Extending unified theory of acceptance and use of technology with perceived monetary value for smartphone adoption at the bottom of the pyramid

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International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt

Extending unified theory of acceptance and use of technology with perceived monetary value for smartphone adoption at the bottom of the pyramid Kuldeep Baishya*, Harsh Vardhan Samalia Indian Institute of Management Shillong, Nongthymmai, Meghalaya 793014, India

A R T I C LE I N FO

A B S T R A C T

Keywords: Technology adoption Smartphone BOP UTAUT Perceived monetary value

The affluent markets of developed countries have become very competitive. Therefore, companies are trying to explore market opportunities at the segment of low-income people termed as “Bottom of the Pyramid” (BOP). With the proliferation in popularity and reduction in the price of smartphones, there is a potential market opportunity for smartphone producing companies at the BOP segment. The companies need to identify the factors influencing smartphone adoption at the BOP in order to explore this market opportunity. The current study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) with “Perceived Monetary Value” to investigate the antecedents of smartphone adoption at the BOP. Empirical analysis has shown that “Performance Expectancy” (PE), “Effort Expectancy” (EE), “Social Influence” (SI), and “Perceived Monetary Value” (PMV) predict the “Behavioral Intention” (BI), and BI and “Facilitating Conditions” (FC) predict the “Use Behavior” (UB). Findings from this study can be used by the managers of the companies targeting the BOP segment in pricing, marketing, and product-specific decision-making process. The policymakers can also analyze the results of this study for successful implementation and delivery of Information and Communication Technology (ICT) based services for the BOP segment.

1. 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 lowincome 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

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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, & AlShuridah, 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, &

Corresponding author. E-mail addresses: [email protected] (K. Baishya), [email protected] (H.V. Samalia).

https://doi.org/10.1016/j.ijinfomgt.2019.11.004 Received 17 September 2018; Received in revised form 22 September 2019; Accepted 4 November 2019 0268-4012/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Kuldeep Baishya and Harsh Vardhan Samalia, International Journal of Information Management, https://doi.org/10.1016/j.ijinfomgt.2019.11.004

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2.2. Literature related to smartphone adoption

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 contextspecific 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.

Researchers have recommended the use of ICTs for improving service delivery in the BOP segment (e.g., Berger & Nakata, 2013; Tarafdar, Anekal, & Singh, 2012) as the ICT based service delivery channels can be utilized to reform public administration (Shareef, Dwivedi, Kumar, & Kumar, 2016). Adoption of mobile-based devices plays a critical role in the use of ICT (Law, Hom, Buhalis, Cobanoglu, & Sarasota, 2014). Diffusion of mobile telephony and mobile-based applications are in the rise in developing countries (Chaudhuri, 2012; Choudriea, Junior, McKenna, & Richter, 2019). The number of smartphone users in India in 2017 was 299.24 million, and the same is predicted to cross 400 million in 2022 (Portal, 2018). Looking at the popularity and high penetration rate of smartphones, mobile-based applications can be considered as potential platforms for delivering efficient services in the BOP segment. Therefore, scholars have encouraged the need for studies on adoption of advanced mobile-based services at the BOP (Hossain & Jamil, 2015; Kansal, 2016). A few studies have explored the adoption of mobile telephony in the BOP context. The economic and societal benefits associated with the usage of mobile phones are detailed in multiple studies (e.g., Abraham, 2007; Silva & Ratnadiwakara, 2008). Silva, Ratnadiwakara, and Zainudeen, (2011) looked at the social influence of the adoption of mobile phone among the BOP people and concluded that social pressure is an important factor of mobile phone adoption. Chabossou, Stork, Stork, and Zahonogo, (2009) found that income and education positively contribute to the adoption of mobile phones. Maity (2014) checked the moderating roles of age and gender in mobile phone adoption at the BOP segment and found that both age and gender moderate the impact of “Perceived Ease of Use” in voice service adoption. Akter, Ray, and D’Ambra, (2013) explored the role of service quality and trust in adoption of mobile-based health services in the BOP segment in Bangladesh, and found that both “Perceived Quality” and “Perceived Trust” are significant predictors of mobile-based health service adoption. These studies applied different technology adoption models for exploring the variables of their concerns in the BOP segment. The exploration of smartphone adoption can be studied under technology adoption models. UTAUT is one of the widely accepted models for the study of technology acceptance in a variety of settings (Venkatesh et al., 2016). This model has been widely applied, extended, and integrated for empirical testing of adoption of a range of ICT based technologies, such as mobile payment (Slade, Dwivedi, Piercy, & Williams, 2015), online banking (Alalwan, Dwivedi, & Rana, 2017; Alalwan, Dwivedi, Rana, & Algharabat, 2018; Martins, Oliveira, & Popovič, 2014), open data technology (Zuiderwijk, Janssen, & Dwivedi, 2015), and mobile marketing (Shareef, Dwivedi, Kumar, & Kumar, 2017). However, there are limited studies which extend UTAUT or any other technology adoption model to the BOP context. Particularly, to the best of our knowledge, no research has explored smartphone adoption in the BOP context. Therefore, this study proposes an extended UTAUT based theoretical framework and test it empirically to identify the factors impacting smartphone adoption at the BOP.

2. 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.

2.1. Literature related to BOP BOP represents the economically weaker segment of the world’s population with a daily per capita income of US$2 or less (Prahlad, 2004). It consists of 2.7 billion people with an estimated market value of worth US$5 trillion (Rahman, Mannan, & Amir, 2018). Heeks (2012) associated the BOP with the “Emerging Market” of low-income nations with a significant population who live on a daily expense of US$5 or less. He mentioned BOP as a high-growth market and advocated the use of emerging IT innovation models for designing products for this segment. Some of the researchers in the field of BOP have proposed a cutoff value of per capita income of US$3000 per year for this segment (Calton, Werhane, Hartman, & Bevan, 2013; Hart & London, 2011). For this research, we have set the upper threshold value for BOP at Indian Rupees 13,152 per month considering five members per family with a living expense of US$5 per person per day (Heeks, 2012) with a purchasing power parity value of 17.536 (OECD, 2017). While setting this upper threshold value of income to qualify for the BOP, we have considered Prahlad’s proposition of US$2 per person per day to be too low. On the other hand, the upper cutoff per capita value of US$3000 or more per year (Calton et al., 2013; Hart & London, 2011; Yurdakul, Atik, & Dholakia, 2017) is considered to be too high from a developing country’s perspective. The BOP segment faces numerous challenges in purchasing essential commodities due to non-affordability and separation from formal markets (Karnani, 2007; Prahlad, 2004). Prahlad (2012) explained the benefits of converting the unorganized BOP market to an organized market. Berger and Nakata (2013) advocated for (suggested the logic of) leveraging ICTs for the benefits of the BOP.

