management) (2nd ed.). London: Routledge. Gilliam, D. A. (2015). Trade show boothscapes. Journal of Mar- keting Management, 31(17e18), 1878e1898. ECONOMIC AND BUSINESS REVIEW 2021;23:15e25 23 Gopalakrishna, S., & Lilien, G. L. (2012). Trade shows in the business marketing communications mix. In GaryL. Lilien, & RajdeepGrewal (Eds.), Handbook of business- to-business marketing. Cheltenham: Edward Elgar (Chapter 13, pp. 226e245). Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis (7th ed.). NJ: Prentice. Hall. Han, H., & Verma, R. (2014). Why attend tradeshows? A com- parison of exhibitor and attendee's preferences. Cornell Hos- pitality Quarterly, 55(3), 239e251. Harrington, K., Clark, S., & Miller, M. (2013). Sample size re- quirements for structural equation models an evaluation of power, bias, and solution propriety. Educational and Psycho- logical Measurement, 73(6), 913e934. Harrison, R., Parker, A., Brosas, G., Chiong, R., & Tian, X. (2015). Theroleoftechnologyinthemanagementandexploitationof internal business intelligence. Journal of Systems and Informa- tion Technology, 17(3), 247e262. Hassan, N., Noor, M., & Hussin, N. (2017). Knowledge transfer practice in organization. International Journal of Academic Research in Business and Social Sciences, 7(8), 750e760. Hedgebeth, D. (2007). Making use of knowledge sharing tech- nologies. VINE. Journal of Information and Knowledge Manage- ment Systems, 37(1), 49e55. Heisig,P., Suraj,O.,Kianto,A.,Kemboi,C., Arrau,G.,&Easa,N. (2016). Knowledge management and business performance: Global experts' views on future research needs. Journal of Knowledge Management, 20(6), 1169e1198. Hinton, P., McMurray, I., & Brownlow, C. (2014). SPSS explained (2nd ed.). London: Routledge. Ibert,O.(2007).Towardsageographyofknowledgecreation:The ambivalences between knowledge as an object and knowing in practice. Regional Studies, 41(1), 103e114. Jensen, R. (1999). The dream society: How the coming shift from in- formation to imagination will transform your business. New York: McGraw. Hill. Kawulich, B. (2012). Collecting data through observation. In Claire Wagner, Barbara Kawulich, & Mark Garner (Eds.), Doing social research: A global context (pp. 150e160). New York: McGraw Hill. Keegan, W. J. (1989). Global marketing management (4th ed.). Eng- lewood Cliffs: Prentice. Hall International Editions. Kellezi, J. (2013). The effectiveness of trade shows in global competition. European Academic Research, 1(3), 2286e4822. Kirchgeorg, M., Jung, K., & Klante, O. (2010). The future of trade shows: Insights from a scenario analysis. Journal of Business & Industrial Marketing, 25(4), 301e312. Kirchgeorg, M., Springer, C., & Kastner, E. (2010). Objectives for successfully participating in trade shows. Journal of Business & Industrial Marketing, 25(1), 63e72. Kitchen, E. (2017). What is the value of networking? An exami- nation of trade show attendee outcomes. Journal of Convention & Event Tourism, 18(3), 191e204. Kline, R. B. (2011). Principles and practice of structural equation modeling (3th ed.). New York: The Guilford Press. Kotler, P., & Keller, K. (2015). Marketing management (15th ed.). Essex: Pearson Education. Kozak, N. (2006). The expectations of exhibitors in tourism, hos- pitality and the travel industry. Journal of Convention & Event Tourism, 7(3), 99e116. Li, P., & Bathelt, H. (2017). From temporary market to temporary cluster: Evolution of the Canton Fair. Area Development and Policy, 2(2), 154e172. Ling-Yee, L. (2006). Relationship learning at trade shows its an- tecedents and consequences. Industrial Marketing Management, 35(2), 166e177. Lin, Y., Kerstetter, D., & Hickerson, B. (2015). Developing a trade show exhibitor's overall satisfaction measurement scale. In Tourism travel and research association (Vol. 28). Advancing Tourism Research Globally. Locatelli, D., Silveira, M., & Mour~ ao, P. (2019). Speed dating or marriage? Brazilian business fairs according to a sample of metal/mechanic companies. Journal of Business & Industrial Marketing, 34(1), 80e94. Maskell, P. (2014). Accessing remote knowledgedthe roles of trade fairs, pipelines, crowdsourcing and listening posts. Journal of Economic Geography, 14(5), 883e902. Maskell, P., Bathelt, H., & Malmberg, A. (2006). Building global knowledgepipelines:Theroleoftemporaryclusters. European Planning Studies, 14(8), 997e1013. Mayer, J., Salovey, P., & Caruso, D. (2000). Models of emotional intelligence. In R. J. Sternberg (Ed.), Handbook of intelligence (pp. 396e420). Cambridge: University Press. Pilerot, O., & Limberg, L. (2011). Information sharing as a means to reach collective understanding. Journal of Documentation, 67(2), 312e333. Prenzel, I. (2010). Applicability of mobile marketing in the marketing mix of trade fair organizers. Munich: GRIN Verlag. Preston, C. (2015). Event marketing: How to successfully promote events, festivals, conventions and expositions. London: Wiley. Proszowska,A.(2018).Methodsofevaluationoftradefairsresults employed by exhibitorse an overview and scope of applica- tion. Handel Wewnętrzny, 5(376), 236e246. Ratajczak,J.