343 Organizacija, V olume 58 Issue 4, November 2025 Research Papers 1 Received: 13th May 2025; Accepted: 25th August 2025 Exploring the Role of Perceived Benefits and Attitudes Toward Web in Modelling Online Purchase Intentions: A Case of Slovenia Miha MARIČ 1 , Gašper JORDAN 2 , Robert LESKOVAR 1 1 University of Maribor, Faculty of Organizational Sciences, Kranj, Slovenia, miha.maric@um.si, robert.leskovar@um.si 2 Independent researcher, gasper.jordan77@gmail.com Background and Purpose: E-commerce has reshaped consumer behaviour by offering unparalleled convenience, variety and accessibility, while creating new opportunities for businesses to grow revenues. Despite its prominence, there remains a need for parsimonious models that explain online purchase intention in terms of core consumer perceptions. This study aims to develop and test a structural equation model (SEM) in which consumer perceived benefits and attitudes toward the web drive intention to purchase online. Methods: An online survey was administered to 190 Slovenian consumers. Questionnaire items were drawn from established scales measuring (a) perceived benefits of online shopping, (b) attitude toward the web, and (c) online purchase intention. Internal consistency was assessed via Cronbach’s alpha. SEM was then applied using IBM SPSS AMOS to evaluate both measurement and structural components of the model, testing hypotheses that per- ceived benefits influence both attitude and intention, and that attitude further mediates intention. Results: The survey instrument demonstrated excellent reliability (Cronbach’s α = 0.92). The three-construct SEM explained 74 % of the variance in online purchase intention. Fit indices indicated very good model performance (NFI = 0.969, NNFI = 0.970, CFI = 0.979, IFI = 0.979). All hypothesized paths were significant, confirming that higher per - ceived benefits enhance both positive web attitudes and purchase intentions, and that web attitudes further bolster intention. Conclusion: This streamlined SEM offers a robust and well-fitting explanation of consumer online purchase inten- tions. E-commerce platforms can leverage these insights by emphasizing the specific benefits consumers value and cultivating positive web experiences to drive sales. The model offers both practical guidance for online retailers and a foundation for future research, such as incorporating environmental consciousness, to refine our understanding of sustainable e-commerce adoption. Keywords: Attitude toward web, Perceived benefits, Online purchase intention, SEM, Consumer behaviour, e-com- merce DOI: 10.2478/orga-2025-0021 1 Introduction Usage of information and communication technolo- gies in recent decades has affected every aspect of busi- nesses (i.e. business intranets, business-to-business, busi- ness-to-government, big data, artificial intelligence) and people’s everyday activities (i.e. education, e-banking, en- tertainment, online gaming) (Cai, Fan & Du, 2017; Park & 344 Organizacija, V olume 58 Issue 4, November 2025 Research Papers Lee, 2011). Rich interactions and opportunities for all gen- erations in an online environment (Blažun V ošner, Bobek, Kokol, & Javornik Krečič, 2016) also boost social media (Zhu, Wang, Wang, & Wan, 2016). E-commerce is bun- dled with a wide range of marketing tools (i.e. customer relationship management, data analytics, recommendation system) to detect purchase intentions (Lim, Osman, Sala- huddin, Romle & Abdullah, 2016). A change in customer behaviour, especially regard- ing privacy concerns, has been observed (Fortes & Rita, 2016), and the introduction of smart solutions, both on the part of retailers and customers, has been reported (Gerrik- agoitia, Castander, Rebón, & Alzua-Sorzabal, 2015). The growth of e-commerce is not likely to abate (Chen, 2012; Lai, 2014; Liao, Lin, Luo & Chea, 2016; Pei, Paswan & Yan, 2014; Shiau & Luo, 2012; Sin, Nor & Al-Agaga, 2012), because consumers find it more economical and more convenient to shop online (Hong & Cha, 2013). Limitations of traditional shopping, such as specific times and places, as well as the availability of sellers, are largely removed with the introduction of e-marketplaces (Hsieh & Liao, 2011; Kiang & Shang, 2015). Because online shopping is a process of purchasing a product via the web (Jusoh & Ling, 2012), it is essential to note that attitude towards the web is a crucial factor influencing the behavioural aspects of online consumers. Online shop- ping is increasing rapidly (Guritno & Siringoringo, 2013); therefore, understanding the impact of perceived benefits, along with attitudes towards the web, on online purchase intention is of great importance. We have found a lack of research on the impact of per- ceived benefits on attitudes towards the web and online purchase intention, as well as perceptions of e-commerce, which is the focus of our study. Hypothesized relations will be tested with the use of structural equation modelling. 2 Theoretical Background and Hypothesis Development This chapter provides insight into the literature review and previous research related to consumer behaviour, per- ceived benefits, attitude towards the web and online pur- chase intention. 