Extending unified theory of acceptance and use of technology with perceived monetary value for smartphone adoption at the bottom of the pyramid
Introduction
Researchers have recommended the use of Information and Communication Technology (ICT) for the socio-economic development of the low-income people termed as “Bottom of the Pyramid” (BOP) (Berger & Nakata, 2013). The socio-economic conditions of these low-income people are different from other people (Prahlad, 2004). They are characterized by low literacy, poor health condition, limited access to the media, strive to meet basic needs, and geographical isolation (Prahlad, 2004). They are socially isolated from other segments which induce them to increase the consumption of aspirational products in order to reduce the feeling of isolation (Alwitt, 1995; Hill & Stephens, 1997). A smartphone can act as an aspirational product in the BOP segment due to its growing popularity (Meeker, 2015) and reduced price. Adoption of smartphones and Internet may encourage the BOP people to embrace online systems (Veeramootoo, Nunkoo, & Dwivedi, 2018). In order to increase the usage of smartphones at the BOP, one needs to understand the underlying factors influencing technology adoption in this segment.
The research in the domain of technology adoption has evolved over several years (Williams, Dwivedi, Lal, & Schwarz, 2009), and this evolvement can be attributed to the increasing dependencies of human lives on technology (Koul & Eydgahi, 2017; Kulviwat, Bruner, & Al-Shuridah, 2009). In a review based study, Korpelainen (2011) found that Diffusion of Innovation (DOI) (Rogers, 1962), Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975), Theory of Planned Behavior (TPB) (Ajzen, 1991), Technology Acceptance Model (TAM) (Davis, 1986; Davis, Bagozzi, & Warshaw, 1989; Venkatesh & Davis, 1996), and Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003) are the most widely cited theories in the domain of technology adoption. Williams, Rana, and Dwivedi, (2015) mentioned that the UTAUT had harmonized the literature related to technology adoption. The UTAUT is developed based on eight popular theoretical frameworks. Though some researchers have criticized UTAUT stating that it has dropped some potential causal relationships (Dwivedi, Rana, Jeyaraj, Clement, & Williams, 2017), a synthesis of UTAUT based research from 2003 to 2014 revealed that the model had been empirically tested in multiple settings (Venkatesh, Thong, & Xu, 2016).
Technology adoption is contextual (Jelinek, Ahearne, Mathieu, & Schillewaert, 2006; Kimberly & Evanisko, 1981) and research related to technology adoption in the context of the BOP people is limited. When this fact is coupled with the potentiality of smartphone penetration at the BOP, it becomes interesting to explore the factors influencing the adoption of smartphones in the BOP segment. Therefore, this study proposes an extended UTAUT based research framework and performs an empirical analysis of the framework for understanding smartphone adoption at the BOP.
This research will enhance the current state of knowledge in the domain of technology adoption by exploring and discussing the adoption of smartphones in the BOP segment in India. The main contributions of this study are twofold. First, we have investigated the effects of the determinants of smartphone adoption at the BOP as suggested in the original UTAUT (Venkatesh et al., 2003) to test the model for different technologies in the context of different types of user groups. Results from such studies help in enhancing the overall generalizability and understanding of the model. Second, we have incorporated a context-specific construct in our proposed research model in order to enrich the understanding of smartphone adoption at the BOP. This is important because the socio-economic conditions of the BOP people are vastly different from other segments of people (Prahlad, 2004). Moreover, the context-specific outcome of our empirical analysis in terms of the moderating effects of gender, age, and experience in smartphone adoption will enhance the current state of knowledge. Literature in the domain of technology adoption in the context of the BOP people are limited (Hasan, Lowe, & Petrovici, 2017) and therefore, the current study will enrich the literature of technology adoption by providing insights on smartphone adoption at the BOP.
Section snippets
Literature review
The literature review of this study can be separated into two parts. The first part will explore the literature associated with the characteristics of BOP, and the second part will review the literature related to smartphone adoption.
Research model
The research model of this study is based on UTAUT. According to UTAUT, “Performance Expectancy” (PE), “Effort Expectancy” (EE), and “Social Influence” (SI) have direct impacts on the “Behavioral Intention” (BI). UTAUT also posits that “Use Behavior” (UB) is determined by “Facilitating Conditions” and “Behavioral Intention”. UTAUT recognized moderating roles of four variables, namely age, gender, experience, and voluntariness of use. The original UTAUT framework is displayed in Fig. 1.
Since the
Data and sample
The current study is specific to the BOP segment. While selecting the respondents, the criterion for defining BOP is set at a monthly household income of Indian Rupees 13,152 or less. In order to ensure that a respondent belongs to BOP category, monthly household income is confirmed to be below INR 13,152 before filling the questionnaire. The respondents were given the questionnaire sheet to fill up in the presence of a trained data collector. Data points were collected both at home and
Empirical analysis
The empirical analysis of this study can be performed in two phases: Measurement Model and Structural Model. The fundamental assumptions of normality, collinearity, outlier analysis, and common method variance (CMV) are found to be satisfied in our dataset. For normality, we have checked the univariate skewness and univariate kurtosis of the observed variables. The maximum univariate skewness observed in the dataset is -0.462, and the maximum univariate kurtosis observed is 2.739. Hancock and
Discussion
The current study has proposed an extended UTAUT based model with constructs suitable for the BOP segment and verifies the model in the context of smartphone adoption at the BOP. Table 8 lists the hypotheses and outcomes of the study.
The empirical results show that “Perceived Monetary Value” is a significant predictor of smartphone adoption at the BOP. The results also confirm that all the primary hypotheses proposed under UTAUT stand true in the current context. The confirmation of these
Theoretical contribution
The extended UTAUT framework proposed in this study enhances the understanding of technology adoption at the BOP. The outcome of the primary hypotheses proposed in our research model is found to be consistent with the UAUT results. It reconfirms the generalizability of the UTAUT model. The context-specific insights received from the current study enrich the current state of knowledge in the domain of technology adoption.
Secondly, this study has included a construct related to the monetary
Practical implications
Empirical outcomes of our investigations have inferences for corporate managers as well as the public policymakers. The empirical result of this study shows that “Perceived Monetary Value” of a smartphone has a strong impact on “Behavioral Intention” to use a smartphone at the BOP. This result implies that the managers should carefully take the decision regarding the pricing of the smartphone targeted for the BOP segment as these people are sensitive to cost due to their limited disposable
Conclusion
The current study proposed an extended UTAUT model and checked its validity in the context of smartphone adoption at the BOP. Using 590 data points from Indian BOP segment, we have found that PE, EE, SI, and PMV of a smartphone usage have positive impacts on BI to use a smartphone. The study established that FC and BI positively impact UB.
Although the technology adoption rate is higher at the top and middle of the economic pyramid, the markets have gradually become saturated in those segments.
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