(2007).Engineeringengineers:Atruestoryonhowto develop a trade show intelligence process. Competitive Intelli- gence Magazine, 10(1), 6e9. Reychav,I.(2009).Knowledgesharinginatradeshow:Alearning spiral model. VINE-Journal of Information and Knowledge Man- agement Systems, 39(2), 143e158. Rinallo, D., Borghini, S., & Golfetto, F. (2010). Exploring visitor experiences at trade shows. Journal of Business & Industrial Marketing, 25(4), 249e258. Rittichainuwat, B., & Mair, J. (2012). Visitor attendance motiva- tions at consumer travel exhibitions. Tourism Management, 33(5), 1236e1244. Santos,J.F.,&Mendon ça, P. (2014). Motivations to participate in international trade fairs: The Portuguese experience. British Journal of Economics, Management & Trade, 4(12), 1957e1972. Sarmento, M., & Farhangmehr, M. (2016). Grounds of visitors' post. Trade fair behavior: An exploratory study. Journal of Promotion Management, 22(5), 735e750. Sarmento, M., Farhangmehr, M., & Sim~ oes, C. (2015). A rela- tionship marketing perspective to trade fairs: Insights from participants. Journal of Business & Industrial Marketing, 30(5), 584e593. Sarmento, M., & Sim~ oes, C. (2019). Trade fairs as engagement platforms: The interplay between physical and virtual touch points. European Journal of Marketing, 59(9), 1782e1807. Forthcoming. Sarmento, M., Sim~ oes, C., & Farhangmehr, M. (2014). B2B in- teractionsat tradefairs andrelationship quality:A conceptual approach. In Arch G. Woodside, Hugh M. Pattinson, & Roger Marshall (Eds.), Field guide to case study research in business-to- business marketing and purchasing (pp. 167e189). Bingley: Emerald Group Publishing Limited. Savolainen, R. (2017). Information sharing and knowledge sharing as communicative activities. Information Research: An International Electronic Journal, 22(3). paper 767. Available at: http://informationr.net/ir/22.3/paper767.html. Shereni, N., Mpofu, N., & Ngwenya, K. (2018). Exhibitors' perception of the 2017 Sanganai/Hlanganani world tourism expo. African Journal of Hospitality, Tourism and Leisure, 7(3), 1e13. Sideridis, G., Simos, P., Papanicolaou, A., & Fletcher, J. (2014). Using structural equation modeling to assess functional con- nectivity in the brain power and sample size considerations. Educational and Psychological Measurement, 74(5), 733e758. Silva, P. (2014). Feiras e Exposiç~ oes. Instrumento de Competitividade Internacional. Lisboa: Chiado Editora. Simeone, L., Secundo, G., & Schiuma, G. (2017). Knowledge translationmechanismsinopeninnovation:Theroleofdesign in R&D projects. Journal of Knowledge Management, 21(6), 1406e1429. 24 ECONOMIC AND BUSINESS REVIEW 2021;23:15e25 Singh, J., Shukla, P., & Kalafatis, S. (2017). IT usage for enhancing trade show performance: Evidence from the aviation services. Journal of Business & Industrial Marketing, 32(3), 398e408. Smith, T., Hama, K., & Smith, P. (2003). The effect of successful trade show attendance on future show interest: Exploring Japanese attendee perspectives of domestic and offshore in- ternational events. The Journal of Business and Industrial Mar- keting, 18, 403e418. Søilen, K. S. (2010). Boosting innovation and knowledge through delocalization: Market intelligence at trade shows. Problems and Perspectives in Management, 8(3), 200e207. Sonnenwald, D. (2006). Challenges in sharing information effec- tively: Examples from command and control. Information Research (4) paper 270. Available at: http://InformationR.net/ ir/11.4/paper270.html. Stevens, R. (2005). Trade show and event marketing: Plan, promote and profit (2nd ed.). Mason: Cengage Learning. Tafesse, W., Korneliussen, T., & Skallerud, K. (2010). Importance performanceanalysisasatradeshowperformanceevaluation and benchmarking tool. Journal of Convention& Event Tourism, (4), 314e328. Tafesse, W., & Skallerud, K. (2015). Towardsan exchange viewof trade fairs. Journal of Business & Industrial Marketing, 30(7), 795e804. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's Alpha. International Journal of Medical Education, 2,53e55. Van Riel, A., Henseler, J., Kemeny, I., & Sasovova, Z. (2017). Esti- mating hierarchical constructs using consistent partial least squares: The case of second. order composites of common factors. Industrial Management & Data Systems, 7(3), 459e477. Weisberg, F. (1992). Central tendency and variability-quantitative applications in the social sciences. Thousand Oaks: Sage Publications. Whitfield, J., & Webber, D. J. (2011). Which exhibition attributes create repeat visitation? International Journal of Hospitality Management, 30, 439e447. Zhang, X., & Jiang, J. (2015). With whom shall I share my knowledge? A recipient perspective of knowledge sharing. Journal of Knowledge Management, 19(2), 277e295. Zielinski, M., & Leszczynski, G. (2011). Trade fairs as source of knowledgee the role of trade fairs organizer. The impact of globalizationonnetworksandrelationshipsdynamics.In 27th International IMP Conference, Glasgow. ECONOMIC AND BUSINESS REVIEW 2021;23:15e25 25