2.1 eConsumer Behaviour Online purchasing involves decision-making, and cus- tomers typically collect information before making a pur- chase (Žnidaršič, Marič & Ferjan, 2012). Most frequently, this information includes the acceptable price and quality ratio, preferred brand, delivery and payment conditions (Oliveira-Castro, 2003). This decision-making process depends on the information processing style employed (Zinkhana & Braunsberger, 2004; Bleda & Valente, 2009), where the perceived value of the product plays a crucial role in decision-making. With the advent of internet consumer behaviour anal- ysis, the field has become interdisciplinary, requiring theoretical foundations from psychology, marketing sci- ence, human-computer interfaces, and economics (Foxall, 2003). Customer benefits, expressed as a utility function, are the core of the neoclassical theory of consumer behav- iour (Graham & Isaac, 2002). Psychologists and econo- mists ascribe rationality to the consumer; thus, choice it- self has been viewed as a cognitive activity (Foxall, 2003). Consumer choice, translated into a utility function, however, presents a bounded and simplified model of the customer decision-making process. Oliveira-Castro (2003) argues that consumer behaviour is not always induced by optimality and rationality. Baumgartner and Steenkamp (1996) discussed the importance of sensory stimulation by examining the products, and they concluded that purchase intention is also affected by a desire to adjust actual stimu- lation to the most preferred level. Fischer and Hanley (2007) analyzed decision behav- iour and found two distinguished types: extensive and lim- ited consumer decisions. The characteristics of extensive decisions are strong emotional involvement and a demand for additional information. Limited decisions are taken with less information due to the consumer’s prior expe- rience. The volume of possible information has increased ex- ponentially due to online presence and accessibility, influ- encing customers’ behaviour and choices. Whereas it was almost unimaginable for an individual to purchase goods from the other side of the world, it has become a standard practice nowadays due to the increase in e-commerce and improved infrastructure. 2.2 Perceived Benefits Online consumer behaviour is widely dependent on the perception of benefits associated with online shopping (Bhatnagar & Ghose, 2004; Garbarino & Strahilevitz, 2004; Huang, Schrank, & Dubinsky, 2004). Consumers perceive online shopping as convenient, offering various selections, low prices, personal attention, and easy access to information (Delafrooz, Paim, Haron, Sidin, & Khatibi, 2009). According to Gutman (1982), the benefits can be physiological, psychological, sociological or material in nature. The perceived benefits in online shopping are the sum of online shopping advantages that meet customers’ needs (Pavlou, 2003). Since consumers’ buying patterns can differ (Lee, Jackson, Miller-Spillman & Ferrell, 2015; Soopramanien, 2011), purchasing decisions are personal, and often very complex behaviours (Chen, Yan, Fan & Gordon, 2015), but in the end, the consumers make online 345 Organizacija, V olume 58 Issue 4, November 2025 Research Papers purchases for both convenience and enjoyment (Childers, Carr, Peck &Carson, 2001). 2.3 Attitude towards Web Over the past decade, companies have reached custom- ers across the web in various ways (Wu, Chen, Chen, & Cheng, 2014), as people spend increasing amounts of time on social media and the internet. The web has had a signif- icant impact on marketing practices, and most consumers are now comfortable purchasing products online (Poddar, Donthu &Wei, 2009). The reason behind the increase in online shopping is that online shopping enables a huge number of alternatives with immediate access to informa- tion of interest (Seock & Norton, 2007). An attitude is defined as a set of beliefs, feelings, and behavioural tendencies towards socially significant ob- jects, groups, events or symbols (Hogg & Vaughan, 2005), and it is a learned predisposition to behave in a consist- ently favourable or unfavourable way (Schiffman & Ka- nuk, 2000). We simplify that the attitude towards the web is an individual’s specific behaviour towards the web, to- gether with its attractiveness. Therefore, it is of great im- portance that potential consumers have a positive attitude towards the web, as it is a crucial starting point for them and e-retailers to make the final step towards purchasing products online. 2.4 Online Purchase Intention Purchase intention, defined by (Wang & Yang, 2008; Wells, Valacich & Hess, 2011), is “the decision to act or as a mental stage in the decision-making process where the consumer has developed an actual willingness to act towards an object or brand”. It is no surprise that under- standing customer online purchase intentions drives inten- sive research efforts in e-marketing and e-retail literature (Kwek, Tan, & Lau, 2015). Online purchase intention was defined by Kwek, Tan & Lau (2015), Hsu, Chang & Chen (2012), and Wu, Yeh & Hsiao (2011) as the consumer’s intention to perform a specified purchasing behaviour. Therefore, it can be uti- lized as a component of consumers’ cognitive behaviour. Online purchase intention is viewed as the situation when a “customer is willing and intends to become involved in an online transaction” (Pavlou, 2003), and it “is led by their emotions” (Ha & Lennon, 2010). As noted by Schiff- man & Kanuk (2007), an increase in purchase intention in- creases the likelihood of purchasing. The final decision to choose or opt out of the offered good or service depends on the consumers’ intention (Madahi & Sukati, 2012; Wang & Tsai, 2014). 