Towards Affirmative Customer Recommendations in Mobile Commerce

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Journal of International Business Research and Marketing
Volume 3, Issue 4, May 2018, Pages 7-11


Towards Affirmative Customer Recommendations in Mobile Commerce

DOI: 10.18775/jibrm.1849-8558.2015.34.3001
URL: http://dx.doi.org/10.18775/jibrm.1849-8558.2015.34.3001

1 2 Hammad Mushtaq, 1 Mansoora Ahmed, 1 Muhammad Khalid Iqbal

1  School of Management, Wuhan University of Technology, Wuhan, P.R. China
2  School of Business & Economics, University of Management & Technology, Lahore, Pakistan

Abstract: Lately, the prevailing adoption of mobile commerce is evident throughout the developed economies. A similar trend is followed in developing countries; however, the acceptance of emerging mobile-based business ventures is still in its infancy. Establishing trust and affirmative word of mouth can contribute to effective placement of mobile commerce. The current study presents a literature review and research model to highlight the impact of social influence and customer attitude towards personalized communications on building trust perceptions and customer recommendations in mobile commerce. Mobile commerce vendors invest in pursuing customers through interactive and integrative marketing communications. Prospect customers develop trust perceptions concerning mobile commerce vendor over these communications. Customers perceived social influence has an important role in technology adoption structure of “Unified Theory of Acceptance and Usage of Technology” (UTAUT) by Venkatesh. This paper presents a hypothesized model based on the theoretical background of integrated marketing and technology adoption theories. The research design and methodology has been articulated to test proposed research model empirically.

Keywords: Mobile commerce trust, Online loyalty, Integrated marketing, Online behavioral advertising

Towards Affirmative Customer Recommendations in Mobile Commerce

1. Introduction

Recently mobile usage has escalated with tremendous pace as it carries convenient features such as ubiquitous connectivity, entertainment, and socialization to name a few. Likewise, the advent of mobile commerce platform has affected the customer expectations as now they have a liberty to access most recent information vital to finalize their buying decision. Product or service recommendation of existing consumers provides a decision cursor to prospect customers. The customer’s spawned positive recommendation is fundamental to the emphasis on the success of online vendors, as it shows their credibility that is built after a positive interaction. On the contrary, Wakefield (2018) stated that since customer tends to share their emotions on social media, disconfirmation of their expectation might result in negative recommendations. Online buyers who are interacting for the first time with mobile commerce vendor may perceive these encouraging recommendations as an indication to form a favorable image about the vendor.

Customers may express their positive recommendations or word of mouth (WOM) once they receive better service quality than their initial expectations. Lee (2005) highlighted the importance of interactivity in building customer preference and likeliness to perform transactions in mobile commerce. Batra & Keller (2016) indicated integrated marketing communications research primacies such as the development of enhanced models and frameworks that are critical to comprehend and assimilate the customer touch-points for a brand to engender buying intentions and enduring loyalty. Integrated marketing is a reputed way of building an online brand reputation (Gensler et al., 2013) and recognition with its features like personalized advertisement on the digital platforms that are frequently visited by both existing and potential customers. According to (eMarketer, 2017) “Programmatic ad spending in China totaled $16.69 billion in 2017, a 48.6% increase over 2016”. A similar situation is expected in Pakistan, according to the statistics provided by (“www.statista.com/statistics”) the number of smart cellphone users was projected at 2.32 billion by the end of the year 2017, and mobile internet usage is expected to increase due to the declining costs of mobile internet. The trend indicates that vendors are capturing significant support from online advertising in generating economic gains.

Though significant research has been carried out in online trust and online purchase intentions in World Wide Web context, however, there is still room for extending the generalizable antecedents of online trust especially in the context of mobile commerce. Giovannini et al. (2015) theorized mobile commerce trust as “one’s willingness to accept vulnerability while interacting with another through a mobile device given extant expectations regarding intentions and behavior of the other party.” There is a gap in the empirical research regarding how trust engender favorable product or service recommendations especially in emerging markets like Pakistan (Ur Rehman et al., 2011). Although customer attitude towards online-personalized advertisement is reflected in much interactive marketing related research (Bell & Buchner, 2018) at the same time there is limited background found in our investigation regarding customer attitude towards Online Behavioral Advertisement (OBA) as an antecedent of mobile trust. According to Venkatesh & Davis (2000), social impact has developed through a system, compliance, internalization, and identification. It would be interesting especially in mobile commerce context where most internet users have the presence on social media networks where they are followed up by retailers with personalized advertising. This research focus on explaining mobile commerce trust driven by positive social image and positive approach to behaviorally targeted communication has a positive impact on customer recommendations. Furthermore, the role of social influence and attitude towards online behavioral advertising in engendering mobile trust is researched using the empirical evidence from emerging mobile commerce market.

