Online service quality and perceived value in mobile government success: An empirical study of mobile police in China

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

Online service quality and perceived value in mobile government success: An empirical study of mobile police in China Changlin Wanga,*, Thompson S.H. Teob a b

Dept. E-Commerce, School of E-Commerce & Logistics Management, Henan University of Economics and Law, 180 Jinshui East Road, Zhengzhou City, 450046, PR China Dept. Analytics & Operations, BIZ, NUS, Office: BIZ1 8-75, 15 Kent Ridge Drive, 119245, Singapore

A R T I C LE I N FO

A B S T R A C T

Keywords: Mobile government success Service quality Perceived value Citizen satisfaction Online service quality Offline service quality

Measuring the success of mobile government (m-government) is a significant challenge faced by the public sector today, as governments are increasingly using mobile technology to provide public services to citizens and mgovernment endeavors have often fallen short of their potential. To address this gap, we draw on DeLone and McLean’s (2003) updated information systems (IS) success model in order to develop an m-government success model that theorizes service quality as comprising online and offline service quality and further uses perceived value to measure net benefits. The results of a survey of 286 m-government users in China indicate that information quality and online service quality, but not system quality, are positively associated with citizen satisfaction, which in turn is positively associated with perceived value. The results also show that the relationship between online service quality and citizen satisfaction is positively moderated by offline service quality, while citizen satisfaction partially mediates the relationships between information quality/online service quality (but not system quality) and perceived value. This study extends the updated IS success model by differentiating between online and offline service quality, as well as by introducing the notion of perceived value. Our results provide guidance to researchers and practitioners regarding the role of service quality and perceived value in measuring m-government system success.

1. Introduction The measurement of information system (IS) success has garnered significant attention from researchers and practitioners for many years (Rana, Dwivedi, & Williams, 2013; Iannacci & Cornford, 2018; Rana, Dwivedi, & Williams, 2013; Sabherwal, Jeyaraj, & Chowa, 2006). While some authors have focused on how to form an IS success model for measuring general IS (DeLone & McLean, 1992, 2003; Seddon, 1997; Petter, DeLone, & McLean, 2008). Others have focused on how the updated IS success models are used in specific areas, such as e-commerce success (Delone & Mclean, 2004; Wang, 2008), m-commerce success (Zhou, 2013), and e-government success (Scott, DeLone, & Golden, 2016; Teo, Srivastava, & Jiang, 2009). Although various aspects related to quality have been examined in the m-government context (see review in Appendix A), this has not been done using the updated IS success model. Consequently, our study adapts the updated IS success model to the m-government context, which need not necessarily be the same as the e-commerce and e-government contexts. Governments around the world are actively promoting m-government. M-government can be defined as the strategy utilized by a

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government to provide information and services to stakeholders (e.g. employees, citizens, businesses, and other organizations) via mobile technology and devices without restrictions of time and place (Ishmatova & Obi, 2009). Due to the mobility, identification and personalization advantages offered by m-government, users can overcome time and space limitations. First, it is convenient for citizens to access information from m-government services 24/7 in a timely manner. Moreover, m-government can provide users with personalized services, facilitate user participation, and enhance the interaction between the government and citizens (Trimi & Sheng, 2008). Despite this, however, the effects of m-government use have mostly fallen short of their potential in a similar way to e-government (Vincent & Harris, 2008). This problem is even more pronounced in the context of developing countries, with only about 15 % of e-government initiatives successfully achieving their key goals without any major adverse consequences (Teo et al., 2009). Therefore, measuring the effectiveness of m-government is a significant challenge for government departments. As mentioned above, although DeLone and McLean’s (2003) updated IS success model is most commonly used to measure the success of a variety of Internet-based systems, such as e-commerce, m-

Corresponding author. E-mail addresses: [email protected] (C. Wang), [email protected] (T.S.H. Teo).

https://doi.org/10.1016/j.ijinfomgt.2020.102076 Received 24 July 2019; Received in revised form 15 January 2020; Accepted 15 January 2020 0268-4012/ © 2020 Elsevier Ltd. All rights reserved.