2.5 Hypotheses Overview We formulated three hypotheses to determine the sig- nificance of the relationships between the constructs “Per- ceived benefits”, “Attitude towards web”, and “Online purchase intention”. We propose that perceived benefits and a positive attitude have a positive effect on online pur- chase intention. Proposed hypotheses were tested in the proposed model (Figure 1) as follows: • H1: Construct “Perceived benefits” explains a statistically significant part of the variance in construct “Attitude towards web”. • H2: Construct “Attitude towards web” explains a statistically significant part of the variance in construct “Online purchase intention”. • H3: Construct “Perceived benefits” explains a statistically significant part of the variance in construct “Online purchase intention”. The assumption is that “Perceived benefits” affect “Attitude toward web” and “Intention of online purchase” while “Attitude toward web” affects “Intention of online purchase”. 3 Methodology This study employs a quantitative approach to test the hypothesized relationships among perceived benefits, attitudes toward the web, and online purchase intention through a structural equation model (SEM). This chapter outlines the research design, data collection, and statistical methods employed, ensuring reliability and validity in the analysis. 3.1 Questionnaire and data collection An online questionnaire was designed, and invitations for an online survey were sent (considering sampling rele- vance (Etikan, Abubakar Musa & Sunusi Alkassim, 2016) in winter 2016 via e-mail and social media to participants in Slovenia. After gathering, we stripped incomplete re- sponses. IBM SPSS—Statistical Package for the Social Sciences, version 27, software was used for data analysis. Attitude towards web was measured on a 5-item scale developed by Küster, Vila and Canales (2016), which was: “ATW1 - This website connects with me. ATW2 - I would like to visit this website again. ATW3 - I feel comfortable navigating this website. ATW4 - This site is a good place to spend my time. ATW5 - I consider this website to be a good site for fashion. The response scale was a 5-point Likert scale ranging from 1 (completely disagree) to 5 (completely agree). The coefficient of reliability (Cron- bach’s alpha) was 0.92, respectively. 346 Organizacija, V olume 58 Issue 4, November 2025 Research Papers Perceived benefits were measured on a three-item scale developed by Chen et al. (2015), which were: “PB1 - I think online shopping can help me easily find a lower price. PB2 - I think online shopping has the advantage of a wide selection of products. PB3 - I think online shopping is more convenient than bricks-and-mortar shopping.” The response scale was a 5-point Likert scale ranging from 1 (completely disagree) to 5 (completely agree). The coef- ficient of reliability (Cronbach’s alpha) was 0.87, respec- tively. Online purchase intention was measured on a three- item scale developed by Salisbury, Pearson, Pearson and Miller (2001), which consisted of: “OPI1 - I would use the Internet for purchasing a product. OPI2 - Using the Internet for purchasing a product is something I would do. OPI3 - I could see myself using the Internet to buy a prod- uct.” The response scale was a 5-point Likert scale ranging from 1 (completely disagree) to 5 (completely agree). The coefficient of reliability (Cronbach’s alpha) was 0.94, re- spectively. 3.2 Sample Selection and Generalizability We analyzed complete responses from a total of 190 participants, comprising 98 men (51.6%) and 92 women (48.4%). The marital statuses of the participants were as follows: 60 (31.6%) married, 2 (1.1%) widowed, 5 (2.6%) divorced, 70 (36.8%) single, 19 (10.0%) in life partner- ships, and 34 (17.9%) married but living apart. The em- ployment statuses of the respondents were as follows: 74 (38.9%) students, 7 (3.7%) self-employed, 46 (24.2%) employed in the public sector, 59 (31.1%) employed in the private sector, 1 (0.5%) retired, and 3 (1.6%) unemployed. An educational levels earned by participants were: 1 (0.5%) without primary school, 1 (0.5%) primary school, 2 (1.1%) finished secondary vocational education, 4 (2.1%) finished technical secondary education, 42 (22.1%) high school diploma, 4 (2.1%) finished vocational college, 28 (14.7%) finished professional higher education, 66 (34.7%) bachelor’s degree, 27 (14.2%) master’s degree, and 15 (7.9%) PhD degree. The geographical location of the 190 respondents was: 69 (36.3%) in the western part of Slovenia and 121 (63.7%) in the eastern part. The average age of respondents was 29.7 years. Participants reported an average shopping experience of 7.7 years, with an average of 10.4 online purchases in the past year. The selection of a Slovenian sample allows for cultural specificity in understanding consumer behaviour but limits the generalizability of findings to other contexts. While the demographic diversity strengthens the study’s robustness, future research could expand to include participants from Table 1: Descriptive statistics for research constructs n M SD Min Max Perceived benefits 190 4.37 0.78 1 5 Attitude towards the web 190 4.14 0.99 1 5 Online purchase intention 190 4.36 0.85 1 5 Notes: n = total number of respondents, M = mean value, SD = standard deviation, Min. = minimum, Max. = maximum. Figure 1: The relationships between SEM constructs and standardized solutions, along with t-values for the hypotheses tested 347 Organizacija, V olume 58 Issue 4, November 2025 Research Papers different regions and levels of technological readiness (Žnidaršič, Marič & Ferjan, 2012; Oliveira-Castro, 2003). 