E-retailing: Determining online shopping motivations

Transcription

1 E-retailing: Determining online shopping motivations MATEA MATIC AND KATIJA VOJVODIC Department of Economics and Business Economics University of Dubrovnik Lapadska obala 7, Dubrovnik CROATIA Abstract With the increasing importance of online shopping, understanding customers online purchasing behaviour and the motives affecting consumers online intentions has become an important issue for academics and practitioners. In that context, the main research objective of this paper is to explore the motives that determine online intentions and online. The data was collected through survey questionnaire using the purposive sample of 193 Croatian students from the University of Dubrovnik. Research results indicate that the main motives for Internet use amongst the students are convenience and recreational. The statistical analysis shows that there is a significant relation between the convenience and recreational motives and the online intention. Additionally, results indicate that there is a significant difference between the recreational motive and. Keywords: e-retailing, online intention, online, online shopping motivations, convenience, recreational motives 1 Introduction Nowadays, the Internet, as a retail format, represents a form of in-home shopping and the major non-store channel [18]. Furthermore, Wang and Head [33] emphasize that the Web is increasingly being viewed as a tool and place to enhance customer relationship. Given the ever-increasing growth in electronic retailing, a better understanding of online purchasing behaviour and factors affecting online consumer intentions has become an important issue for online retailers in order to maintain their customers. In that sense, to be successful, e-retailers must understand the factors that motivate consumers online shopping. The development of electronic commerce, in particular electronic retailing, sets numerous challenges for both retailers and consumers. Moreover, changing conditions in people s personal environment has contributed to a profound change in consumer behaviour [28]. In that context, Mathwick et al. [21] emphasize that retailing in the 21 st century means doing business with customers on their terms. Shoppers are very often of the touch-and-feel type [19], i.e., they prefer to touch and feel goods before deciding to buy. According to Weitz [35], electronic retailers are using technology to convert touch-and-feel attributes into look-and-see attributes. Generally, it is often stated that Internet consumers are principally motivated by convenience. Rohm and Swaminathan [25] argue that convenience is probably the most important perceived benefit from shopping on the Internet and a key factor influencing the growth of online shopping. It is mainly related to saving time and effort and flexibility in the timing for shopping. In addition, several types of convenience can be distinguished. These primarily include access convenience, search convenience, possession convenience, transaction convenience, time convenience and place convenience [16]. Similarly, in examining the key convenience dimensions of online shopping, Jiang et al. [13] identify the five dimensions of online shopping convenience, i.e., access, search, evaluation, transaction, and possession/post convenience. Attitude is often seen as an antecedent variable, and motivation variables have a significant causal relationship with information search variables [32]. Shim et al. [29] emphasize that consumers attitudes towards online shopping affect consumers intention to use the Internet for information search and, consequently, influence their intention to use the Internet for purchasing. Van der Heijden et al. [31] examine online purchasing intention using two different perspectives: a technology-oriented perspective and a trust-oriented perspective. They assert that the trust-antecedent perceived risk and the technology-antecedent perceived ease-of-use directly influence the attitude towards purchasing online. In that sense, Salo and Karjaluoto [26] ISBN:

2 outline that the actual outcome of trust-enhancing methods in online environments should be the development of long-term trusted customer relationships. In the context of online shopping, the findings suggest that attitudes toward online shopping and online shopping intentions are not only affected by ease of use, usefulness, and enjoyment, but also by exogenous factors like consumer traits, situational factors, product characteristics, previous online shopping experiences, and trust in online shopping [23]. The main research objective of this paper is to explore the motives that determine online intentions and online. To achieve the research objectives, the paper is divided into five sections. Following the introduction, the second sections provides the selected literature review on online purchasing behaviour, with the special emphasis on consumers online intensions as well as online. Data and research methodology are discussed in the third section, while the fourth presents research results and the discussion of findings. The final section of the papers draws certain conclusions and directions for future research. 2 Literature review Examining online shopping motivations is a wide, complex area covering a number of different elements. The same applies to online intentions and online s. As regards motivations, online shopping was found to be predicted by information motivations, interactive control motivations, and socialization motivations [14]. It is often stated that the motivations that lead a potential e-customer to make a are not the same as those that influence an experienced customer [11]. On the other hand, factors that influence intentions include product type, prior, and, to a lesser extent, gender [5]. Moreover, impulse orientation, prior online experience and online trust have significant impact on the customer intention [30]. Similarly, Jayawardhena et al. [12] assert that prior and gender have a significant effect on intention. As regards the relationship between past online s and purchasing intentions, the results show that past purchasing predicts intentions to [34]. Kau et al. [15] outline that in spite of the convenience of Internet shopping, consumers are still wary of shopping online. Consequently, online initial trust and familiarity with online purchasing have a positive impact on intention [6]. With regard to the effects of trust beliefs on consumers online intentions, the results suggest that brand trust beliefs affect online intentions [3]. Similarly, the online intention is influenced by the construct of playfulness, followed by the perceived security and usefulness [4]. Security and privacy issues are of the utmost importance in electronic retailing. These mainly refer to the risks associated with online retailing, such as revealing personal information, the possibility of credit card fraud and the inability to touch the products before purchasing. In that sense, online trust can discourage online consumers from online retailing and can influence intentions. Risk perceptions regarding Internet privacy and security have been identified as issues for both new and experienced users of Internet technology [22]. In that context, privacy concern has a negative influence on online spending, i.e., people who are concerned about privacy spend less on online s [1]. Additionally, Schlosser et al. [27] emphasize the role of trust in predicting online intentions. Similarly, Mukherjee and Nath [24] argue that trust and commitment are the central tenets in building successful long-term relationships in the online retailing context and that behavioural intentions of customers are consequences of both trust and commitment. Another important issue refers to understanding customers re intentions in online shopping. In that context, the findings reveal that trust, perceived ease of use, perceived usefulness and enjoyment are significant positive predictors of customers' re intentions [7]. As regards the effects of consumers' perception of online retailers ethical behaviour on consumer and revisit intentions it was found that perceived ethics of an Internet retailer s website significantly affected consumers' trust and attitudes to the retailer s website that eventually had positive impacts on and revisit intentions [20]. Furthermore, to enhance customer intentions, online stores should develop marketing strategies to better address the trustworthiness, reliability, and responsiveness of web-based services [17]. In regard to online environment, it is important to take into consideration some online environment cues affecting customer intention, such as web site quality and web site brand. In that context, the study by Harris and Goode [9] revealed that consumers interpretations of online environments exerted a powerful influence over trust and intentions. The findings confirmed that web-site quality and web site brand affected consumers trust ISBN:

3 and perceived risk, and in turn, consumer intention. Furthermore, it was also revealed that the web-site brand was a more important cue than web site quality in influencing customers intention. Similarly, evidences suggested that website trust, web-site satisfaction, web-site awareness, and overall customer satisfaction all influenced the online intention [36] [2]. In examining roles of e-shopping quality and experiential e- shopping motives it was found that web site content/functionality and atmospheric/experiential quality had significant impact on e-shopping satisfaction contributing to e-shopping intention, while privacy/security and customer service had significant impact on e-shopping intention but not on e-shopping satisfaction [8]. Another important element with regard to consumer online intention is related to web interface feature [10]. To conclude, the review of the examined literature emphasizes that the convenience of online shopping, security and privacy, and, consequently, purchasing intentions play an important role in preference for online purchasing. 3 Research methodology The analysis of the relationship between the motives of Internet usage and online intention is undertaken on the dataset collected in February and March 2013 through a survey questionnaire of 193 students from the University of Dubrovnik. The survey questionnaire consisted of 11 statements and questions. In addition, the questionnaire included demographic characteristics such as gender and monthly income of the students surveyed. Surveys were completed anonymously during regular class time, with a response rate of a 100 percent. The motives for using the Internet as well as the intention were measured with an ordinal measurement scale taking degrees from 1 to 5, where respondents expressed their degree of agreement or disagreement with the statement (1=completely disagree, 5=strongly agree). Also, the was measured with the ordinal scale taking statements from 1 to 4 (1-do not, 2 once a year, 3- several times a year (2 to 8 times), 4- several times a year (more then 8 times). The collected data was analysed using statistical software package SPSS 20. Moreover, various multivariate statistical analyses were used, such as factor analysis and one-way analysis of variance (ANOVA). Amongst the diagnostics of analysis, results of Cronbach's alpha coefficient (0.806) indicated high internal reliability of the measurement scale of the Internet use motives. The following table shows the results of the statistical tests KMO and Bartlett test of sphericity. Table 1 Kaiser-Meyer-Olkin test and Bartlett test of sphericity Kaiser-Meyer-Olkin test,806 Bartlett test of sphericity Chi-Square 734,547 df 36 Sig.,000 Results from the Table 1 show that Kaiser-Meyer- Olkin test for the proportion of variance in the variables caused by the underlying factors takes value of indicating that the sample is adequate. The significance of Bartlett s test of sphericity (α 0.05) suggests that the correlation matrix is not a unit matrix and that the sample is suitable for further factor analysis. 4 Results Principles component factor analysis with varimax rotation method and Kaiser-Guttman criteria was performed on the data. According to the Kaiser- Guttman criteria the model can include only the factors with eigenvalues above 1. Table 2 shows the number of the extracted factors from scale of motives for the Internet use. Table 2 Total Variance Explained Rotation Sums of Squared Loadings Item Total % of Variance Cumulative % 1 3,653 40,587 40, ,108 23,418 64,005 Extraction Method: Principal Component Analysis Two components or variables with eigenvalues above 1 were extracted explaining 40.58% and 23.41% of the total variance respectively. Factor loadings of rotated component matrix vary between and indicating significant loading of the manifest variables in explaining the extracted factors. The purpose of factor analysis is achieved and the interpretation of factors is satisfactory. Therefore, the results of factor analysis scale of the Internet use motives indicate excellent properties of convergent and discriminant validity. As per the content of the statements, it can be concluded that the Factor 1 is related to the convenience features of the respondents and it is named the motive of convenience, whilst the Factor ISBN:

4 2 is related to entertainment and it is, therefore, named the recreational motive. In continuation of analysis, the relationship between these two factors (variables) and the statements of consumers is investigated. In further analysis ANOVA between convenience and recreational motives and the online intention and was used. Table 3 shows the results of ANOVA between the motive of convenience and the online intention and. Table 3 Analysis of variance of the motive of convenience and the online intention and intention Sum of squares df Mean square F Sig. 12, ,001 3,134,016 5, ,733 1,754,158 Since the variation within groups is confirmed, it can be assumed that that convenience motive significantly influences the tendency of online intention. The results from Table 3 show that there is a significant difference in relation between the motive of convenience and the online intentions (p<0.05, F=3.134). The results of the variance show that the online intention has a tendency to increase if online respondents use Internet for a convenience motive. Therefore, these online users will be more inclined and have a positive intention toward online in the future. Contrary to this, results of the variance show that there is no significant difference between the motive of convenience and online (p>0.05, F=1.754). It can be concluded that these motives will not influence the of online, nor the determination on the frequency of online. The next table shows the results of ANOVA between the recreational motive and the online intention and. Since the variation within groups is confirmed, it can be assumed that that recreational motive significantly influences the tendency of online intention and. The results from the Table 4 indicate that there are significant differences between the recreational motive and the online intention (p<0.05, F=9.992). It is evident that the intention will increase in relation to the recreational motive. Therefore, respondents who use the Internet recreationally will be more inclined to products online. These respondents consider the Internet as a fun and notable place for purchasing online in the future. On the contrary, the respondents who are less inclined to use the Internet for recreational motive will not feature intention for online purchasing. Table 4 Analysis of variance of the recreational motive and the online intention and intention Sum of squares df Mean square F Sig. 26, ,767 9,992,000 33, ,376 9,926,000 Unlike the results for the motive of convenience, the results for the recreational motive indicate statistically significant differences between the recreational motive and online (p<0.05, F=9.926). Furthermore, the results show that there are differences between the recreational motive and frequency of online. The respondents who use Internet for fun often online several times a year, i.e., 2 to 8 times a year. If the recreational motive for using the Internet increases, respondents will probably select the Internet as the main place of. Therefore, for respondents who use the Internet for fun, the Internet provides a useful place to get necessary information about the desired product and also the possibility of buying the same product. 5 Conclusions This research contributes to the literature by identifying university students online shopping motivations. Moreover, the research provides insights into university students intentions to products at online stores, their online and the frequency of online purchasing. The statistical analysis shows that there is a significant relation between convenience and recreational motives and the online intention. In addition, the results indicate that there is a significant difference between the recreational motive and. Furthermore, the analysis shows that the respondents who use the ISBN:

5 Internet for convenience motives will be more inclined toward online in the future. Respondents who use Internet only for fun and entertainment motives will be online purchasing in the future. Also, these respondents using the Internet only for entertainment will buy products online 2 or 8 times a year. It can be concluded that the convenience and recreational motives will influence online intention in the future. Also, contrary to the convenience motive, the recreational motive will influence online as well as the frequency of online purchasing. However, the findings should be considered in the light of their limitations. First of all, it refers to a sample selection, i.e. university students. This limited sampling frame may affect the generalization of findings. Future research sample should be extended by including other population groups. It would also be interesting to include demographic data, i.e. the socio-economic characteristics of customers. For example, gender differences could be identified in terms of online behaviour. Moreover, other factors should also be included in the future research, primarily age, education, and household income. However, factors such as product type, prior, website design, or web-site satisfaction could be taken into consideration. Given that the university students represent a group of young adult online shoppers, the research findings may be useful to web-retailers, marketing managers and internet marketers to recognize and understand the motives affecting their online purchasing behaviour in order to target better their consumers. References: [1] Akhter, S. H., Who spends more online? The influence of time, usage variety, and privacy concern on online spending, Journal of Retailing and Consumer Services, Vol. 19, No. 1, 2012, pp [2] Bai, B., Law, R., Wen, I., The impact of website quality on customer satisfaction and intentions: Evidence from Chinese online visitors, International Journal of Hospitality Management, Vol. 27, No. 3, 2008, pp [3] Becerra, E. P., Korgaonkar, P. K., Effects of trust beliefs on consumers' online intentions, European Journal of Marketing, Vol. 45, No. 6, 2011, pp [4] Bonera, M., The propensity of e-commerce usage: the influencing variables, Management Research Review, Vol. 34, No. 7, 2011, pp [5] Brown, M., Pope, N., Voges, K., Buying or browsing? An exploration of shopping orientations and online intention, European Journal of Marketing, Vol. 37, No. 11/12, 2003, pp [6] Chen, Y.-H., Barnes, S., Initial trust and online buyer behaviour, Industrial Management & Data Systems, Vol. 107, No. 1, 2007, pp [7] Chiu, C.-M., Chang, C.-C., Cheng, H.-L., Fang, Y.-H., Determinants of customer re intention in online shopping, Information Review, Vol. 33, No. 4, 2009, pp [8] Ha, S., Stoel, L., apparel retailing: roles of e-shopping quality and experiential e-shopping motives, Journal of Service Management, Vol. 23, No. 2, 2012, pp [9] Harris, L. C., Goode, M. M. H., servicescapes, trust, and intentions", Journal of Services Marketing, Vol. 24, No. 3, 2010, pp [10] Hausman, A. V., Siekpe, J. S., The effect of web interface features on consumer online intentions, Journal of Business Research, Vol. 62, Is. 1, 2009, pp [11] Hernandez, B., Jimenez, J., Martín, M. J., Adoption vs. acceptance of e- commerce: two different s, European Journal of Marketing, Vol. 43, No. 9/10, 2009, pp [12] Jayawardhena, C., Wright, L. T., Dennis, C., Consumers online: intentions, orientations and segmentation, International Journal of Retail and Distribution Management, Vol. 35, No. 6, 2007, pp [13] Jiang, L. A., Yang, Z., Jun, M., Measuring consumer perceptions of online shopping convenience, Journal of Service Management, Vol. 24, No. 2, 2013, pp [14] Joines, J. L., Scherer, C. W., Scheufele, D. A., Exploring motivations for consumer Web use and their implications for e-commerce, Journal of Consumer Marketing, Vol. 20, No. 2, 2003, pp [15] Kau, A. K., Tang, Y. E., Ghose, S., Typology of online shoppers, Journal of ISBN:

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