Please cite this article as: Changlin Wang and Thompson S.H. Teo, International Journal of Information Management, https://doi.org/10.1016/j.ijinfomgt.2020.102076

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experience in O&O services is increasingly valued by service providers (Leung, Wu, Ip, & Ho, 2019). Finally, although existing research has identified the difference between a business IS (e.g., e-commerce) and a government IS (e.g., e-government), this differentiation is less important when using net benefits to measure their success. We provide new insights by using perceived value to measure net benefits in order to reflect the balance of costs and benefits in the mobile technology environment. Furthermore, perceived value (similar to public value) is measured from the public sector perspective. The remainder of this paper is structured as follows. First, we review extant research on m-government, IS success, service quality, and perceived value, and outline our research model and hypotheses. Second, we describe our method and report our empirical results. Finally, we discuss the implications of our results and conclude the paper.

commerce, and e-government, there is no empirical research measuring m-government success based on this updated model. Given that DeLone and McLean’s model (2003) is recognized as a basic framework for measuring IS success (Scott et al., 2016), and that we are examining the role of online and offline service quality in our research model, we here use the DeLone and McLean updated model (2003) rather than the DeLone and McLean model (1992). Measuring the success of an mgovernment system is of great significance in theory and practice. In theory, m-government is different from m/e-commerce because mgovernment frequently encompasses social goals (e.g., enhancing social justice, advancing public value, and promoting the sustainable development of society) (Grimsley & Meehan, 2007); moreover, it can also provide personalized and localization-based services (Wang, 2014), which may change the relevant variables (e.g., service quality related to the mobile context) and the relationships between them in the updated IS success model. Increasingly, governments are providing public services to citizens through online and offline (O&O) channels. Correspondingly, it is important to consider both offline service quality (e.g., Lee, Kim, & Ahn, 2011) and online service quality (Rana, Dwivedi, Williams, & Weerakkody, 2016; Shareef, Archer, & Dwivedi, 2015). Only minimal research has paid attention to the role of offline service quality in citizens’ use of online public services; moreover, the emergence of O&Obased public services has also brought challenges to the updated IS success model, as the construct of service quality in the updated IS success model is a single-dimensional variable. Last but not least, the construct of ‘net benefits’ is too general and needs to be defined in terms of a specific context (Scott et al., 2016). For example, continuance intention is often used to measure IS success in the extant research (e.g., Teo et al., 2009). Although both government IS and business IS have similar goals as regards creating value for customers/citizens, there are some differences in their application goals. The former mainly focuses on the public interest (e.g., social equity, social sustainable development), while the latter focuses mainly on profit and improving output efficiency (e.g., cost reduction, market share) (Grimsley & Meehan, 2007; Perry & Rainey, 1988; Scott et al., 2016). Given that government IS and business IS encompass different strategic goals, we also need to conduct more research into whether other variables (such as the value of perceived public services) can be used as dependent variables when measuring the success of government IS. It is therefore necessary to extend and validate the updated IS success model to encompass the mgovernment context. In practice, government agencies around the world have embraced the digital revolution and increased investment in m-government. By evaluating m-government success, the key factors associated with usage can be identified, which will help the government to improve both the system design and the efficiency of service delivery. Hence, it is necessary to assess m-government success and extend IS success research into m-government so that we can improve mgovernment practices for government agencies. The purpose of this study is to develop a contextual model based on the updated DeLone and McLean model (DeLone & McLean, 2003) in order to measure m-government success. In doing so, we make three key contributions to our understanding of the updated IS success model in the m-government context. First, although assessing m-government success is a significant challenge faced by the public sector, there has been no empirical research on m-government success to date. We accordingly offer a framework that extends IS success studies into the mgovernment context and test this framework using survey data. Second, although the government is increasingly inclined to provide public services to citizens through O&O channels, it is not clear how O&O service quality affects IS success. Thus, we refine the connotation of service quality from the perspective of service channels – namely, online service quality (ONQ), and offline service quality (OFQ) – and explore their effects on m-government success. Note that OFQ is used to moderate the relationship between ONQ and citizen satisfaction, thereby facilitating an understanding of why the offline service