3.3 Results Table 1 presents descriptive statistics for three groups of questions, which form constructs: perceived benefits, attitude toward the web, and online purchase intention (number of respondents, means, standard deviations, min- imums, and maximums). All mean values are above 4, and according to the Likert scale, we interpret them as strong to complete agreement that: online shopping is beneficial (M = 4.37), attitude towards use of the web is positive (M = 4.14), and online purchase intention is strong (M = 4.36). Structural equation modelling (SEM) (Prajogo & Mc- Dermott, 2005) was employed within IBM SPSS AMOS version 27 software to explore the relationships between constructs and test hypothezes of statistically significant relations. SEM combines factor and regression analysis, thereby enabling the evaluation of the significance of hy- pothezised relations among variables (Diamantopoulos & Siguaw, 2000). Figure 1 depicts the relations between SEM constructs and standardized solutions with t-values for the hypotheses tested. The data in our sample of questionnaires confirms all three hypotheses for relationships between constructs: • H1: Construct “Perceived benefits” explains a statistically significant part of the variance in construct “Attitude towards web” (standardized solution = 0.68, t-test = 9.17). • H2: Construct “Attitude towards web” explains a statistically significant part of the variance in construct “Online purchase intention” (standardized solution = 0.16, t-test = 1.85). • H3: Construct “Perceived benefits” explains a statistically significant part of the variance in construct “Online purchase intention” (standardized solution = 0.58, t-test = 6.14) We further examined fit indices, as explained in Hoop- er, Coughlan & Mullen (2008); Hu & Benter, 1999); and Kenny (2015), including the normed fit index (NFI), non- normed fit index (NNFI), comparative fit index (CFI), and incremental fit index (IFI). Table 2 presents the results of the examination of selected indices and standardized re- siduals. All indices demonstrated a statistically significant, very good fit according to reference values. (p-value = 0.0000). The ꭕ2 for our model was 87.35 with 32 degrees of freedom. We can conclude that: a) two constructs (“Perceived benefits” and “Attitude towards web”) can explain 74% of the variation in the construct “Online purchase intention”, and b) model fit indices show very good fit and statistically significant relations. 4 Discussion Online purchases are increasing, and consumer behav- iour is adapting, but there are still doubts and restraints among potential customers and organizations regarding e-commerce. We have therefore explored perceptions of existing and possible future customers. Perceived benefits and attitudes towards the web are positively related, and both are associated with online purchase intention. The study provides new insights into the interplay of perceived benefits, attitudes toward the web, and online purchase intention, contributing to the e-commerce and consumer behavior literature. Consistent with previous research, perceived benefits emerged as a significant de- terminant of both attitudes toward the web and online pur- chase intention (Bhatnagar & Ghose, 2004; Delafrooz et al., 2009; Chen et al., 2015). These findings validate the utility-driven model of consumer behavior while also em- phasizing the role of positive emotional responses (Gra- ham & Isaac, 2002; Poddar et al., 2009). Table 2: Results of examination of selected indices and standardized residuals Notes: NFI = Normed Fit Index, NNFI = Non-normed Fit Index, CFI = Comparative Fit Index, IFI = Incremental Fit Index. Fit indices Value for the model Reference value Model fit according to individual indices* ꭕ2/df 2.729 ≤ 2 or ≤ 5 Good fit NFI 0.969 ≥ 0.90 Very good fit NNFI 0.970 ≥ 0.95 Very good fit CFI 0.979 ≥ 0.93 Very good fit IFI 0.979 ≥ 0.95 Very good fit SRMR 0.0465 ≤ 0.08 Good fit 348 Organizacija, V olume 58 Issue 4, November 2025 Research Papers The findings underscore the significance of perceived benefits and attitudes toward the web in influencing online purchase intentions. Our research confirmed the hypoth- esis on the relations between constructs in the proposed SEM. The following significant and positive relations were extracted from survey data: a) “Perceived benefits” and “Attitude towards web”, b) “Attitude towards web” and “Online purchase intention” and c) “Perceived benefits” and “Online purchase intention”. These results contribute to the growing body of literature on e-consumer behav- iour by demonstrating the interplay between utilitarian and emotional factors in online decision-making (Žnidaršič et al., 2012; Graham & Isaac, 2002; Poddar et al., 2009). Finding b) specifically confirms and extends the study by Seock and Norton (2007), which was conducted