Direct vs indirect e-government adoption: an exploratory study

Rajiv Kumar (Indian Institute of Management Ranchi, Ranchi, Jharkhand, India)
Amit Sachan (Indian Institute of Management Ranchi, Ranchi, Jharkhand, India)
Arindam Mukherjee (Indian Institute of Management Ranchi, Ranchi, Jharkhand, India)

Digital Policy, Regulation and Governance

ISSN: 2398-5038

Publication date: 12 March 2018

Abstract

Purpose

The purpose of this study is to investigate the factors that influence direct and indirect adoption of e-government services in India.

Design/methodology/approach

A conceptual model has been proposed by integrating the factors influencing adoption of e-government services from extant literature. A quantitative technique is used for the purpose of the study.

Findings

The study classifies e-government adoption in two types: direct adoption and indirect adoption. The study has found that there is some difference between the factors influencing direct and indirect e-government adoption. Perceived awareness, perceived usefulness, trust in internet, trust in government and social influence are found to be positively correlated to direct and indirect e-government adoption. Availability of resources, computer self-efficacy, perceived ease-of-use, perceived compatibility, multilingual option and voluntariness are positively correlated to direct e-government adoption and negatively correlated to indirect e-government adoption. Perceived image is found to be significant for direct e-government adoption but non-significant for indirect adoption. Trust in intermediary is found to be significant only for indirect e-government adoption.

Research limitations/implications

The sample size of 382 may not be a proper representation of a country like India, which has huge diversity and is densely populated. The study has been conducted in India, which is a developing country. The result might not be significant for developed countries.

Practical implications

The findings of this study provide useful insights into the decision-making process of e-government users in India and similar emerging economies. These findings can be important for government officials tasked with providing e-governance services.

Originality/value

Despite the digital divide, how the government is expecting its citizens to access e-government services and derive benefits and how the needy will be able to cope with the mandatory e-government services is an interesting topic to study. This leads to a new concept of indirect adoption.

Keywords

Citation

Kumar, R., Sachan, A. and Mukherjee, A. (2018), "Direct vs indirect e-government adoption: an exploratory study", Digital Policy, Regulation and Governance, Vol. 20 No. 2, pp. 149-162. https://doi.org/10.1108/DPRG-07-2017-0040

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

Information and communication technologies (ICTs) can serve multiple purposes from providing education to being a medium for accessing public services (Garcia-murillo and Velez-ospina, 2017). ICT has been shown to be an innovation enabler and one of the most important factors for economic growth in developed markets (Amiri and Woodside, 2017). A primary goal of United Nations, World Bank and other international organizations is to have digital equality in developing nations. Hence, governments in such developing countries are driving efforts focused on providing access to e-government services for their citizens. In India, through ICTs, government is enhancing its effectiveness and efficiency in delivering public services and empowering its citizens as well (Digital India, 2018; National E-Governance Plan, 2018). Framing e-government strategies and policies are complex exercises which encompass a variety of issues. In India, most of the state governments have taken initiatives in e-governance, which have resulted in varying degrees of success (Second Administrative Reforms Commission, Promoting E-Governance, 2008). Most of the states/union territories in India either already have an information technology policy in place or are in the process of finalizing one. Present government has announced INR 1 lakh crore (approximately US$15bn) investment for the Digital India program (Digital India week to bring investment worth billions of dollars, 2015).

Despite these efforts, reports have consistently shown that a majority of these initiatives fail (Success and Failure in eGovernment Projects, 2008). High failure rate of e-government projects brings severe direct and indirect financial costs. Further, it damages morale, credibility and trust, preventing the benefits of e-government from being delivered. One primary reason for failure is the lack of access by citizens to these online-delivered government services. E-government project failures could be due to various other factors. It is, therefore, important to understand the adoption from a citizen’s perspective.