3. 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 current study is specific to the BOP, it is pertinent to consider BOP specific construct in the research model. Financial constraints may impact the actions of the people belonging to the BOP 2

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Fig. 1. Original UTAUT Framework.

segment. This financial challenge may compel them to look for higher “Value for Money”. They tend to search for better quality products at affordable prices (Prahlad, 2004; Rahman, Hasan, & Floyd, 2013; Rahman et al., 2018). The perception of monetary value plays a critical role, especially in the BOP segment in buying decision-making process. Therefore, we have extended UTAUT with “Perceived Monetary value” (PMV) (Dodds, Monroe, & Grewal, 1991; Lallmahomed, Lallmahomed, & Lallmahomedc, 2017; Turel, Serenko, & Bontis, 2007) to explore smartphone adoption at the BOP. The proposed research model considers that PE, EE, SI, and PMV are direct predictors of BI; and FC and BI are direct predictors of UB. As per propositions made in the original UTAUT model, this study has considered gender, age, and experience as the moderators in the proposed model. Additionally, the moderating effects of these three variables are also checked in the proposed relationship between PMV and BI. Fig. 2 displays the pictorial representation of the proposed model.

social media, Internet, mobile banking, gaming, and online shopping. However, the utility varies from person to person, and everyone may have different perceptions about the benefits of a smartphone. Many of the previous research have established positive association between PE and BI in variety of contexts, such as mobile payment (Oliveira, Thomas, Baptista, & Campos, 2016), online tax filing (Lu & Nguyen, 2016), digital learning (Pynoo et al., 2011), e-Library (Awwad, 2015), e-Government (Gupta, Dasgupta, & Gupta, 2008; Lallmahomed et al., 2017), online shopping (Lian & Yen, 2014), and grievance management system (Rana, Dwivedi, Williams, & Weerakkody, 2016). A positive perception of an individual about the usefulness and benefits associated with the usage of a smartphone is likely to boost the adoption. This logic can be transformed to the following hypothesis. H1. “Performance Expectancy” of smartphone usage has a significant positive effect on “Behavioral Intention” to use a smartphone at the BOP. The original UTAUT model considered gender and age as moderators in the relationship between PE and BI. Research on gender studies suggest that men are task-oriented (Minton & Schneider, 1980) and therefore, PE of smartphone which emphasizes task accomplishments should be stronger for men. The younger people are more

3.1. Performance-Expectancy (PE) PE of smartphone usage is the extent to which a person perceives the use of a smartphone as helpful in attaining gains in daily life. A smartphone can be used for multiple purposes, including voice call,

Fig. 2. Extended UTAUT. 3

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3.3. Social influence (SI)

interested in extrinsic rewards (Hall & Mansfield, 1975) which indicates a moderating role of age in the relationship between PE and BI. Considering these facts, we propose the following sub-hypotheses.

SI of smartphone usage is the degree to which a person believes that other individuals who are important in his/her life think that he/she should use a smartphone. A person is influenced by his/her friends, family, and colleagues. With the increase in demand, the smartphone may become a necessary product for social assimilation. Therefore, a person’s BI to use a smartphone is likely to be influenced by SI. The relationship between SI and BI is already established in existing literature in diversified contexts, such as adoption of e-Government (Gupta et al., 2008), e-Library acceptance (Awwad, 2015), online tax filing (Lu & Nguyen, 2016), online air ticket purchasing (EscobarRodriguez & Carvajal-Trujillo, 2014), mobile payment (Khalilzadeh, Ozturk, & Bilgihan, 2017), and online shopping (Lian & Yen, 2014). Baishya, Samalia, and Joshi, (2017) argued that higher education may sometime suppress the impact of SI on BI. Since the BOP people are less educated, they would be more influenced by the social basis rather than the instrumental basis. Therefore, the next hypothesis can be framed as follows.

H1a. The association between “Performance Expectancy” of smartphone usage and “Behavioral Intention” to use a smartphone at the BOP is moderated by gender. H1b. The association between “Performance Expectancy” of smartphone usage and “Behavioral Intention” to use a smartphone at the BOP is moderated by age.

3.2. Effort expectancy (EE) EE of smartphone usage can be defined as the extent of an individual’s perception about the ease of using a smartphone. EE was adapted from TAM where it was termed as “Perceived Ease of Use”. Usage of mobile services can be increased with user-friendly designs which are easy to use and learn (Kim, Mirusmonov, & Lee, 2010). “Perceived Ease of Use” is conceptualized as an essential factor in various technology adoption studies encompassing a variety of domains (Liebana-Cabanillas, Sanchez - Fernández, & Munoz - Leiva, 2014). Selfperception of an individual about the ease of using a smartphone is likely to have a positive impact on its adoption. The easy to use and user-friendly features of a smartphone will expedite the adoption process. The relationship between EE and BI is empirically established in many existing studies in a variety of settings, such as online ticket purchase (Escobar-Rodriguez & Carvajal-Trujillo, 2014), e-Library acceptance (Awwad, 2015), mobile health (Dwivedi, Shareef, Simintiras, Lal, & Weerakkody, 2016), and online shopping (Lian & Yen, 2014). Dwivedi, Rana, Jeyaraj et al. (2017) established this relationship in a meta-analytic structural equation modeling with a sample drawn from bibliographic databases. Shareef, Baabdullah, Dutta, Kumar, and Dwivedi, (2018) found that “Perceived Ability to Use” which is analogous to EE is a predictor of mobile banking adoption. The quantum of EE of smartphone use at the BOP may significantly vary from other segments due to the difference in education level and exposure to technology-based devices. Thus, we propose the following hypothesis.