only among US students and suggested that other popula- tion groups be considered to generalize the results more widely. Furthermore, the study underscores the potential of e-commerce platforms to integrate sustainability into their offerings, leveraging perceived benefits to encour - age eco-friendly purchasing behaviours (Gutman, 1982; Childers et al., 2001). The limitation of the proposed model is the omission of other determinants that may have implications for actu- al purchase decisions. However, simple models have one advantage – they can be applied with minimal resources and effort. There are likely constructs with undisclosed re- lationships that may involve perceived risks, privacy con- cerns, the fear of identity theft, loyalty to the brand/retail- er, and similar factors. Again, as the model becomes larger and more complex, the effort, resources, vulnerability, and mistrust grow exponentially. Also, the sample was geographically restricted to Slo- venia, which may limit generalizability to other cultural contexts. While the demographic diversity strengthens the study’s internal validity, cross-cultural comparisons could provide a more comprehensive understanding of the re- lationships among the constructs (Žnidaršič et al., 2012; Fischer & Hanley, 2007). The reliance on self-reported data introduces the pos- sibility of response biases, such as social desirability bias. Furthermore, the cross-sectional design limits the ability to assess dynamic changes in consumer behaviour over time (Etikan et al., 2016). Future research should employ longi- tudinal designs to capture temporal shifts and investigate additional factors, such as trust, perceived risks, or con- sumer environmental consciousness (Ha & Lennon, 2010; Schiffman & Kanuk, 2007). The results of our analysis demonstrate the theoretical- ly backed-up positive relations between the constructs in- cluded in the model. Positive perceived benefits positively affect the attitude towards the web, which in turn increases online purchase intention. The theoretical contribution of this study lies in testing and confirming the relationships among the observed constructs. For practitioners, these findings provide actionable in- sights into enhancing customer engagement and promoting sustainable consumption practices. E-commerce platforms can highlight perceived benefits such as convenience, cost savings, and access to eco-friendly products to enhance consumer attitudes and purchase intentions. Additional- ly, integrating features such as green delivery options and eco-certifications can further align online shopping with sustainable consumer values (Bhatnagar & Ghose, 2004; Chen et al., 2015; Schiffman & Kanuk, 2000). Retailers can also design user-friendly interfaces that foster positive attitudes toward the web by providing per- sonalized recommendations, simplifying navigation, and highlighting sustainability-related features (Poddar et al., 2009; Seock & Norton, 2007). These strategies align with previous findings that positive attitudes have a significant impact on consumer intentions to engage in online transac- tions (Wu et al., 2014; Schiffman & Kanuk, 2000). By deepening our understanding of online purchas- ing, we enable organizations in the e-commerce field to promote perceived benefits, improve attitudes towards the web, and, in turn, increase the online purchase intentions of current and potential customers. Due to the worldwide accessibility and similarity of e-commerce, we believe that these findings will be confirmed in future studies. Expanding the model to include sustainability-spe- cific constructs, such as environmental consciousness or perceived risks, could also provide richer insights into consumer behaviour (Chen et al., 2015; Ha & Lennon, 2010). Expanding the model to include moderating varia- bles, such as cultural influences or technological readiness, could also deepen understanding of consumer behaviour in diverse contexts (Žnidaršič et al., 2012; Olivera-Castro, 2003). Comparative studies across regions with varying levels of environmental awareness could further refine the model and provide insights into global e-commerce trends. 5 Conclusion Research efforts on factors, constructs, and situations that stimulate or deter potential buyers from making on- line purchases are substantial due to their practical value for real businesses. The presented and tested SEM model, along with the examination of fit indices, is encouraging from both theoretical research and practical implementa- tion perspectives. Simple models are not the best-suited solution for every application, but the one proposed and tested seems to have an acceptable ratio between “costs” (effort and resources required to implement) and “benefits” (accuracy and value to the business). To gain a higher percentage than 74% of explained variation of online purchase intention, new constructs should be introduced, causing a more complex and more “expensive” model. Particularly when considering that the 349 Organizacija, V olume 58 Issue 4, November 2025 Research Papers intention of online purchase has the greatest value at the moment of customer entrance into a web shop, the prac- tical implementation must measure intention in real-time, not through surveys. Current research in this field heavily relies on artificial intelligence, big data