2. Theory development In this section, we provide an overview of the literature on mgovernment, the IS success model, service quality, and perceived value. Subsequently, we develop the research model and outline the hypotheses. 2.1. M-government services In recent years, governments throughout the world have made great efforts to develop e-government, and have achieved great success in doing so (Rana, Williams, Dwivedi, & Williams, 2011; Seifert & Chung, 2009). At the same time, there has been a substantial amount of empirical research into e-government. In brief, these studies focus primarily on three main topics. The first of these is e-government adoption. These studies are mainly concerned with explaining the factors that influence citizen adoption of e-government from different theoretical perspectives (e.g., Alryalat, Dwivedi, & Williams, 2013; Weerakkody, El-Haddadeh, Al-Sobhi, Shareef, & Dwivedi, 2013; Simintiras, Dwivedi, & Rana, 2014; Alryalat, Rana, & Dwivedi, 2020; Rana & Dwivedi, 2015). The second of these is e-government success, which explores the factors that affect citizens’ continued use of e-government (Chan et al., 2010; Teo et al., 2009). The third category pertains to e-government service quality (e.g., Tan, Benbasat, & Cenfetelli, 2013; Shareef et al., 2015), satisfaction (e.g., Magoutas & Mentzas, 2010), and trust (Janssen, Rana, Slade, & Dwivedi, 2018). These studies focus on the antecedents and/or consequences of these variables, or the relationships among these variables (e.g., Santa, MacDonald, & Ferrer, 2019). In this digital era, reinventing government systems in order to deliver efficient and cost-effective services to citizens and businesses via information and communication technologies (ICT) is a significant challenge faced by governments worldwide (Fang, 2002). Governments are increasingly inclined to use m-government as a means of promoting openness, transparency, and accountability in order to build trustworthy government (Bertot, Jaeger, & Grimes, 2010; Shareef, Archer, & Dwivedi, 2012; Shareef, Dwivedi, Kumar, & Kumar, 2017). M-government incorporates the characteristics of mobility, personalization and location, which enables it to provide citizens with time-critical, location-sensitive, and personalized services. Consequently, m-government has significant advantages over e-government approaches based on cable Internet (Wang, 2014). There is thus increased investment in mgovernment systems that allow governments to deliver public services to citizens using multiple channels. Previous studies have focused primarily on the factors that drive users to adopt m-government, as well as the key success factors for m-government. Research in the former category has largely focused on m-government adoption (Hung, Chang, & Kuo, 2013; Shareef, Kumar, Dwivedi, & Kumar, 2016; Shareef, Dwivedi, Laumer, & Archer, 2016; Shareef, Dwivedi, & Kumar, 2016), with relatively few studies being conducted on m-government continuance (e.g., Wang, 2014; Li, Yang, Chen, & Yao, 2018). Regarding the latter 2