In developing countries, inadequate resources and limited citizens’ awareness regarding new e-government services have resulted in low diffusion and adoption of e-government services (Al-sobhi and Weerakkody, 2010). Some citizens are bound to be excluded from benefiting from e-government services, creating a gap and inequality in accessing these e-government services because of limited access to internet and little exposure to associated ICTs (Weerakkody et al., 2013). Despite low internet penetration in India (34.8 per cent of total population) (India Internet Users, 2018) and low computer literacy (less than 18 per cent of total population were computer-literate in 2014) [National Sample Survey Office (NSSO), 2018], Government of India has made it mandatory that few government services can be offered online only. A question arises that how citizens, incapable of operating computer and without internet connectivity, can access these mandatory e-government services. It is also important to understand, despite the digital divide, how citizens are getting benefited from e-government services. In this regard, many countries worldwide have established solutions and strategies for increasing access to public services by effectively facilitating the use of information technologies (Phang et al., 2005). One of these strategies involves using third-party intermediary organizations to offer additional support to citizens, thus facilitating cadoption of e-government services (Bailey and Bakos, 1997; Weerakkody et al., 2013). Literature has classified these intermediary organizations in different forms, ranging from digital firms, such as Amazon, eBay and PayPal, to physical organizations, such as post office, travel agents and estate agents (Bailey and Bakos, 1997; Janssen and Klievink, 2009).

There are two categories of people who access public services through internet. One section accesses public services on their own directly from computer devices. They are capable of accessing government services electronically. There is another category of people who do not access e-government services directly. This category can be further classified into two. Some people are capable of operating computers and internet and have resources to access e-government services. However, they do not access these services themselves as they do not find accessing e-government services on their own to be beneficial. They generally take help of intermediaries who access these e-government services on their behalf. Another category of this type are not computer-literate and are unable to access public services electronically on their own. Such people also adopt e-government services indirectly through intermediaries or other mediums. Studies have argued that given the low literacy rate, the ability to indirectly use an e-government portal – i.e. get information from the portal with the help of a kiosk attendant who would use the portal, retrieve information and share it with the villager – was vital in making the portal accessible (Venkatesh et al., 2014). As an example, retail service providers (RSPs) are authorized agents who can book a train ticket on behalf of citizens, especially for those who are not able to book train ticket by themselves. This is an example of indirect adoption of one of the popular e-government services in India [Indian railway catering and tourism corporation limited (IRCTC), 2018]. A large number of citizens are getting benefits of online train reservation system through these RSPs.

As prior literature in the information systems and e-government realms show, there is a paucity of research that has carried out studies that investigate the indirect adoption of e-government services and explored if adoption behavior differs from the direct adoption of e-government services. As many researchers in the information systems field build their argument from a theoretical background (AlAwadhi and Morris, 2008; Carter and Belanger, 2005; Shareef et al., 2011; Viswanath Venkatesh et al., 2014), it is essential to present a practical model or framework that helps to understand the factors that affect citizens’ level of adoption for both directly and indirectly accessed e-government services. Also, given the increasing importance of intermediaries, it is important to explore adoption behavior of e-government services indirectly. This paper aims to explore indirect adoption of e-government services. It would be reasonable to expect that successful introduction and adoption of ICTs in e-government services would depend on many factors, including social and attitudinal factors. Are these factors the same for direct and indirect adoption? This study tries to answer this question.

2. Literature review

E-government adoption is a key area of research. Studies associated with e-government adoption have mainly focused on factors that impact citizens’ attitudes toward e-government (Al-Shafi and Weerakkody, 2010; Carter and Belanger, 2005; Rana et al., 2016; Shareef et al., 2011; Sharma et al., 2012; Venkatesh et al., 2014; Wangpipatwong et al., 2005). A primary condition for successful implementation of any information technology project is users’ acceptance and adoption (Weerakkody et al., 2013). Users’ attitude to use and adopt new technologies may determine the success or failure of such projects (Pinto and Mantel, 1990). Users’ acceptance of technology refers to the “initial decision made by the individual to interact with the technology” (Venkatesh et al., 2003). Numerous theories and models have been used to examine users’ adoption of information technology. For example, technology acceptance model (TAM) (Davis, 1989), diffusion of innovation (DoI) theory (Rogers, 1995), theory of reasoned action (TRA) (Fishbein and Ajzen, 1975), theory of planned behavior (Ajzen, 1985), motivational model (Igbaria et al., 1996), model of PC utilization (Thompson et al., 1991), social cognitive theory (Bandura and Cervone, 1986) and the most recent unified theory of acceptance and use of technology (UTAUT) (Viswanath Venkatesh et al., 2003) have been used. These models aimed to explain user behavior and usage of new technology with a variety of independent variables. The UTAUT model was proposed on the basis of similarities of the independent variables from the models cited above. Similar to technology adoption, there are proposed e-government adoption models that applied previous technology adoption models.