H3. “Social Influence” of smartphone usage has a significant positive effect on “Behavioral Intention” to use a smartphone at the BOP. We may also frame sub-hypotheses based on UTAUT model by including age, gender, and experience as moderating variables in the positive relationship between SI and BI. Females care more about others’ judgments on their activities than the males (Miller, 1976; Venkatesh, Morris, & Ackerman, 2000). The need for affiliation of a person increases with age (Rhodes, 1983), suggesting that SI is stronger for older people. The impact of SI should decrease with experience as it would provide the users with some instrumental basis for adoption decision. Based on these facts, we propose the following sub-hypotheses. H3a. The association between “Social Influence” of smartphone usage and “Behavioral Intention” to use a smartphone at the BOP is moderated by gender. H3b. The association between “Social Influence” of smartphone usage and “Behavioral Intention” to use a smartphone at the BOP is moderated by age.

H2. “Effort Expectancy” of smartphone usage has a significant positive effect on “Behavioral Intention” to use a smartphone at the BOP.

H3c. The association between “Social Influence” of smartphone usage and “Behavioral Intention” to use a smartphone at the BOP is moderated by experience.

The original UTAUT proposed the existence of moderating effects of gender, age, and experience in the relationship between EE and BI. The cognitions related to gender roles (Lynott & McCandless, 2000) may induce a moderating effect of gender, and the difficulty associated with increased age in processing complex stimuli (Plude & Hoyer, 1985) may drive a moderating effect of age in the relationship between EE and BI. As the BOP people are less educated (Prahlad, 2004), and they rarely get assistance from the tech-savvy people in other segments, there is a fair possibility that they will get the real sense of EE with increased experience. To include the moderating effects of these three variables in the relationship between EE and BI, following sub-hypotheses can be proposed.

3.4. Perceived monetary value (PMV) The original UTAUT was developed and tested in organizational settings where the monetary cost of technology use is usually borne by the organization. However, in individual consumer settings, the consumer has to bear the monetary cost associated with the use of the product (Venkatesh, Thong, & Xu, 2012). The purchasing and recurring cost associated with the use of a smartphone may significantly impact an individual in the adoption decision. This impact may be more when the individual has financial constraints (Hart & London, 2011). Perceived Monetary Value (PMV) is an issue of concern, especially for the people belonging to the BOP segment due to their limited disposable income (Prahlad, 2004). In the case of a non-essential product such as a smartphone, they may minimize the budget as they are bound to spend most of their earnings on basic needs. PMV of a smartphone can be defined as the extent to which a person perceives the suitability of the cost of a smartphone with regards to perceived benefits. This construct is a combination of two aspects, namely price and perceived value (Dodds et al., 1991). Therefore, the first measurement scale item of PMV (PMV1) captures the price aspect (Kim, Park, & Oh, 2008) of the smartphone and the second measurement scale (PMV2) captures the perceived value (Kang & Maity, 2012) aspect of the smartphone. Both

H2a. The association between “Effort Expectancy” of smartphone usage and “Behavioral Intention” to use a smartphone at the BOP is moderated by gender. H2b. The association between “Effort Expectancy” of smartphone usage and “Behavioral Intention” to use a smartphone at the BOP is moderated by age. H2c. The association between “Effort Expectancy” of smartphone usage and “Behavioral Intention” to use a smartphone at the BOP is moderated by experience.

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3.5. Facilitating-Conditions (FC)

these measurement scales are adapted from previous literature listed in Appendix A. Kim et al. (2008) mentioned that the belief of a person regarding the suitability of price (PMV1) can capture the price aspect of the product. PMV is a multidimensional construct, and it does not only depend on the available budget but also on the perception of the value obtained compared to the incurred cost (Dodds et al., 1991). In other words, people may decide to slightly increase the budget for a product if they find that the product offers value for them. Kang and Maity (2012) used the “value for money” measure (PMV2) to capture the perceived value aspect. Thus, the measurement scales PMV1 and PMV2 are found to be appropriate to measure PMV (Kang & Maity, 2012; Kim et al., 2008). Some of the studies concerning adoption and product evaluation have inducted constructs related to the monetary aspect. For instance, Escobar-Rodriguez and Carvajal-Trujillo (2014) found a direct positive relationship between Price-saving orientation and BI. Dodds et al. (1991) introduced “Price” and “Perception of Value” in the context of product evaluation, and empirically found that “Price” and “Perception of Value” are negatively related. Kim et al. (2008) explored the factors of consumers’ adoption of short message service (SMS) in Korea and found that “Perceived Monetary Value” is a positive predictor of “Continued Intention to Use” SMS. Lallmahomed et al. (2017) investigated the acceptance of e-Government facilities in Mauritius and established that “Perceived Price Value” has a significant positive impact on BI to use e-Government service. Several studies have found a positive relationship between PMV and BI in a variety of contexts, such as location-based mobile services (Pura, 2005), short messaging service (Turel et al., 2007), and mobile Internet (Kim, Chan, & Gupta, 2007). However, Baptista and Oliveira (2015) found that the impact of “Price Value” is not significant on BI to use mobile banking. The reason for this contradicting result was attributed to the fact that mobile banking services are seen as free of cost by users. In the case of a smartphone, the purchasing and network subscription costs may impact the intention of using it. Therefore, the fourth hypothesis can be proposed as follows.