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(1995) asserted that SERVQUAL was an appropriate instrument for use by researchers in measuring IS service quality. These authors assessed SERVQUAL measures in five dimensions (i.e., tangibles, reliability, responsiveness, assurance, and empathy) in three different types of organizations in three countries. Thereafter, service quality has become a major area of attention for IS researchers and practitioners, as it is now considered to be an important construct and has been introduced into the IS success model (DeLone & McLean, 2003; Seddon, 1997). In general, researchers recognize that there are different perspectives regarding service quality in different IS contexts. Specifically, there are six main perspectives on service quality in existing literature: namely, transcendent, societal loss, product-oriented, user-oriented, manufacturing-oriented, and value-oriented (Tan et al., 2013). With reference to extant studies, Tan, Benbasat, and Cenfetelli proposed a user-oriented conception of e-government service quality that incorporated both e-government service content and service delivery. As the public sector increasingly uses m-government to serve citizens, mgovernment service quality has become more and more important. Appendix A summarizes quality-related constructs in the m-government context and presents the following results. First, these studies are useroriented, and most studies use empirical research methods. Second, the quality-related constructs of interest are information quality (INQ), system quality (SYQ), service quality (SEQ), and integration service quality (IQQ); these constructs are positively associated with intention to use. Third, past research has not examined online quality and offline quality separately in the m-government context. Finally, only two studies clearly point out the dimensions of service quality. For example, SEQ is a multi-dimensional construct, which can be divided into interaction quality, environment quality, INQ, SYQ, network quality, and outcome quality (Al-Hubaishi, Ahmad, & Hussain, 2017); accordingly, the four dimensions of SEQ are connectivity, interactivity, authenticity, and understandability, respectively (Shareef, Dwivedi, Stamati, & Williams, 2014). To some extent, these categories embody the technical characteristics of m-government SEQ. However, one significant advantage of m-government is that it compels the public sector to provide services for citizens via online and offline channels. For example, a tax authority can not only provide online tax services for users through m-tax, but can also allow users to make an appointment online and then conduct on-site services. Prior research has indicated that online purchase behavior is different from offline behavior (Hult, Sharma, Morgeson, & Zhang, 2019); more specifically, online channels are more convenient, more flexible and make it easier to compare products and prices, while offline channels prompt purchasers to physically check a product and to get into personal contact with a seller, as well as creating an immersive experience (Grewal, Iyer, & Levy, 2004). In the m-government context, online access to public services tends to be more efficient, cheaper, easier and faster compared to offline access. However, offline access tends to involve human interaction, features more adequate communication, and makes citizens feel more real and safe (Lee et al., 2011). In addition, public sectors increasingly use both online and offline channels to provide services to citizens (Meijer, 2011). Consequently, we here divide service quality into two dimensions: namely, online service quality (ONQ) and offline service quality (OFQ). ONQ refers to the degree and direction of discrepancy between citizens’ perception and expectation when they access m-government services via online channels, while OFQ refers to the degree and direction of discrepancy between citizens’ perceptions and expectations when they access m-government services via offline channels. Currently, most research into IS service quality (e.g., Teo et al., 2009; Scott et al., 2016) actually refers to online service quality, while some studies focus on the impact of the integration of online and offline service quality on the adoption of IS systems (Gallino & Moreno, 2014; Li et al., 2018; Shen, Li, Sun, & Wang, 2018). However, there are still conflicting conclusions regarding the impact of offline service quality on users’ intention to use. For example, Lee et al. (2011) argued that offline service quality is negatively related to