The literature on citizen adoption of e-government initiatives is somewhat fragmented. However, in recent years, researchers have begun to integrate approaches into models to identify significant factors and the relationships among factors, which influence the adoption of online government services by citizens. A major advantage of this technique is that the integration of approaches can reduce the limitations of the individual approach (Gilbert et al., 2004).

E-government adoption model by Gilbert and others (Gilbert et al., 2004) combines attitude-based constructs from DoI theory (Rogers, 1995) and TRA (Ajzen and Fishbein, 1980) with aspects of service quality from TAM theory (Davis, 1989). Attitude-based approaches are supported by the behavioral theory that links perceptions to user intentions so that they can be useful in linking attitudes to behaviors by combining attitude-based approaches with service-based approaches. The dependent variable in these models is the willingness of citizens to use e-government services, while independent variables are perceived relative benefits and perceived barriers in using e-government services. The perceived relative benefits variable includes the avoidance of personal interaction, control, convenience, cost, personalization and time. The perceived barriers variable includes confidentiality, ease-of-use, enjoyment, reliability, safety and visual appeal. This model includes age as a factor that can influence the adoption of e-government initiatives.

A comprehensive e-government adoption model developed by Carter and Belanger (2005) combines constructs from TAM (Davis, 1989), DoI theory (Rogers, 1995) and Web trust theory (McKnight et al., 2002). It combines compatibility, relative advantage, image and complexity from DoI theory (Rogers, 1995), perceived ease-of-use (PEOU) and perceived usefulness (PU) from TAM (Davis, 1989) and trust of internet and trust of government from Web trust theory (McKnight et al., 2002). This model can be applied to a wide range of e-government initiatives at local, state and federal levels.

3. Theoretical framework

As we have mentioned in the Introduction, the existing literature on e-government has not adequately presented a comprehensive framework for direct and indirect e-government adoption. We have tried to identify the constructs from our detailed literature review in conjunction with the insight from different theories related to DoI, technology adoption and behavioral, social and cultural characteristics. Consequently, this study is exploratory in nature. Rather than testing any specified theory of e-government adoption, we are conducting this research with the objective of developing a theory of direct and indirect e-government adoption. For such an exploratory study, refinement of variables and hypotheses is typical and also a part of the theory development process (Stevens, 2012). So, we continue to refine exogenous variables and hypotheses to develop final paradigms of direct and indirect adoption of e-government services. Table I lists the hypotheses for direct and indirect adoption. A model depicting the factors influencing adoption of e-government, both directly and indirectly, is presented in Figure 1.

4. Research methodology

4.1 Measures

To assess the research model proposed for this study, a questionnaire survey was used. Survey items were adapted from previous studies (Carter and Belanger, 2005; Carter et al., 2016; Davis, 1989; Gefen and Straub, 2000; Jarvenpaa and Staples, 2000; Moore and Benbasat, 1991; Pavlou, 2003; Shareef et al., 2011; Slyke et al., 2004; Vassilakis et al., 2005; Weerakkody et al., 2013) with modifications keeping in mind the context of e-government adoption in India. A five-point Likert scale (interval scale) was used to measure responses to the statements in the research questionnaire on a scale from 1 (strongly agree) to 5 (strongly disagree). Because English is not the first language of India and most Indians are not fluent in English, the questionnaire was also prepared in Hindi, which is the popular mother tongue spoken in India. Back translation was used, with the questionnaire translated from English to Hindi first and then from Hindi to English. The questionnaire consisted of 56 questions, including demographic queries. A pre-test was done using five researchers and three practitioners to improve the questions and enhance the comprehension of respondents before final distribution (Saunders et al., 2009).This pre-test resulted in a minor amendment to the wording in five questions.

4.2 Data collection

Both paper-based and online survey were used to collect data. To test the hypotheses by using a proper sample, the self-administered questionnaire was distributed randomly among 671 citizens across a broad diversity of citizens from several communities during the period from August 2016 to January 2017. We also personally met the respondents and requested them to fill the questionnaire. The selection of participants, from different geographical areas, was deemed essential to obtain increased generalizability of results. Data were collected from village, town, city, academic institutions and government and private organizations. Academic institutions were targeted because students with diverse background from all corners of the country study here. Similarly, government and private companies have employees from a diverse background. We received a total 439 responses; however, 57 of these were discarded because of incomplete answers. Ultimately, we used 382 responses for our statistical analysis, which indicates an effective response rate of around 87.02 percent.