FC of smartphone usage can be defined as the extent of an individual’s consideration about the availability of proper knowledge and assistance to continue the use of a smartphone. The use of a smartphone requires certain sets of skills and knowledge, such as installing new applications and managing settings. Thong, Venkatesh, Xu, Hong, and Tam, (2011) theorized and validated FC as an undeviating antecedent to actual use or “Use Behavior” (UB). The relationship between FC and UB is already established in many empirical studies in variety of contexts, such as adoption of e-Government (Gupta et al., 2008), online air ticket purchasing (Escobar-Rodriguez & Carvajal-Trujillo, 2014), e-Library acceptance (Awwad, 2015), mobile banking (Baptista & Oliveira, 2015, 2017; Oliveira, Faria, Thomas, & Popovič, 2014), and clinical decision support system (Chang, Hwang, Hung, & Li, 2007). Therefore, we intend to test this relationship in the BOP context with the following hypothesis. H5. “Facilitating Conditions” of smartphone usage has a significant positive effect on “Use Behavior” of a smartphone at the BOP. The UTAUT model considered the presence of moderating effects of age and experience in the association between FC and UB. Older people are more inclined towards receiving assistance (Hall & Mansfield, 1975), indicating a stronger impact of FC on UB. The effect is expected to be stronger with increasing experience as the smartphone users’ will explore multiple ways to get assistance and support for use. Applying these logics, we can propose the following sub-hypotheses. H5a. The association between “Facilitating Conditions” of smartphone usage and “Use Behavior” of a smartphone at the BOP is moderated by age. H5b. The association between “Facilitating Conditions” of smartphone usage and “Use Behavior” of a smartphone at the BOP is moderated by experience.

H4. “Perceived Monetary Value” of a smartphone has a significant positive effect on “Behavioral Intention” to use a smartphone at the BOP.

3.6. Behavioral-Intention (BI) BI to use a smartphone is the extent to which a person has outlined mindful tactics to use or not to use the smartphone in the future. Adoption is closely associated with BI (Carter & Belanger, 2005). In the absence of an external factor such as affordability, BI to use a smartphone is likely to lead to actual use. The positive relationship between BI and UB is almost universally accepted and has been established in many empirical studies in different settings, such as the adoption of digital learning (Pynoo et al., 2011), e-Library (Awwad, 2015), and online air ticket purchasing (Escobar-Rodriguez & Carvajal-Trujillo, 2014). Therefore, the following hypothesis can be proposed.

We may explore the moderating effects of gender, age, and experience in the relationship between PMV and BI to use a smartphone. Perception of price and benefits of a smartphone may differ across gender, age, and experience. Women seek more details in the decisionmaking process (Deaux & Kite, 1987), and they are more careful than men in spending money (Slama & Tashchian, 1985). Theories on social role suggest that there exist a difference in behavior based on age, and elderly people are likely to be more conscious on evaluating “price value” of products as they are involved in taking care of their families (Deaux & Lewis, 1984). The perception of the monetary value of a smartphone may change with experience as the users may discover the merits and demerits of using it with increased experience. Therefore, we propose the following sub-hypotheses.

H6. “Behavioral Intention” to use a smartphone has a significant positive impact on “Use Behavior” of the smartphone at the BOP.

H4a. The association between “Perceived Monetary Value” of smartphone and “Behavioral Intention” to use a smartphone at the BOP is moderated by gender.

4. Research methodology

H4b. The association between “Perceived Monetary Value” of smartphone and “Behavioral Intention” to use a smartphone at the BOP is moderated by age.

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 workplace from both rural and urban areas of six states of India, namely Assam, Delhi, Gujarat, Karnataka, Madhya Pradesh, and Maharashtra. These six states were selected to represent the entire

4.1. Data and sample

H4c. The association between “Perceived Monetary Value” of smartphone and “Behavioral Intention” to use a smartphone at the BOP is moderated by experience.

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to the local languages, namely Assamese, Hindi, Kannada, and Marathi. Two native translators were independently allocated for each language. Both the independent versions translated by the local translators were examined by both the translators and the final version was agreed. The final version of each language was translated back to English by another translator to ensure consistency between English and the local languages.

Table 1 Respondents’ Demographic Statistics (N = 590). Demographics

Category

Frequency

Percentage

Gender

Male Female Up to 25 26-35 36-45 46-55 > 55 0-6 7-24 > 24 No education Primary High School Intermediate Graduation Post-Graduation

366 224 178 153 145 107 7 199 165 226 73 251 79 94 81 12

62.03 37.97 30.17 25.93 24.58 18.14 1.19 33.73 27.97 38.31 12.37 42.54 13.39 15.93 13.72 2.03

Age

Experience (In months)

Education

5. 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 Mueller (2013) mentioned that normality may be a problem when the values of univariate skewness and kurtosis approach 2 and 7, respectively. The problem of multicollinearity is checked through the variance inflammatory factor (VIF) of the independent variables. The VIF of PE, EE, SI, FC, and PMV are found to be 1.248, 1.545, 1.315, 1.312, and 1.176, respectively which are well below the cutoff value of 5 (Hair, Black, Babin, & Anderson, 2006). The presence of outlier of the dataset is analyzed through general Cook’s distance (gcd) (Cook, 1977), and no issue of concern is detected in the dataset. We have tested the presence of CMV by Harman’s single factor test (Harman, 1967). In this test, CMV is a concern if a single factor accounts for the majority of the variance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). In the current study, the parallel analysis of the dataset suggests six factors, and none of them explains more than 50 percent of the variance. Therefore, CMV was found posing no issue in the dataset.

geographical zones of India. We identified the poor people in urban slum areas with the help of the Municipality authorities, and the rural BOP people were identified with the help of Gram Panchayat authorities. The authorities were requested to help us to identify the areas where people face challenges regarding disposable income, electricity, access to information, and education. Data collection period was between February 2017 and July 2018. Each respondent was given a nutritional food package as an incentive for participating in the survey. The demographic statistics of the respondents are listed in Table 1. Different scholars have different opinions on the minimum sample size required in Structural Equation Modeling (SEM). For instance, Jackson (2003) recommended a sample size (N) to the number of parameters (q) ratio of 20:1 for a good analysis in SEM, while Bentler and Chou (1987) mentioned that a sample size of 10 per parameter is good enough for analysis. Based on the median value of the sample size used in surveys of 165 SEM based published articles, Kline (2005) mentioned that the typical sample size in SEM based study is 200. However, Kline (2005) cautioned that the sample size of 200 may be too small for a complex model. In this study, we have 21 valid measurement scale items which necessitate a minimum sample size of 210 considering 10 data points per measurement scale item (Bentler & Chou, 1987). Every measurement scale item is captured using a Likert scale ranging from 1 to 5. We have administered the questionnaire to 1000 respondents out of which 590 data points are found to be valid, indicating a valid response rate of 59 percent. A face-to-face process of data collection helped us in achieving a high rate of valid response. Missing values in the dataset are handled using the deletion method (Yadav & Roychoudhury, 2018).