processing, and past purchasing data. E-commerce represents a powerful platform for ad- vancing both consumer convenience and sustainable prac- tices. By emphasizing perceived benefits and fostering positive attitudes toward the web, online retailers can in- crease purchase intentions while aligning with global sus- tainability objectives. This dual focus on enhancing user experience and promoting eco-friendly behaviour provides a competitive advantage for businesses and contributes to societal well-being. The findings of this study serve as a roadmap for prac- titioners and researchers, highlighting the potential of e-commerce to drive both economic growth and sustain- able development. Future research can build on this foun- dation to explore additional factors and strategies, further refining the pathways to sustainable consumer behaviour. Everything changes over time, and so does customer behaviour in online purchasing. Additionally, customers rely on advanced information and communication technol- ogies, as well as recommendations from friends and rel- atives, previous experiences, and preferences for specific brands and products. Therefore, we can identify numerous future research challenges in the world of digital market- ing. References Baumgartner, H., & Steenkamp, J. B. E. M. (1996). Ex- ploratory consumer buying behavior: Conceptual- ization and measurement. International Journal of Research in Marketing, 13, 121–137. https://doi. org/10.1016/0167-8116(95)00037-2 Bhatnagar, A., & Ghose, S. (2004). A latent class seg- mentation analysis of e-shoppers. Journal of Business Research, 57(7), 758–767. https://doi.org/10.1016/ S0148-2963(02)00357-0 Blažun V ošner, H., Bobek, S., Kokol, P., & Javornik Krečič, M. (2016). Attitudes of active older Internet users to- wards online social networking. Computers in Hu- man Behavior, 55, 230–241. https://doi.org/10.1016/j. chb.2015.09.014 Bleda, M., & Valente, M. (2009). Graded eco-labels: A demand-oriented approach to reduce pollution. Tech- nological Forecasting & Social Change, 76, 512–524. https://doi.org/10.1016/j.techfore.2008.05.003 Cai, Z., Fan, X., & Du, J. (2017). Gender and attitudes toward technology use: A meta-analysis. Computers & Education, 105, 1–13. https://doi.org/10.1016/j. compedu.2016.11.003 Chen, Y . Y . (2012). Why do consumers go internet shop- ping again? Understanding the antecedents of repur- chase intention. Journal of Organizational Computing and Electronic Commerce, 22(1), 38–63. https://doi.or g/10.1080/10919392.2012.642234 Chen, Y ., Yan, X., Fan, W., & Gordon, M. (2015). The joint moderating role of trust propensity and gender on con- sumers’ online shopping behavior. Computers in Hu- man Behavior, 43, 272–283. https://doi.org/10.1016/j. chb.2014.10.020 Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511– 535. https://doi.org/10.1016/S0022-4359(01)00056-2 Delafrooz, N., Paim, L. H., Haron, S. A., Sidin, S. M., & Khatibi, A. (2009). Factors affecting students’ attitude toward online shopping. African Journal of Business Management, 3(5), 200–209. Diamantopoulos, A., & Siguaw, J. A. (2000). Introducing LISREL. SAGE Publications. Etikan, I., Abubakar Musa, S., & Sunusi Alkassim, R. (2016). Comparison of convenience sampling and pur- posive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. Fischer, A., & Hanley, N. (2007). Analyzing decision be- haviour in stated preference surveys: A consumer psy- chological approach. Ecological Economics, 61, 303– 314. https://doi.org/10.1016/j.ecolecon.2006.02.024 Fortes, N., & Rita, P. (2016). Privacy concerns and online purchasing behaviour: Towards an integrated mod- el. European Research on Management and Business Economics, 22(3), 167–176. https://doi.org/10.1016/j. iedeen.2016.04.002 Foxall, G. R. (2003). The behavior analysis of consumer choice: An introduction to the special issue. Journal of Economic Psychology, 24, 581–588. https://doi. org/10.1016/S0167-4870(03)00002-3 Garbarino, E., & Strahilevitz, M. (2004). Gender differ- ences in the perceived risk of buying online and the effects of receiving a site recommendation. Journal of Business Research, 57(7), 768–775. https://doi. org/10.1016/S0148-2963(02)00363-6 Gerrikagoitia, J. K., Castander, I., Rebón, F., & Alzua-Sorzabal, A. (2015). New trends of intelligent e-marketing based on web mining for e-shops. Pro- cedia – Social and Behavioral Sciences, 175, 75–83. https://doi.org/10.1016/j.sbspro.2015.01.1176 Graham, F., & Isaac, A. G. (2002). The behavioral life-cycle theory of consumer behavior: Survey evidence. Jour- nal of Economic Behavior & Organization, 48, 391– 401. https://doi.org/10.1016/S0167-2681(01)00242-6 Guritno, S., & Siringoringo, H. (2013). Perceived useful- ness, ease of use, and attitude towards online shopping usefulness towards online airlines ticket purchase. Pro- cedia – Social and Behavioral Sciences, 81, 212–216. 