2. State of the Art Literature

The following sections present a literature review to form the theoretical background of the proposed model.

2.1 Customer Recommendations & Mobile Commerce Trust

Positive recommendations either in case of verbal or explicitly shared on the electronic platform such as social media network referred to as the electronic word of mouth eWOM (Wakefield & Wakefield, 2018). eWOM is an essential asset for any vendor operating in the online environment. Schuckert et al. (2015) research on customer reviews supported that explicit customer recommendations in the form of online reviews depict the satisfaction level between customers and service providers. They further argue that these reviews also provide valuable evidence to support prospective consumers in making their choice to do business with the vendor. Gefen (2002) posit that customer loyalty in e-commerce is not only limited to repeat purchase but also encompasses the likeliness to recommend the vendor in the social circle usually social media platform. Similarly, Amin et al. (2014) confirmed a significant role of trust in engendering the customer loyalty.

Despite risks involved in online transactions, it is being extensively accepted in major economies. The customer’s positive experience with online shopping develops trust. While the customer trust is an issue since the relationship commitment theory (Morgan & Hunt, 1994) was proposed, trust is taken as a foremost driver to engender relationship along with commitment, customer dependence and relational norms (Zang et al., 2016).

2.2 Social Influence

Cialdini (2004) pointed out the social norms’ significance in shaping range of individual behaviors. The social norms always influence us to gain a precise understanding to effectually respond to the social situations, mostly through times of uncertainty. According to Tago et al. (2017), Social Influence (SI) denotes the magnitude to which a person recognizes that it is central what others have confidence in that he or she should adopt the system. SI is part of UTAUT by Venkatesh et al. (2003), which states “SI as the degree to which an individual perceives how important others believe are that he or she should use the new system” (Venkatesh et al., 2003). Wang (2016) found SI as an important determinant for extended use of mobile social networking applications. Mobile technologies are an essential part of Mobile commerce, comprising mobile internet, mobile payment system, mobile shopping applications and many more. SI role in the adoption of such technologies has been frequently researched. However, its role in both building customer trust and recommendations may bring essential implications for both practitioners and researchers.

2.3 Attitude towards OBA

Hewett et al. (2016) suggest that with the advancement in online technologies, the nature of brand communication has been modulated as the impact on consumer sentiment and business sales. Interactive marketing is now a trusted way of building an online brand reputation and recognition (Lee, 2015) with its features like personalized advertisement on the digital platforms frequently visited by both existing and potential customers. Mobile marketing is emergent phenomena in the retail environment (Gao et al., 2013), hence businesses are increasingly depending on the mobile phone based communications to share promotional information with their targeted customers (Shankar et al., 2010). OBA is a distinctive practice of directed advertising (Smit et al., 2014), which focus on guiding both prospect and frequent customers. Inquiry on trust in various advertising media settings has been established (Soh et al., 2009) that regardless of their intrinsic suspicion and incongruity concerning promotion communications, broadly the customer tend to appreciate advertisements in comparison to the level of disinclination. They treasure advertisements to be attractive, enlightening and valued in their exploration practice (Soh et al., 2009). OBA is believed to be an integral part in future publicizing campaigns. The ability to attain accurate targeting through OBA is an attractive feature for advertisers (Batra & Keller, 2016). Prominent researchers contend that future of advertising is in precisely directed publicity that will have more personalized communication; thus advertising will become more personalized and targeted and will involve more individualized interaction. Moreover, advertisers can reprise communications based on consumer demeanor and requirements.

2.4 Research Gap

Litterio et al. (2017) highlighted that relationship between advertising and WOM or customer recommendations with its influence on sales has not yet been widely investigated. Recently Kim and Peterson (2017) highlighted that research on “online trust” originators depict opposing outcomes that provide solid implications for impending research in technology-driven interactions such as mobile commerce. Although customer attitude towards online personalized advertisement is reflected in many interactive marketing related studies at the same time, there are limited studies found in our search about customer attitude towards Online Behavioral Advertisement (OBA) as an antecedent of mobile trust. Thus, it is important to explain what leads to positive word of mouth and its explicit sharing by a customer in mobile commerce applications context. This study proposes that once the trust is developed with a vendor the customer express this trust by sharing their reviews so it can benefit other customers. In continuation to this, since customers themselves find the reviews helpful in making their decision to purchase so they will voluntarily share their experience with prospect customers provided they believe in social influence and positive attitude towards behavioral advertisements.

3. Research Design

The research model presented in figure 1 depicts that both social influence and attitude towards OBA has a significant and a positive impact on mobile commerce trust. Following sections discuss the hypothesis development and research design.

