Behavioral intent to adopt mobile commerce in Kazakhstan: generation y perspective

Introduction

Mobile commerce (M-commerce) is any transaction, involving the transfer of ownership or rights to use goods and services, which is initiated and/or completed by using mobile access to computer-mediated networks with the help of an electronic device, such as a mobile phone, a PDA or a smart phone (Tiwari & Buse 2007). The Generation Y refers to a specific cohort of individuals born, roughly, between 1980-1994 who are now entering colleges and universities (McCrindle Research 2008). It is argued that there are generally higher usage and familiarity with communications, media, and digital technologies in the Generation Y than the previous Generation X (Junco & Mastrodicasa 2007). Kazakhstan, a central Asian nation with 16 millions people, has an Internet penetration rate of 34.3%, a mobile phone penetration rate of 100% (International Telecommunication Union 2009) and a population median age of 29.5 (CIA- The World Factbook 2013), representing an ideal environment to study M-commerce adoption among the Generation Y. Due to its inherent characteristics such as ubiquity, personalization and flexibility, M - commerce promises businesses unprecedented market potential (Siau & Shen 2003). However, there is only marginal use of mobile devices for transaction purposes in Kazakhstan (Halyk Bank Press Service 2009).

This study investigates:

i) The determinants of behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan.

ii) Whether gender has an impact on the determinants of behavioural intent to adopt M-commerce in Kazakhstan?

Conceptual model

According to the Diffusion of Innovation (DOI) theory (Rogers 1983), technological innovation is communicated through particular channels, among members of a social system over time, working through five stages: Knowledge, persuasion, decision, implementation and confirmation. There are five perceived characteristics of innovation that can be used to form a favourable/unfavourable attitude toward the innovation at the persuasion stage:

i) Compatibility: The degree to which an innovation is perceived as consistent with the existing values, past experiences, and the needs of potential adopters. Incompatibility with the values and norms of a social system leads to poor or slower adoption.

ii) Complexity: New ideas that are simpler to understand are adopted more rapidly than innovations that require the adopter to develop new skills and understandings.

iii) Observability: The degree to which the use and benefits of the innovation are visible to others, and therefore act as a further stimulus to uptake by others.

iv) Trialability: An innovation that is trialable represents less uncertainty to the individual who is considering it for adoption, who can learn by doing.

v) Relative advantage: The greater the perceived relative advantage of an innovation over the one it supersedes, the more rapid its rate of adoption will be.

Rogers’ DOI (1983) has been adopted and widely studied in mobile setting. For instance, Wu and Wang (2005) indicate that perceived relative advantage and compatibility influence favourable attitude towards M-commerce. Similarly, empirical study by Tanakinjal, Deans and Gray (2010) suggest that compatibility, complexity, trialability and relative advantage affect behavioral intention towards mobile marketing in Malaysia (observability was excluded). Recent case study by Borg and Persson (2010) supported the relevancy of all five perceived characteristics of innovation in Rogers’ DOI in forming favourable attitude towards mobile transaction in South Africa.

Correspondingly, the following hypotheses are proposed:

H₁: Compatibility determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan

H₂: Complexity determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan

H₃: Observability determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan

H₄: Trialability determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan

H₅: Relative advantage determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan

One limitation of the Rogers' DOI (1983) is that it does not identify social factors such as trust, that may influence users’ attitude towards new innovation. According to Siau and Chen (2003), trust is one of the major factors influencing peoples’ decisions to provide their personal data via an electronic medium. Studies by Joubert and Van Belle (2009) and Tanakinjal et al. (2010) indicate that trust influences intention to adopt M - commerce. In addition, perceived risk is a necessary antecedent for trust to be operative and an outcome of trust building is a reduction in the perceived risk of the transaction or relationship (Mitchell 1999). Perceived risk is an uncertainty regarding the possible negative consequences of using a product or service and is a combination of uncertainty with the possibility of serious of outcome (Bauer 1967). Wu and Wang (2005) and Tanakinjal et al. (2010) view perceived risk as influential determinant of behavioural intent to adopt M-commerce. Taking the perspective that trust is a willing dependency on another’s action and that the outcome of trust is an evaluation of the congruence between expectations of the trusted party and actions (Hupcey et al. 2001), this study proposed:

H₆: Trustworthiness determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan

H₇: Perceived risk determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan

Research methodology

Questionnaire and samplings

The pre-tested questionnaire using 7-point Likert scale comprised of measurement variables related to demographic, trustworthiness, perceived risk and Rogers’ (1983) five perceived characteristics of innovation. The measurement variables related to trustworthiness and perceived risks were adapted from Hupcey et al. (2001) and Bauer et al. (2005) respectively. A total of 670 questionnaires were randomly distributed in three private institutions of higher learning in two of the most populated cities in Kazakhstan. Out of the 345 questionnaires returned, 233 were from Almaty and 112 from Astana. The respondents were equally distributed across gender.