4.3 Data analysis

We used factor and regression analyses to analyze our data. The specific tool we used was SPSS (version 20). We verified the sample’s representativeness by demographic analysis, as shown in Table II. As ICT behavior and e-government usage is a relatively new trend in a developing country like India, the sample shows that interested respondents are relatively young, which is logically acceptable.

Identifying influencing factors for indirect adoption of e-government services is a new area. As the nature of the study is exploratory, we revised the adopted questionnaire. Scales were also modified accordingly to suit the Indian context. Reliability and validity were assessed for the multi-item scales variables. Reliability was assessed based on Cronbach’s α values (Cronbach, 1951). All constructs were found to be reliable (Cronbach’s α > 0.70) (Table III). Cronbach’s α values were chosen to examine the internal consistency of the collected data. We have conducted exploratory factor analysis (EFA). Those items which were loaded less than 0.40 or cross-loaded more than one factor were removed (Stevens, 2012). Table III shows the loading and cross-loading from a factor analysis (varimax rotation). All loadings were greater than 0.60, and cross-loadings were below 0.35, supporting internal consistency and discriminant validity.

EFA analysis retained all the 13 independent constructs as the pursuing factors of e-government service adoption. Among the 56 measuring items, items AOR1, TG2, TOIR2, TOIR4, and PI3 were removed. The remaining measuring items were loaded according to the definitions of the respective constructs. Finally, we retained 13 constructs with 51 measuring items.

4.4 Results

Table IV shows the results of the model testing. The various determinants well predict e-government portal use. The results show that only H7b was not supported. The model explains 77.27 per cent of the variance in citizen adoption of e-government. Because the overall model is significant (p = 0.000), significance of each variable on e-government adoption (both direct and indirect) was tested. Table IV illustrates which hypotheses were supported.

5. Findings and discussion

Researchers and practitioners have paid little attention to the factors related to a citizen’s adoption of e-government initiatives. Moreover, a majority of e-government studies have focused on developed countries such as the USA and UK (Bélanger and Carter, 2012; Carter et al., 2016). As stated earlier, there is a paucity of research discussing influencing factors for indirect adoption of e-government services. As intermediaries play an important role in reducing the digital divide, adoption of e-government services through intermediaries (i.e. indirect adoption) is an important phenomenon that requires further understanding. This study has taken a step forward in this regard. This study did a comparative analysis of the factors that influence direct and indirect adoption of e-government services. We also discussed prior research on e-government adoption and also extended the discussion on ICT adoption and use in developing countries (Ahmad et al., 2013; Bélanger and Carter, 2012; Carter, 2008; Liang and Lu, 2013; Rehman et al., 2012; Shareef et al., 2011; Weerakkody et al., 2013).

The results depict that when citizens are aware that there exists an alternative to the physical mode of delivering of government services, such as e-government, they might be interested in it. Perceived awareness (PA) about e-government services and its benefits influences citizens’ intention to adopt this mode of service. If e-government services are perceived to be advantageous, citizens will most likely adopt it. Further, if they have resources and sufficient technological and psychological ability to use it, they will access it directly. Otherwise, they try to adopt it indirectly via intermediaries. Hence, PA is found to be significant for both direct and indirect adoption. On the other hand, availability of resources (AOR) and computer self-efficacy (CSE) are found to be significant for direct adoption and negatively significant for indirect adoption.

When citizens perceive that accessing e-government services is useful, they use it either directly or indirectly through intermediaries. System acceptance will suffer if users do not perceive a system to be useful and easy to use (Davis, 1989). Hence, PU is a significant predictor of direct and indirect e-government adoption.

Multilingual option (MLO) could be a major issue for citizens, whose first language is not English, in accessing e-government websites. The result also shows that MLO is significant for both direct and indirect adoption. MLO is negatively significant for indirect adoption because citizens who are not able to understand a website’s language may approach intermediaries. However, from demographic analysis, we found that almost 51.30 per cent of the respondents have their vernacular different from English and Hindi. Most of the Indian government web pages, especially central government websites, are written in English; some are also in Hindi. However, some state government websites are designed in their respective local language. Hence, it was found that language is an influencing factor in adopting e-government services, both directly and indirectly. Results show that MLO is significant for direct adoption and negatively significant for indirect adoption of e-government services. A similar argument is drawn for PEOU.