350 Organizacija, V olume 58 Issue 4, November 2025 Research Papers https://doi.org/10.1016/j.sbspro.2013.06.415 Gutman, J. (1982). A means-end chain model based on consumer categorization processes. Journal of Mar- keting, 46(2), 60–72. https://doi.org/10.2307/3203341 Ha, Y ., & Lennon, S. J. (2010). Effects of site design on consumer emotions: Role of product involvement. Journal of Research in Interactive Marketing, 4(2), 80–96. https://doi.org/10.1108/17505931011051641 Hogg, M., & Vaughan, G. (2005). Social psychology (4th ed.). Prentice Hall. Hong, I. B., & Cha, H. S. (2013). The mediating role of consumer trust in an online merchant in predicting purchase intention. International Journal of Infor- mation Management, 33(6), 927–939. https://doi. org/10.1016/j.ijinfomgt.2013.08.007 Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Struc- tural equation modelling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53–60. Hsieh, J. Y ., & Liao, P. W. (2011). Antecedents and mod- erators of online shopping behavior in undergraduate students. Social Behavior and Personality: An In- ternational Journal, 39(9), 1271–1280. https://doi. org/10.2224/sbp.2011.39.9.1271 Hsu, C. L., Chang, K. C., & Chen, M. C. (2012). The im- pact of website quality on customer satisfaction and purchase intention: Perceived playfulness and per- ceived flow as mediators. Information Systems and e-Business Management, 10(4), 549–570. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1007/s10257-011-0181-5 Huang, W. Y ., Schrank, H., & Dubinsky, A. J. (2004). Effect of brand name on consumers’ risk perceptions of online shopping. Journal of Consumer Behaviour, 4(1), 40–50. https://doi.org/10.1002/cb.156 Jusoh, Z. M., & Ling, G. H. (2012). Factors influencing consumers’ attitude towards e-commerce purchases through online shopping. International Journal of Hu- manities and Social Science, 2(4), 223–230. Retrieved July 21, 2021, from https://pdfs.semanticscholar. org/327f/0ec65bd0e0dabad23c42514d0e2ac8b05a97. pdf Kenny, D. A. (2015, November 24). Measuring model fit. Retrieved June 23, 2016, from http://davidakenny.net/ cm/fit.htm Kiang, M. Y ., & Shang, K. H. (2015). Online purchase decision and its implication on e-tailing strategies. In New meanings for marketing in a new millennium (pp. 212–217). Springer. Küster, I., Vila, N., & Canales, P. (2016). How does the online service level influence consumers’ purchase in- tentions before a transaction? A formative approach. European Journal of Management and Business Eco- nomics, 25(3), 111–120. https://doi.org/10.1016/j.re- deen.2016.04.001 Kwek, C. L., Tan, H. P., & Lau, T. C. (2015). Investigating the shopping orientations on online purchase intention in the e-commerce environment: A Malaysian study. Journal of Internet Banking and Commerce. Retrieved July 21, 2021, from http://www.icommercecentral. com/open-access/investigating-the-shopping-orien- tations-on-online-purchase-intention-in-the-ecom- merce-environment-a-malaysian-study-1-21. php?aid=38386 Lai, J. Y . (2014). E-SERVCON and e-commerce success: Applying the DeLone & McLean model. Journal of Organizational and End User Computing, 26(3), 1–22. https://doi.org/10.4018/joeuc.2014070101 Lee, M. Y ., Jackson, V ., Miller-Spillman, K. A., & Ferrell, E. (2015). Female consumers’ intention to be involved in fair-trade product consumption in the US: The role of previous experience, product features, and per- ceived benefits. Journal of Retailing and Consumer Services, 23, 91–98. https://doi.org/10.1016/j.jretcon- ser.2014.12.001 Liao, C., Lin, H. N., Luo, M. M., & Chea, S. (2017). Factors influencing online shoppers’ repurchase in- tentions: The roles of satisfaction and regret. Infor- mation & Management, 54(5), 651–668. https://doi. org/10.1016/j.im.2016.12.005 Lim, Y . J., Osman, A., Salahuddin, S. N., Romle, A. R., & Abdullah, S. (2016). Factors influencing online shop- ping behavior: The mediating role of purchase inten- tion. Procedia Economics and Finance, 35, 401–410. https://doi.org/10.1016/S2212-5671(16)00050-2 Madahi, A., & Sukati, I. (2012). The effect of external fac- tors on purchase intention amongst young generation in Malaysia. International Business Research, 5(8), 153–159. https://doi.org/10.5539/ibr.v5n8p153 Oliveira-Castro, J. M. (2003). Effects of base price upon search behavior of consumers in a supermarket: An operant analysis. Journal of Economic Psychol- ogy, 24, 637–652. https://doi.org/10.1016/S0167- 4870(03)00006-0 Park, B. W., & Lee, K. C. (2011). Exploring the value of purchasing online game items. Computers in Human Behavior, 27(6), 2178–2185. https://doi.org/10.1016/j. chb.2011.06.013 Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technol- ogy acceptance model. International Journal of Elec- tronic Commerce, 7(3), 101–134. Pei, Z., Paswan, A., & Yan, R. (2014). E-tailer’s return pol- icy, consumer’s perception of return policy fairness and purchase intention. Journal of Retailing and Consum- er Services, 21(3), 249–257. https://doi.org/10.1016/j. jretconser.2014.01.004 351 Organizacija, V olume 58 Issue 4, November 2025 Research Papers Poddar, A., Donthu, N., & Wei, Y . (2009). Web site cus- tomer orientations, Web site quality, and purchase intentions: The role of Web site personality. Journal of Business Research, 62(4), 441–450. https://doi. org/10.1016/j.jbusres.2008.01.036 Prajogo, D. I., & McDermott, C. M. (2005). The relation- ship between total quality management practices and organizational culture. International Journal of Oper- ations & Production Management, 25(11), 1101–1122. https://doi.org/10.1108/01443570510626916 Salisbury, W. D., Pearson, R. A., Pearson, A. W., & Miller, D. W. (2001). Perceived security and World Wide Web purchase intention. Industrial Manage- ment & Data Systems, 101(4), 165–177. https://doi. org/10.1108/02635570110390071 