5.1. Measurement model This model is used to confirm the reliability and validity of the constructs and associated measurement scale items. Each item is expected to have a factor loading of 0.5 or more to fit in the Measurement Model (Hu & Bentler, 1999). In this phase, one item of Facilitating Conditions (FC3) is removed from the model for having a factor loading less than 0.5. All other items are found to have a satisfactory factor loading of more than 0.5. The Composite Reliability score of each construct is found to be higher than the least acceptable value of 0.7 (Fornell & Larcker, 1981; Hair et al., 2006). Fornell and Larcker (1981) set the minimum value of AVE at 0.5 to satisfy the condition of convergent validity. Table 2 lists the factor loading, Composite Reliability, and AVE values for the measurement scales and the associated constructs. The criteria for confirming the discriminant validity is that the square root of AVE of a construct must be higher than the correlation of that construct with any other construct (Fornell & Larcker, 1981). Table 3 shows that the criterion of the discriminant validity is well satisfied in this study. We have also separately checked the reliability and validity of the data collected in each local language. Each of the data subsets has satisfied the conditions of reliability and validity. Once the measurement model has fulfilled the criteria of reliability and validity, we have checked the goodness-of-fit (gof) indices for the model. Table 4 displays the gof indices’ values and their respective acceptable criteria.

4.2. Methods and software We have used Structural Equation Modeling (SEM) for the empirical validation of the research model. A package called ‘Lavaan’ is used in R to execute SEM. We have calculated Composite Reliability to confirm that the constructs are reliable. Convergent and Discriminant validities of the measurement model are checked through “Average-VarianceExtracted” (AVE). Goodness-of-fit for the model is checked through multiple indices, including Root Mean Square Error of Approximation (RMSEA), Chi-Square (with regards to the degree of freedom), Standardized Root Mean Square Residual (SRMR), Comparative Fit Index (CFI), and Tucker-Lewis Index (TLI). PE, EE, SI, FC, PMV, and BI are measured through items validated in original UTAUT and product evaluation (Dodds et al., 1991) model. “Use Behavior” is measured in terms of the extent of smartphone exploration. A few additional measurement scales are captured to enrich the current context. Measurement scale items of the constructs are given in Appendix A with their source. These scales are also translated

5.2. Structural model The structural model checks the validities of the specified relationships among the constructs. We have estimated three different models with 590 valid data points to determine the validity of the 6

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Table 2 Factor Loading, Composite Reliability, and AVE.

Table 5 Model estimation without moderators.

Construct

Item

Factor Loading

Composite Reliability

AVE

PE

PE1 PE2 PE3 PE4 EE1 EE2 EE3 EE4 EE5 SI1 SI2 SI3 FC1 FC2 FC3 FC4 PMV1 PMV2 BI1 BI2 BI3

0.888 0.853 0.840 0.852 0.847 0.858 0.830 0.833 0.829 0.885 0.864 0.855 0.825 0.902 0.383 0.898 0.909 0.874 0.853 0.897 0.884

0.92

0.74

EE

SI

FC

PMV BI

0.92

0.70

0.90

0.75

0.91

0.77

0.89

0.80

0.91

0.77

Independent Variable

Dependent Variable

Estimate

Standard Error

Z value

PE EE SI PMV BI FC R Square

BI BI BI BI UB UB BI UB

0.257*** 0.191*** 0.215*** 0.238*** 0.486*** 0.334*** 0.365 0.318

0.045 0.049 0.043 0.045 0.050 0.040

5.716 3.859 5.036 5.247 9.771 8.371

RMSEA: 0.060 SRMR: 0.039 CFI: 0.959 TLI: 0.951 Chi-Square/Degree of Freedom: 3.11

Note: *p < .05, **p < .01, ***p < .001.

This newly estimated model has an improved model fit indices. The RMSEA and SRMR have improved a bit, while we can see a significant improvement in CFI and TLI values.

6. Discussion

Table 3 Discriminant Validity of Measurement Model. Construct

PE

EE

SI

FC

PMV

BI

UB

PE EE SI FC PMV BI UB

0.86 0.35 0.27 0.31 0.31 0.43 0.28

0.84 0.45 0.44 0.32 0.43 0.33

0.87 0.34 0.22 0.41 0.28

0.88 0.17 0.41 0.50

0.89 0.41 0.23

0.88 0.46

1

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 hypotheses at the BOP enhances the generalizability of the UTAUT. On the other hand, the moderating effects of the demographic variables are found inconsistent with the UTAUT. The reasons for these inconsistencies in the moderating effects may be attributed to the difference in socioeconomic and cognitive characteristics of the BOP in terms of disposable income, assimilationist culture (Gupta & Srivastav, 2016), education level, and consumption pattern (Chikweche, Stanton, & Fletcher, 2012). The current study rejects the moderating role of gender as opposed to the original UTAUT. Previous literature argued that the quantum of ICT adoption for women is lower than men because of a variety of depriving factors, such as lesser access to financial resources, lower education level, and lack of skills (Hafkin & Taggart, 2001). The respondents’ profile in the current study shows no special deprivation for women as compared to men in terms of education or financial resource. Rather, the percentage of women respondents having an educational qualification of intermediate level or above (34.8 %) is slightly higher than the percentage of male respondents having the same level of education (29.7 %). The average monthly household income of the women respondents (INR 9512) is also found to be slightly higher than the average monthly household income of the men respondents (INR 9168). The absence of deprivation for women may have resulted in an insignificant moderating effect of gender. This is an interesting finding

Note: Diagonal elements are the square root of AVE of the constructs, off-diagonal elements are the correlation between the constructs.

proposed model. The first model checks the direct relationships among the predictors and the outcome variables without including any moderator. The subsequent model includes all the moderators as proposed in Fig. 2. The third model estimates a model with all the predictors and moderators which are found to be significant in the second model. A ‘p’ value below 0.05 in the regression indicates support of the proposition at a 95 percent confidence level. The collected dataset supports all the primary hypotheses at a 95 percent confidence level. However, some sub-hypotheses stating the moderating roles are rejected. Table 5 displays the estimation of the model without the inclusion of moderators. In the next model, we have included age, experience, and gender to see their moderating effects as proposed in the research model. Table 6 provides the estimation of the proposed model along with the moderating effect. The model with all the moderators has a relatively lower CFI and TLI value. In order to improve these model fit indices, we have estimated the model again with only those predictors which are found to be significant in the previous model. The outcomes of the newly estimated model are shown in Table 7.