We also find that perceived image (PI) influences the direct adoption and use of e-government, whereas it does not influence the indirect adoption of e-government. As discussed, PI refers to citizens’ perception that adopting e-government services portray them superior to others in the society. Direct interaction with e-government systems, instead of using traditional government offices or adoption through intermediaries, reflects a perception of superior status. This is also supported by DoI and theory proposed by Moore and Benbasat (1991). However, adoption of e-government services indirectly, i.e. through intermediaries, does not reflect any superiority to others in the society. Therefore, PI was not found to be significant for indirect adoption of e-government services.

Results show that higher levels of perceived compatibility (PC) is associated with increased intentions to adopt e-government directly. Compatibility construct has cultural, behavioral and social aspects. It is dependent on individual characteristics, such as avoiding personal interaction and social influence (SI) (Shareef et al., 2011). Several researchers have indicated that specific characteristics of e-governance that allow citizens to avoid personal interaction might create the perception of compatibility among citizens while adopting an e-government system (Gilbert et al., 2004). However, PC was found to be negatively significant for indirect adoption of e-government. We argue that individuals not compatible with e-government services will either use conventional mode of accessing e-government services or access e-government services through intermediaries. However, government offices are limited by their office hours. Indian government offices are less easy to access, besides the involvement of corrupt practices. Citizens tend to avoid these limited office hours and corrupted general atmosphere. Hence, they may reach out to intermediaries to access e-government services indirectly.

Trust in intermediary (TIR) was found to be positively significant with indirect adoption and insignificant for direct adoption of e-government services. Study of Weerakkody et al. (2013) revealed that building trust in an intermediary would be closely relevant to e-government services adoption. Citizens need to submit their personal information to the e-government portal through an authorized third-party organization, which is an intermediary (Al-Sobhi et al., 2010a, 2010b). It is therefore critical toward indirect adoption that intermediaries perform their jobs honestly, giving utmost importance toward safety and security of personal information.

Trust in internet (TI) and trust in government (TG) are found to be significant for both direct and indirect adoption of e-government services. Citizens who perceive the reliability and security of the internet to be low will be less likely to adopt e-government services either directly or indirectly. The direct adopters and indirect adopters are well aware of internet-related risk through news channels, peers or from other source (s) of information. Hence, they are concerned about internet risk, and therefore, TI becomes important. Through accurate and consistent delivery of services, government must assure citizens that e-government is both safe and beneficial. Government must take necessary steps to provide a robust and secure infrastructure backbone and convey the steps taken through multiple communication channels to assure citizens that online transformations are safe and secure for them. Respondents have expressed that TI and TG are significant predictors of e-government adoption. Privacy and security are critical issues in e-government adoption (Belanger and Janine, 2006; Carter and Belanger, 2005). Hence, fear of misuse of data and threat to their privacy may restrain citizens from using e-government service either directly or indirectly.

The findings of this study suggest that SI plays an important role in determining the direct and indirect acceptance and usage behavior of e-government. SI is found to be positively significant for both direct adoption and indirect adoption of e-government services. The study by Fishbein and Ajzen (1975) argued that SI is the determinant of behavioral intention. SI has a direct effect on intention because people may choose to perform a behavior, even if they are not inclined toward this behavior or its consequences. If citizens believe one or more important referents and think they should access e-government services, then they are sufficiently motivated to comply with the referents (Venkatesh and Davis, 2000).

Voluntariness (VO) is found to be positively significant for direct adoption and negatively significant for indirect adoption of e-government services. This finding of VO being positively significant for direct adoption is consistent with previous studies (Agarwal and Prasad, 1997; Karahanna et al., 1999). The influence of VO on the intention to use is also similar to previous findings, which noted the perception of freedom of choice has a positive effect on intention to use. On the other hand, inverse significance of VO for indirect adoption indicates that if certain government services are mandatorily made available only through online, users who are incapable or have a lack of resources to access e-government services will be forced to access these services through an intermediary. The argument is also supported by earlier research by Karahanna et al. (1999), who stated that “the less voluntary the behavior, the less one’s attitude toward usage predicts use”.