Schiffman, L. G., & Kanuk, L. L. (2000). Consumer be- havior (7th ed.). Prentice Hall. Schiffman, L. G., & Kanuk, L. L. (2007). Consumer be- havior (9th ed.). Prentice Hall. Seock, Y . K., & Norton, M. (2007). Attitude toward In- ternet Web sites, online information search, and chan- nel choices for purchasing. Journal of Fashion Mar- keting and Management, 11(4), 571–586. https://doi. org/10.1108/13612020710824616 Shiau, W. L., & Luo, M. M. (2012). Factors affecting on- line group buying intention and satisfaction: A social exchange theory perspective. Computers in Human Behavior, 28(6), 2431–2444. https://doi.org/10.1016/j. chb.2012.07.030 Sin, S. S., Nor, K. M., & Al-Agaga, A. M. (2012). Factors affecting Malaysian young consumers’ online purchase intention in social media websites. Procedia – Social and Behavioral Sciences, 40, 326–333. https://doi. org/10.1016/j.sbspro.2012.03.195 Soopramanien, D. (2011). Conflicting attitudes and scepticism towards online shopping: The role of ex- perience. International Journal of Consumer Stud- ies, 35(3), 338–347. https://doi.org/10.1111/j.1470- 6431.2010.00945.x Wang, X., & Yang, Z. (2008). Does country-of-origin matter in the relationship between brand personal- ity and purchase intention in emerging economies? Evidence from China’s auto industry. Internation- al Marketing Review, 25(4), 458–474. https://doi. org/10.1108/02651330810887495 Wang, Y . H., & Tsai, C. F. (2014). The relationship be- tween brand image and purchase intention: Evidence from award-winning mutual funds. The Internation- al Journal of Business and Finance Research, 8(2), 27–40. Wells, J. D., Valacich, J. S., & Hess, T. J. (2011). What sig- nal are you sending? How website quality influences perceptions of product quality and purchase intentions. MIS Quarterly, 35(2), 373–396. Wu, L. Y ., Chen, K. Y ., Chen, P. Y ., & Cheng, S. L. (2014). Perceived value, transaction cost, and repurchase-in- tention in online shopping: A relational exchange per- spective. Journal of Business Research, 67(1), 2768– 2776. https://doi.org/10.1016/j.jbusres.2012.09.007 Wu, P. C., Yeh, G. Y . Y ., & Hsiao, C. R. (2011). The ef- fect of store image and service quality on brand image and purchase intention for private label brands. Aus- tralasian Marketing Journal, 19(1), 30–39. https://doi. org/10.1016/j.ausmj.2010.11.001 Zhu, Z., Wang, J., Wang, X., & Wan, X. (2016). Exploring factors of users’ peer-influence behavior in social me- dia on purchase intention: Evidence from QQ. Com- puters in Human Behavior, 63, 980–987. https://doi. org/10.1016/j.chb.2016.05.037 Zinkhan, G. M., & Braunsberger, K. (2004). The com- plexity of consumers’ cognitive structures and its relevance to consumer behavior. Journal of Busi- ness Research, 57, 575–582. https://doi.org/10.1016/ S0148-2963(02)00396-X Žnidaršič, J., Marič, M., & Ferjan, M. (2012). The effect of consumer eco-awareness on the use, the buying and the preference of eco-labeled food products. Advances in Business-Related Scientific Research Journal, 3(1), 91–103. Miha Marič, PhD, is a researcher in the field of leadership, management and organizational sciences. He holds a PhD from the Faculty of Economics at the University of Ljubljana. He is currently employed as an associate professor at the University of Maribor’s Faculty of Organizational Sciences. His research interests are power, leadership, organizational behaviour, human resource management, organization and management. He is the author of numerous original scientific articles, professional articles, papers presented at scientific conferences, scientific monographs, and an editorial board member, editor, and reviewer, as well as a programme committee member of several international conferences. He also participates in research projects and consulting work. Gašper Jordan, M.Sc., is an independent researcher who studied in the field of human resource management at the University of Maribor’s Faculty of Organizational Sciences. His main interests are human resource management, organizational behaviour and organizational psychology. Full Professor Robert Leskovar, PhD, obtained his PhD at the University of Maribor, where he is habilitated in the field of Quality and Information Systems. His research interests include multiple criteria decision making, modeling and simulation, digital marketing, 352 Organizacija, V olume 58 Issue 4, November 2025 Research Papers artificial intelligence, and software engineering. He has published more than forty original scientific articles and is the author or co-author of over twenty chapters in scientific monographs. He serves as the Head of the Department of Informatics at the Faculty of Organizational Sciences, University of Maribor, where he teaches courses at undergraduate, postgraduate, and doctoral levels. As a visiting professor, he has lectured at several international universities, including the Prague University of Economics and Business and RWTH Aachen University, Faculty of Business and Economics. Prof. Leskovar is a member of the Slovenian Society Informatika, the International Society on Multiple Criteria Decision Making, and the Association for Computing Machinery (ACM). In 2022, he was awarded the honorary title Legend of Computing and Informatics for his contributions to the development and promotion of these fields in Slovenia.