Table 4 Measurement Model Fit Indices. Sl. No.

Fit-Index

Obtained Value

Acceptable Criteria

Reference

1 2 3 4 5

RMSEA SRMR Chi-Square/degree of freedom CFI TLI

0.059 0.030 3.08 0.961 0.952

< 0.07 < 0.08 <5 Close to or > 0.9 Close to or > 0.9

(Browne & Cudeck, 1992; Hu & Bentler, 1999; Steiger, 2007) (Hu & Bentler, 1999) (Wheaton, Muthén, Alwin, & Summers, 1977) (Bentler, 1990; Hu & Bentler, 1999) (Bentler & Bonett, 1980; Hu & Bentler, 1999)

7

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Table 6 Model estimation with moderators.

Table 7 Model estimation with selected moderators.

Independent Variable

Dependent Variable

Estimate

Standard Error

Z value

Independent Variable

Dependent Variable

Estimate

Standard Error

Z value

PE EE SI PMV Gender(G) Age(A) Experience(E) G*A G*E A*E G*A*E PE*G PE*A PE*G*A EE*G EE*A EE*E EE*G*A EE*G*E EE*A*E EE*G*A*E SI*G SI*A SI*E SI*G*A SI*G*E SI*A*E SI*G*A*E PMV*G PMV*A PMV*E PMV*G*A PMV*G*E PMV*A*E PMV*G*A*E BI FC Age Experience A*E FC*A FC*E FC*A*E R Square

BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI BI UB UB UB UB UB UB UB UB BI UB

0.221*** 0.209*** 0.226*** 0.185*** 0.003 −0.084* 0.102** −0.010 −0.046 −0.104* −0.095 0.006 0.320*** 0.041 −0.009 −0.323*** 0.473*** −0.026 −0.123 −0.120 0.094 0.061 −0.059 −0.280*** −0.045 0.048 0.082 −0.129 0.002 0.003 0.004 0.011 0.002 0.002 0.217* 0.305*** 0.515*** −0.016 0.129*** 0.026 −0.247** 0.349*** 0.197* 0.438 0.369

0.044 0.049 0.044 0.046 0.017 0.034 0.038 0.042 0.045 0.043 0.056 0.055 0.092 0.110 0.066 0.095 0.148 0.109 0.125 0.099 0.109 0.058 0.079 0.072 0.103 0.080 0.092 0.118 0.134 0.144 0.094 0.134 0.154 0.123 0.089 0.040 0.048 0.035 0.039 0.044 0.079 0.081 0.086

4.972 4.227 5.182 4.049 0.194 −2.470 2.658 −0.243 −1.042 −2.380 −1.718 0.118 3.492 0.377 −0.132 −3.382 3.192 −0.242 −0.985 −1.210 0.866 1.059 −0.751 −3.887 −0.436 0.599 0.898 −1.093 1.101 1.123 1.201 1.130 1.211 1.121 2.487 7.713 10.677 −0.457 3.282 0.585 −3.120 4.303 2.287

PE EE SI PMV Age(A) Experience(E) A*E PE*A EE*A EE*E SI*E PMV*GAE BI FC Experience FC*A FC*E FC*A*E R Square

BI BI BI BI BI BI BI BI BI BI BI BI UB UB UB UB UB UB BI UB

0.229*** 0.202*** 0.226*** 0.184*** −0.091** 0.102** −0.112** 0.290*** −0.361*** 0.356* −0.231*** 0.209* 0.305*** 0.513*** 0.129*** −0.248** 0.352*** 0.188* 0.421 0.367

0.043 0.048 0.041 0.044 0.034 0.039 0.044 0.085 0.079 0.140 0.067 0.090 0.039 0.048 0.039 0.079 0.081 0.085

5.288 4.193 5.449 4.171 −2.653 2.650 −2.568 3.428 −4.556 2.548 −3.458 2.489 7.758 10.661 3.303 −3.131 4.346 2.195

RMSEA: 0.046 SRMR: 0.039 CFI: 0.933 TLI: 0.925 Chi-Square/Degree of Freedom: 2.22

Note: *p < .05, **p < .01, ***p < .001.

contradictory finding (when compared with the UTAUT) suggests that the generational gap in technology adoption differ vastly between organizational (UTAUT) and consumer (current study) settings. The first hypothesis which proposed a relationship between PE of smartphone usage and BI to use a smartphone is found significant. As expected, this relationship is found strong as the people belonging to the BOP segment cannot afford to adopt non-beneficial products. Davis (1986) argued that the perception of usefulness is one of the most superior determinants of technology adoption. Bhatiasevi (2016) found a positive relationship between PE and BI to use mobile banking among university students in Thailand. The relationship between PE and BI is found significant in a variety of contexts across countries, such as mobile payment adoption in Portugal (Oliveira et al., 2016), mobile banking adoption in Brazil (Baptista & Oliveira, 2017), and mobile health adoption in Bangladesh, Canada, and USA (Dwivedi et al., 2016). Gupta, Dogra, and George, (2018) found that PE has a positive impact on the BI to use smartphone applications for tourism purposes among domestic tourists in India. Safeena, Date, Kammani, and Hundewale, (2012) explored the mobile banking adoption in India and found that “Perceived Usefulness” is a significant determinant of BI to use mobile banking. Age moderates the relationship between these two constructs such that the effect is stronger for older users. This finding contradicts with UTAUT, which established that the effect of PE on BI is more for younger people. This contradiction may have arisen because UTAUT was tested in organizational settings where the younger workers may place more importance to extrinsic rewards than the older workers (Hall & Mansfield, 1975) resulting in a stronger impact of PE. However, in consumer settings, the increasing financial responsibilities of older people induce them to place more emphasis on PE. Also, as opposed to the UTAUT, we could not establish the moderating role of gender. The second hypothesis which proposed an association between EE of smartphone usage and BI to use a smartphone is also found to be significant in this study. Perception of effort required to use technology is considered to be a key determinant of adoption (Davis, 1986; Venkatesh et al., 2003). At the BOP segment, EE of smartphone would play a significant role as many people are yet unfamiliar with the basic