In addition to extending knowledge in this area, our study is one of the first to provide a comprehensive model regarding the adoption of e-government services, both directly and indirectly. We found support for our model in some variables that were found significant for direct adoption; some other variables were inversely significant for direct adoption. Moreover, we found few variables to be significant for indirect adoption, and few other variables negatively influenced indirect adoption of e-government services. The variance explained is 77.27 per cent. The significance of the predictors and the magnitude of variance explained suggest that our model provides a good explanation of e-government adoption.

6. Conclusion

The objective of this research was to understand factors that influence citizens to use e-government services directly and indirectly. Specifically, we hypothesized 13 adoption factors as predictors of e-government services use. Our study on Indian citizens supported our proposed model. Our work explores knowledge regarding the factors that are related to using e-government portal indirectly. As our study focuses on a developing country, i.e. India, this research not only contributes to and extends previous research on e-government but also has significant implications for research about the digital divide and ICT use in developing countries. As many governments, especially in developing countries, around the world are increasingly implementing ICT-based initiatives, our study is timely and provides insights that could drive the success of ongoing initiatives to bridge the digital divide.

The findings can be put to use by the practicing managers for reducing the digital divide between rural and urban areas in developing countries. The discussion and recommendations presented in this paper would be valuable for various agencies, both from public and private sectors, as well as policy-makers, for the effective implementation of e-government services and bridging the digital divide. The approach discussed in this paper offers an effective way to diffuse e-government applications and services in other developing countries, particularly in ICT resource-constrained nations.

7. Limitations and directions for future research

As an adequate empirically supported research is not available, this study is at an exploratory level. Thus, limitations of exploratory research apply to this study. It has several other limitations. First, the sample size of 382 may not be a proper representation for a country like India, with huge diversity and having a population of around 1.25 billion. Though we have considered a sample that is representative of the target population, certain inherent bias could not be avoided, despite the best efforts of the researcher. Though India is a good place to study the e-government adoption behavior, this study can be replicated in several other countries to generalize the study and to validate the theory.

We have developed our theoretical framework considering the general aspects of a developing country. As a result, we have predicted some exogenous (explanatory) variables (AOR, CSE and TIR), which might not be significant for developed countries. However, these might have enormous value for developing countries. Also, MLO may not be significant for countries having a single language. Therefore, for generalizing the model, this study should be conducted in some other developed countries.

Figures

Proposed e-government adoption model

Figure 1

Proposed e-government adoption model

List of proposed hypotheses

Explanatory variable Hypothesis Literature support
PA H1a. PA has a positive correlation with the direct e-government adoption
H1b. PA has a positive correlation with the indirect e-government adoption
Parent et al. (2005), Rehman et al. (2012), Shareef et al. (2011)
AOR H2a. AOR has a positive correlation with the direct e-government adoption
H2b. AOR has a negative correlation with the indirect e-government adoption
Dijk et al. (2008), Rehman et al. (2012), Shareef et al. (2011)
CSE H3a. CSE has a positive correlation with the direct e-government adoption
H3b. CSE has a negative correlation with the indirect e-government adoption
Rana et al. (2015), Shareef et al. (2011)
PEOU H4a. PEOU has a positive correlation with the direct e-government adoption
H4b. PEOU has a positive correlation with the indirect e-government adoption
Davis (1989)
PC H5a. PC has a positive correlation with the direct e-government adoption
H5b. PC has a negative correlation with the indirect e-government adoption
Rogers (1995)
PU H6a. PU has a positive correlation with the direct e-government adoption
H6b. PU has a positive correlation with the indirect e-government adoption
Davis (1989)
PI H7a. PI has a positive correlation with the direct e-government adoption
H7b. PI has a positive correlation with the indirect e-government adoption
Rogers (1995)
MLO H8a. MLO has a positive correlation with the direct e-government adoption
H8b. MLO has a negative correlation with the indirect e-government adoption
Shareef et al. (2011)
TI H9a. TI has a positive correlation with the direct e-government adoption
H9b. TI has a positive correlation with the indirect e-government adoption
Carter and Belanger (2005), McKnight et al. (2002)
TG H10a. TG has a positive correlation with the direct e-government adoption
H10b. TG has a positive correlation with the indirect e-government adoption
Carter and Belanger (2005), McKnight et al. (2002)
TIR H11.TIR has a positive correlation with indirect e-government adoption Weerakkody et al. (2013)
SI H12a. SI has a positive correlation with the direct e-government adoption
H12b. SI has a positive correlation with the indirect e-government adoption
Venkatesh et al. (2003)
VO H13a. VO has a positive correlation with the direct e-government adoption
H13b. VO has a negative correlation with the indirect e-government adoption
Agarwal and Prasad (1997), Moore and Benbasat (1991)