Statistical analysis

Analysis of the surveys consisted of four phases. In Phase 1, histograms of data distribution showed reasonable normal distributions (bell-shaped curve) for all measurement variables. Its descriptive statistics are shown in Table 1. In Phase 2, the multivariate analysis of variance showed no significant different in all measurement variables in term of gender (p>.05, two-tail test). Similarly, no major differences between Almaty and Astana were found with all measurement variables. In Phase 3, exploratory factor analysis (principal component analysis, varimax rotation) using Statistical Package for Social Science v. 14 extracted seven factors, representing 73.18% of the total variance explained. Variables in five extracted factors correspond to Rogers’ (1983) five perceived innovation characteristics. Its Kaiser-Meyer-Olkin (KMO) index value for sampling adequacy was 0.859, and the Bartlett’s Test of Sphericity was significant (Sig= 0.0), supporting the factorability of the dataset. The factor component scores for all seven factors are shown in Table 2. The Average Variance Extracted (AVE) for all seven factors was above the recommended threshold of 0.5 (Hair et al. 2010) for satisfactory convergent validity. All factors showed internal consistency with Composite Reliability (CR) >.7 as recommended by Hair et al. 2010).

In phase 4, Structural Equation Modeling (SEM) using AMOS 7.0 revealed sufficient evidence of goodness-of-fit between the measuring models and the sample data (factor validity test). Subsequent structural test produced good fit statistics (χ²/df =1.800, RMSEA=0.048, CFI=0.954, GFI=0.903), providing the basis for further hypothesis testing. The factor analysis component scores, reliability and SEM statistics are presented in Figure 1.

Table 1: Descriptive statistics of measurement variables

Table 2: Factor analysis component scores of measurement variables

Figure 1. SEM statistics of the research conceptual model

Findings and discussion

Hypothesis 1: Compatibility determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan generated the second highest regression weight of 0.76 amongst the various determinants. This result replicated Wu and Wang (2005) and Borg and Persson (2010) that compatibility affects behavioural intention to adopt M- commerce. Hypothesis 2: Complexity determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan generated a significant regression weight of 0.66. Respondents indicated that it would be easy for them to learn (mean=5.41) and adapt to M-commerce (mean=5.26) because of their previous experience with mobile phone usage (mean=5.43). These findings reflected the techno savvy characteristics of the Generation Y. There was moderate support for Hypothesis H₃: Observability determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan (regression weight=.22), possibly because M-commerce is currently in its infancy in Kazakhstan.

The hypothesis H₄: Trialability determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan was also supported (regression weight=.67) suggesting that behavioural intent to adopt M-commerce among this cohort in Kazakhstan is dependent on their pre-adoption ability to use it on a trial basis (mean=5.13), to test the suitability of mobile services (mean=5.20) and the possibility to use M-commerce on a trial basis long enough to see what it can do (mean=5.19). There was significant support for Hypothesis H₅: Relative advantage determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan (regression weight=.50), a finding consistent with that of Wu and Wang (2005).

Hypothesis H₆: Trustworthiness determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan, generated the third highest regression weight of .70. In particular, the variable ‘Provided that my permission is given, I consider mobile commerce as a trustworthy source of personalised marketing messages’ is the highest mean score in this study. Finally, H₇: Perceived risk determines the behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan generated the highest regression weight of .97 among the various determinants. There was evidence to suggest that perceived risk is inversely related to trialability (regression wt=-33) and relative advantage (regression wt=-24). However, gender has no significant affect on adoption intention. Possible explanations for this finding could be that gender equality, in terms of education exists in Kazakhstan (99.5% literacy rate, CIA FactBooks 2009) and that women play a central role in daily economic life of Kazakhs (Barfield 1993).

Marketing implications

The high penetration rate of mobile telecommunication and the strong empirical evidence in support of all hypotheses in this study suggested the viability of M-commerce in Kazakhstan. While this study showed moderate support for observability as determinant of behavioural intent towards M-commerce adoption, marketers need to recognise that adoption decisions are frequently influence by peers and existing fads or fashion amongst member of a social network (Abrahamson 1996). In this regard, mobile service providers need to enlist more retailers to support the use of mobile devices for customer transaction, so as to increase the observability and trialability of M-commerce.

As perceived risk was found to be the main determinant of behavioural intent to adopt M-commerce, a physical flagship store or distribution centre in Kazakhstan may allay customer fears of transacting virtually and to project observability. A short trial using mobile device for customer transaction may positively affect user’s attitude towards the practicality, convenience and perception of risk in M-commerce. Trustworthiness was also found to be significant determinant of behavioural intent to adopt M-commerce. This is especially so with regards to permission for personalised marketing messages, which was the highest scored variable in this study. One solution to address this concern is to employ permission based mobile marketing so that M-commerce users are in control of the types and volume of marketing messages they receive through their mobile phones. Getting consumers permission before sending them personalised marketing messages can reduce concerns about privacy violation, thus developing trust in the mobile service providers. Personalised marketing messages can be seen as a strategy to reduce clutter and improving targeting precision for marketers (Krishnamurthy 2001).

Conclusion and research limitations

This study showed that perceived risk and trustworthiness, in conjunction with Rogers’ (1983) five perceived characteristics of innovations are important determinants of behavioural intent to adopt M-commerce among the Generation Y in Kazakhstan. Gender has no significant affect on adoption intention. Implications for marketers and mobile service providers are discussed. In particular, the issue of risks, privacy violation and permission-based marketing in M-commerce are important factors to consider. The limitations of this study are that it has not yet explore other potential factors (technology enabler, network aggregator, content provider and wireless operators) that may determine behavioural intention towards the adoption of M-commerce. Overall, this study contributes to our understanding of mobile commerce adoption among the Generation Y in Kazakhstan.

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