3.1 Research Conceptual Model

SI has an important role in technology adoption (Venkatesh et al., 2003) as users willingly assume the positive perceptions of peers in their own decision to embrace technology. In mobile commerce context, it is proposed that SI will engender credibility about the mobile commerce vendor as customers perceive that mobile commerce application based purchase is a social norm. Positive social image of mobile commerce can foster influence on perceived trustworthiness. Extending on this context, current study presents the following hypothesis:

Hypothesis 1. Social influence has a positive significant impact on mobile commerce trust.

Recent literature (Giovannini et al., 2015; Kim & Peterson, 2017) indicates that mobile trust has been studied in many dimensions but it has been the subject of little systematic study in advertising. The integrated marketing offers online vendors to make personalized communication and behaviorally targeted advertisements to form their brand image. At the same time, behaviorally targeted online advertising can postulate a positive image and credibility in its audience. Soh et al. (2009) found that customer trust has a relationship with advertising and proposed AdTrust scale that needs further investigation. Based on this background it is hypothesized that:

Hypothesis 2. Attitude towards online behavior advertising has a positive significant effect on mobile commerce trust.

Mobile commerce trust has been researched with many aspects and it has been linked (Lee, 2005; Giovannini et al, 2015) with purchase intentions and customer loyalty. As customer trust develops in the mobile commerce, it will be reflected in terms of positive WOM. Therefore, it is hypothesized that:

Hypothesis 3. Mobile Commerce trust has a positive impact on product/service recommendations.

The perceived social influence occurrence is linked to trust at the same time it is related to customer recommendation and loyalty. Similarly, positive attitude towards personalized communication and advertisement can increase the odds that the technology vendor gets positive recommendations. In our research, we propose that mobile commerce trust mediate these relationships to strengthen it further. This leads to the following hypothesis:

Hypothesis 4. Mobile commerce trust mediates the relationship between social influence and product/service recommendations.

Hypothesis 5. Mobile commerce trust mediates the relationship between attitude towards online behavior advertising and product/service recommendations.

Figure 1: Research Model

3.2 Research Scale

A survey shall be conducted with more than 250 mobile commerce users to collect data. Preferably, respondents will be the ones who are interested in buying technology-based products such as wireless headphones and mobile accessories, using mobile commerce. Data collection will be done using both hard form questionnaires where the researcher will personally collect the responses, and online survey tool shall be administered for convenience in reaching the preferred respondents. The survey questionnaire will use 5 points Likert scale to get responses from strongly disagree to strongly agree, relating each construct of the model. SI scale is adapted from Thakur (2013) as they used it in the mobile service context, i.e., four items. The scale will be adjusted according to mobile commerce use context for example, “People who influence my behavior think that I should use mobile shopping application for online buying.” Attitude towards OBA scale is adapted from Smit et al. (2014), i.e., six items by adjusting it in current study context for example “I prefer that mobile applications show ads that are targeted to my interests”. Likewise, five items scale for mobile commerce trust and 3 item scale for recommendation loyalty is adapted from Amin et al. (2014).

3.3 Research Methodology

After evidence collection and initial data screening, the empirical data analysis will be performed. For this research, to confirm the reliability of the instrument, Cronbach’s alpha coefficient technique will be used. This technique is a standard followed in academic studies and is simpler to use. The minimum acceptable boundary of Cronbach’s alpha coefficient is 0.6. The higher alpha values depict the strong correlation between the construct and its items used in the questionnaire. Further SPSS 23 and Smart PLS will be used as the tool for statistical analysis. The frequencies of demographic questions would be generated in SPSS. Moreover, structure equation modeling (SEM) technique will be used to perform the data analysis and do hypotheses testing because SEM can analyze two independent variables at the same time. Exploratory factor analysis will be conducted to minimize the number of factors. Then, “Cronbach’s alpha and confirmatory factor analysis” (CFA) (Brown & Moore, 2012) will be conducted to confirm the “validity and reliability” (Drost, 2011) of the factors extracted from EFA. After CFA, structure model is constructed in Smart PLS and model will be finalized for the given data.

4. Implications and Conclusion

Individuals trust in unbiased online reviews and opinions because they need knowledge of products and services to pass through the purchase funnel process. This implicates that firms should focus on developing the mechanisms that will facilitate voluntary positive recommendations. Customers should be allowed to share their reviews with their favorite social networks and thus can generate a more positive image of the brand. Mobile shopping applications should provide dedicated and mobile user-friendly interface that may get strengthened by the extensive interactivity based features such as the ability to share reviews on social media networks. Mobile commerce vendors need to be very active in their responsiveness to customer reviews and can make reward strategies for the customer who give reviews and share it on social media networks. This study contributes to the online consumer behavior knowledge stream in digital marketing era. The paper presents literature based theoretical investigation, research model, and hypotheses. Further research should be carried out to investigate the research model empirically.

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