Descriptive statistics of demographic information

Demographic variables Frequency (%)
Gender
Female 167 43.72
Male 215 56.28
Age (years)
18-24 88 23.04
25-34 121 31.67
35-44 112 29.31
45-60 61 15.97
Education
High school 63 16.49
Under-graduate 112 29.32
Graduate 124 32.46
Post-graduate 83 21.73
Internet experience
<1 year 123 32.20
1-3 years 70 18.32
3-5 years 43 11.26
5-7 years 82 21.46
>7 years 64 16.75
E-government adoption type
User (direct) 122 31.94
User (indirect) 90 23.56
Non-users 170 44.50
First language
English 25 06.54
Hindi 160 41.89
Other 197 51.57

Reliability and factor loadings and cross-loadings (EFA)

Component
1 2 3 4 5 6 7 8 9 10 11 12 13
AOR2 0.763
AOR3 0.830
AOR4 0.789
CSE1 0.782
CSE2 0.843
CSE3 0.642
PEOU1 0.838
PEOU2 0.847
PEOU3 0.819
PEOU4 0.797
VO1 0.827
VO2 0.844
VO3 0.842
VO4 0.782
TIR1 0.817
TIR3 0.865
TIR5 0.865
TIR6 0.692
PU1 0.690
PU2 0.876
PU3 0.864
PU4 0.789
MLO1 0.844
MLO2 0.882
MLO3 0.887
PI1 0.702
PI2 0.778
PI4 0.814
TI1 0.769
TI2 0.787
TI3 0.725
PA1 0.839
PA2 0.700
PA3 0.719
PA4 0.671
PC1 0.780
PC2 0.801
PC3 0.814
SI1 0.746
SI2 0.788
SI3 0.632
TG1 0.649
TG3 0.669
Cronbach’s α 0.871 0.888 0.899 0.867 0.873 0.878 0.844 0.828 0.806 0.776 0.828 0.735 0.776

Note: Loadings less than 0.40 are not shown

Predicting e-government adoption (direct and indirect)

Explanatory variable Direct adoption Indirect adoption
Hypothesis Beta value p-value Results Hypothesis Beta value p-value Results
PA H1a 0.419 0.000 S (+) H1b 0.389 0.000 S (+)
AOR H2a 0.756 0.000 S (+) H2b −0.311 0.000 S (−)
CSE H3a 0.548 0.000 S (+) H3b −0.356 0.000 S (−)
PEOU H4a 0.547 0.000 S (+) H4b −0.285 0.000 S (−)
PC H5a 0.698 0.007 S (+) H5b −0.231 0.000 S (−)
PU H6a 0.322 0.000 S (+) H6b 0.314 0.000 S(+)
PI H7a 0.185 0.015 S (+) H7b 0.158 NS
MLO H8a 0.175 0.000 S (+) H8b −0.217 0.000 S (−)
TI H9a 0.197 0.002 S (+) H9b 0.186 0.006 S (+)
TG H10a 0.578 0.000 S (+) H10b 0.199 0.013 S (+)
TIR NA H11 0.473 0.000 S (+)
SI H11a 0.154 0.000 S (+) H12b 0.781 0.000 S (+)
VO H12a 0.208 0.024 S (+) H13b 0.183 0.000 S (−)

Notes: S (+): positive significant; S (−): negative significant; NS: non-significant; and NA: not applicable

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Corresponding author

Rajiv Kumar can be contacted at: rajiv.kumar13fpm@iimranchi.ac.in

About the authors

Rajiv Kumar is a Doctoral Candidate at the Indian Institute of Management Ranchi, Ranchi, Jharkhand, India

Amit Sachan is Assistant Professor at the Indian Institute of Management Ranchi, Ranchi, Jharkhand, India.

Arindam Mukherjee is Assistant Professor at the Indian Institute of Management Ranchi, Ranchi, Jharkhand, India.