RMSEA: 0.051 SRMR: 0.040 CFI: 0.854 TLI: 0.842 Chi-Square/Degree of Freedom: 2.52

Note: *p < .05, **p < .01, ***p < .001.

of the study which implies that if the gaps in terms of the socio-economic factors, such as access to financial resource and education are bridged, there may be a decrease in “digital divide” between men and women. In a study related to telephone, Zainudeen, Iqbal, and Samarajiva, (2010) concluded that there are few gender centric differences in telephone usage in the BOP segment. The moderating role of age is found to be contradictory with the findings of the UTAUT model. Current findings suggest that older people give more importance to utilitarian needs in adopting a smartphone (both the utilitarian constructs PE and PMV showed stronger impact for older people). On the other hand, the impact of “Effort Expectancy” and “Facilitating Conditions” are found to be stronger for younger people. Younger people may view a smartphone as a tool for entertainment (Volkom, Stapley, & Malter, 2013) and this perception may be more in the BOP segment due to their assimilationist behavior (Gupta & Srivastav, 2016). Therefore, younger people are found to emphasize less on utilitarian aspects as compared to older people. This 8

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Table 8 Results. Sl. No.

Independent Variable

Dependent Variable

Moderators

Explanation

1 2

PE EE

BI BI

Age Age, Experience

3 4

SI PMV

BI BI

5 6

FC BI

UB UB

Experience No independent moderating effect; interactive effect of gender, age, and experience is realized Age and Experience None

The effect is found to be stronger at an older age. The effect is found to be stronger at a younger age. The effect is also stronger for experienced users. The effect decreases with increasing experience. The effect is stronger for older males with more experience. The effect is stronger for younger users with more experience. The direct effect is significant.

the increasing financial responsibility of a male with increasing age. At the BOP, men have more access to money as compared to women, and they assume more family responsibility (Chikweche et al., 2012) which compels them to adopt products only when the perception of monetary value is high. Chikweche et al. (2012) explored the family purchase decision-making process at the BOP segment in Zimbabwe and concluded that husbands in a family are increasingly involved in product evaluation and purchase decision. The fifth and the sixth hypotheses considered “Use Behavior” (UB) as the dependent variable. The fifth hypothesis proposed that FC of smartphone usage has a direct positive impact on UB of the smartphone. This hypothesis is found to be significant. If a person has enough resource in terms of knowledge and assistance for using a smartphone, the probability of adoption is high. We have found that age and experience moderate the association between FC and UB such that the effect is more at a younger age with increased experience. However, the original UTAUT model found that the effect of FC on UB is more intensive for an older worker with more experience. This contradictory finding may be attributed to the difference in the type of respondents used in both the studies. In this study, the skills required to use a smartphone may be more for the younger respondents due to their higher educational qualifications. However, the same may not be true for the sample used in the original UTAUT which was carried out in an organizational context where the older workers can be fairly assumed to have more organizational knowledge as compared to the younger workers. The last hypothesis proposed that BI to use a smartphone positively impacts actual “Use Behavior”. This is found to be significant, which implies that the intention to use a smartphone is important for the actual adoption. This proposition has been empirically established in most of the technology adoption literature. The cognitive, social, and monetary attributes of a person at the BOP will initially form the BI to adopt or reject a smartphone which in turn will impact the actual adoption.

flow of usage of a smartphone. In a study, Hussain, Mollik, Johns, and Rahman, (2018) explored the factors of mobile payment adoption at the BOP segment in Bangladesh and found that EE has a significant impact on BI. Rana, Dwivedi, Williams, and Weerakkody, (2015) found that BI to use online public grievance redressal system is directly impacted by the perception of ease of use of the system. Contrary to the UTAUT model which established that the relationship between EE and BI is stronger for female, aged people with less experience; current findings suggest that the effect is stronger for younger people without any role of gender. As opposed to UTAUT, the effect is also found to be stronger with increasing experience. The young people intend to use a smartphone if they find it easy to use even within their limited disposable income. The assimilationist nature of the young people at the BOP may have contributed to the effect to be stronger for younger people. The third hypothesis proposed that SI of smartphone usage impacts BI to use a smartphone at the BOP. This study has empirically validated this proposition. The result implies that the influence of close and important people impact the adoption of smartphones at the BOP. Valente (1996) reported that the adoption of innovation among the poor in emerging Asia takes place after most of the people in the personal network have adopted. The relationship between SI and BI is found significant in the adoption of online permanent account number card registration system in India (Dwivedi, Rana, Janssen et al., 2017). Experience is found to play the moderating role in such a way that the impact of SI of smartphone usage on BI to use a smartphone at the BOP is more during the early phase of experience. The original UTAUT model found similar results. On the other hand, we could not validate the role of age and gender as moderators in this relationship. The informal interaction with the respondents revealed that both young and old people are influenced in the event of smartphone usage with young people being more influenced by friends and the old people being more influenced by their children. The fourth hypothesis proposed that PMV of a smartphone has a significant influence on BI to use a smartphone at the BOP. As expected, this hypothesis is found to be significant. The BOP people are bound to be conservative in spending money due to their limited disposable income (Prahlad, 2004), and they tend to adopt only those products whose PMV is high. Therefore, the perception of monetary value has substantial stimulus on BI to use a smartphone at the BOP. A person compares the price of a smartphone with the associated benefits and forms intention to adopt it if the benefits are perceived to be higher than the cost. The current finding is analogous to several previous studies. For instance, Kim et al. (2008) established that PMV has a positive impact on “Continued Intention to Use” SMS among Korean mobile phone users. The finding is also consistent with some of the adoption studies, such as the adoption of e-Government (Lallmahomed et al., 2017) and mobile Internet (Kim et al., 2007). We could not establish any independent moderating effect of gender, age, or experience in the relationship between PMV and BI. However, the interaction of PMV, gender, age, and experience is found to positively impact BI, suggesting a stronger relationship between PMV and BI for older males with more experience. This may be because of

7. 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 aspect (Perceived Monetary Value) in the research model. As the people belonging to the BOP segment are severely impacted by limited disposable income, any adoption study at the BOP segment must not ignore the construct related to the monetary aspect. “Perceived Monetary Value” is an essential factor to be considered when the study is carried in individual consumer setting where the cost of technology use is borne by an individual user. Additionally, the moderating variables considered in this study have 9

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smartphone designers and the BOP people. The next implication of this empirical study is for the policymakers. The fact that smartphone has the potential to ease and expedite the process of delivering some of the public services makes this study interesting for the policymakers. This study has established that “Performance Expectancy” and “Social Influence” have a direct positive impact on “Behavioral Intention” to use a smartphone. Therefore, the policymakers should emphasize on creating awareness among the BOP people about the benefits associated with the usage of a smartphone. Once a person realizes the benefits of using a smartphone, he/she is likely to motivate his/her peers to use it.

indicated a few meaningful context-specific insights. The study has discovered a few contradictory results about the moderating roles of age and experience as compared to the original UTAUT. For instance; the relationship between PE and BI is found to be more for older people, the relationship between EE and BI appears stronger for younger and experienced people, the relationship between FC and UB is found to be stronger for younger people. These contradictory outcomes indicate the presence of context-specific influence on adoption at the BOP. For example, the older people in the BOP segment are forced to place more importance to utilitarian needs as compared to hedonic needs due to the increasing family responsibility with the limited disposable income resulting in a stronger relationship between PE and BI for the older people. The younger people in the BOP segment may have a relatively better realization of the actual effort required to use a smartphone due to their higher education level as compared to the older people which results in a stronger relationship between EE and BI for the younger people. Similarly, the younger people in the BOP segment may have better access to assistance and knowledge for smartphone use which may result in a stronger relationship between FC and UB for the younger people. These context-specific outcomes can be well considered as additions to the existing knowledge in the domain of technology adoption.

9. 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. So, the marketers are trying to explore the untapped segment at the BOP. There is business potential for smartphone producing companies if they can customize the smartphones to the needs of the BOP. Furthermore, the adoption of smartphones at the BOP may lead to a social change. If most of the people start using smartphone even at the BOP, the government can increase efficiency by delivering fast services to the citizens using smartphone-based applications. Financial organizations such as banks can enhance human resource productivity if an effective application such as mobile banking is adopted even by BOP customers. Therefore, the marketers and the policymakers need a better understanding of the inhibitors and enablers of smartphone adoption at the BOP. This study has contributed to this requirement by proposing relevant hypotheses and carrying out the empirical analysis. A limitation of this research is that the results of the study may not be readily generalized to the global BOP segments as the current study is carried in the context of Indian BOP people. There is a possibility that the outcomes may be affected by the presence of cultural differences in other countries. Therefore, future research should include a crosscountry comparison of similar studies.

8. 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 income. The results also show that “Effort Expectancy” of smartphone usage has a positive direct impact on “Behavioral Intention” to use which indicates that the effort required to use a smartphone should be minimized to improve the adoption rate. Though this indication may be generic to all segments, the managers should give special attention in the BOP segment as these people are characterized by low literacy, and they have little exposure in learning new technology. Therefore, the managers should concentrate on how to make the smartphone user-friendly and easy to use for the BOP segment. It requires collaboration and interactive sessions between the Appendix A. Measurement Scales based on literature and context

Construct

Measurement Scale

Source

Performance Expectancy

Using smartphone enables me to accomplish tasks more quickly (PE1). I believe that using smartphone would help me to communicate faster (PE2) Using smartphone increases my productivity (PE3) I would find smartphone useful in life (PE4) I would find smartphone easy to use (EE1) Learning to operate smartphone is easy for me (EE2) My interaction with smartphone would be clear and understandable (EE3) I think smartphone is user friendly (EE4) It would be easy for me to become skillful at using smartphone (EE5) People who are important to me think that I should use smartphone (SI1) People who influence my behavior think that I should use smartphone (SI2) Using smartphone increases respect in the society (SI3) I get assistance from others in difficulties of smartphone use (FC1) I have the knowledge necessary to use smartphone (FC2) Smartphone is not compatible with other systems I use (FC3) I have the resources necessary to use smartphone (FC4) I think smartphone is reasonably priced (PMV1) I think smartphone offers values for money (PMV2) I intend to use smartphone in future (BI1) I predict I would use smartphone in future (BI2) I plan to use smartphone in future (BI3) I use to explore smartphone (UB)

(Venkatesh et al., 2003) Contextual (Venkatesh et al., 2003) (Venkatesh et al., 2003) (Venkatesh et al., 2003) (Venkatesh et al., 2003) (Venkatesh et al., 2003) Contextual (Venkatesh et al., 2003) (Venkatesh et al., 2003) (Venkatesh et al., 2003) (Venkatesh et al., 2003) Contextual (Venkatesh et al., 2003) (Venkatesh et al., 2003) (Venkatesh et al., 2003) (Dodds et al., 1991; Kang & Maity, 2012; Kim et al., 2008) (Dodds et al., 1991; Kang & Maity, 2012; Kim et al., 2008) (Venkatesh et al., 2003) (Venkatesh et al., 2003) (Venkatesh et al., 2003) (Alshehri, 2012; Attuquayefio & Addo, 2014)

Effort Expectancy

Social Influence

Facilitating Conditions

Perceived Monetary Value Behavioral Intention

Use Behavior

10

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