category, however, there has been limited research on m-government success (Faisal & Talib, 2016; Wirtz & Birkmeyer, 2018). In fact, to the best of our knowledge, there is no empirical research measuring mgovernment success based on the updated IS success model. Following consideration of a reviewer’s suggestion, and considering that the object of our empirical study is m-police, we have here reviewed the literature related to m-police. In more detail, existing research focuses on the design and application of m-police systems (Zahabi & Kaber, 2018), security strategies for m-police (He, Qin, Wang, Chang, & Qin, 2009), and the impact of the application of mpolice on the transmission of information between police departments (Allen, Wilson, Norman, & Knight, 2008). Further analysis indicates that the key focus of these studies is the design of m-police and the application of m-technology in the police department. Fewer studies have been concerned with the use of m-police, with the exception of two prominent examples. The results of the first study, which used an ethnographic approach, indicate that the use of m-technologies enables information and knowledge to be shared more quickly with officers (Lindsay, Cooke, & Jackson, 2009). The other study proposed an mtechnology acceptance model (TAM) developed by a single police force (Lindsay, Jackson, & Cooke, 2014). It should be noted here that these two studies mainly focused on the initial adoption stage of m-police and did not involve the post-adoption stage. 2.2. IS success model Based on a comprehensive review of IS success literature, DeLone and McLean (1992) identified six factors of IS success: system quality, information quality, use, user satisfaction, individual impact, and organizational impact. The DeLone and McLean IS success model is one of the most widely cited frameworks in the IS discipline (Lowry, Karuga, & Richardson, 2007). Based on the DeLone and McLean (1992) model, Seddon (1997) proposed an alternative model that focused primarily on use and variables related to impact. He suggested that perceived usefulness should replace use, and further added social impact to measure the impact of IS use. Subsequently, based on the IS success studies conducted during the period from 1993 to 2002, DeLone and McLean (2003) proposed an updated IS success model by adding service quality and by using net benefits to measure the grouping of all the ‘impact’ variables. In the years since its publication, this updated model of IS success has been used to measure a variety of Internet-based systems success, including that of commercial (Wang, Wang, & Liu, 2016) and governmental IS (Floropoulos, Spathis, Halvatzis, & Tsipouridou, 2010; Rana, Dwivedi, Williams, & Weerakkody, 2015; Wang & Liao, 2008). The creation and selection of success variables is key to the effective use of the IS success model (Petter, DeLone, & McLean, 2012). There is a continuing need to develop measures in order to assess the success of new IS aiming to efficiently deliver services and appropriately measure both hedonic and utilitarian benefits (Petter et al., 2008). For example, service quality in this model is mostly considered as an overall construct, meaning that multi-channel effects (e.g. online versus offline modes of delivery) are neglected. Furthermore, the construct of net benefits in the model is conceptually too broad to measure, and is thus frequently measured by continued usage (e.g., Teo et al., 2009, 2009; Wang, 2008). Although continued usage can reflect net benefits to a certain extent, this construct is inadequate for the purpose of promoting the use of m-government. Accordingly, we will attempt to address these issues in the next two sections. 2.3. Service quality Service quality refers to the degree and direction of discrepancy between the perceptions and expectations of consumers (Parasuraman, Zeithaml, & Berry, 1988). Given that service quality has a strong impact on business performance and that the measurement of IS effectiveness is focused on products rather than services, Pitt, Watson, and Kavan 3

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intention to use, while Cheng, Fu, and de Vreede (2018) suggested that offline service quality is positively related to satisfaction and loyalty. These conflicting conclusions may indicate that offline service quality should be a contingent variable that regulates online service quality and dependent variables (e.g., willingness to use, loyalty, and value). Hence, we use offline service quality as a moderator in our research.

discrepancy between citizens’ perceptions and expectations of an mgovernment system; citizen satisfaction refers to the degree of citizens’ perception of satisfaction with m-government; finally, perceived value refers to a citizen’s overall perception of m-government services based on the considerations of its benefits, along with the sacrifices needed to acquire and/or use it.

2.4. Perceived value

2.5.1. Information quality and citizen satisfaction Information quality, which focuses on the IS content issue, is measured based on the characteristics of the actual information generated by the IS, as well as the extent to which the information product meets user needs in terms of accuracy, timeliness, reliability, relevance, integrity, and simplicity (Floropoulos et al., 2010). Hence, high-quality information is personalized, complete, relevant, easy to understand, and secure; moreover, it is a key consideration if IS suppliers want users to access transactions from the IS (Delone & Mclean, 2004). Previous work suggests that information quality is positively related to citizen satisfaction in e-commerce (Wang, 2008), m-commerce (Chatterjee, Chakraborty, Sarker, Sarker, & Lau, 2009), and e-government (Wang & Liao, 2008). In the m-government context, the services provided by governments can be divided into informational and transactional services (Venkatesh, Chan, & Thong, 2012); moreover, information services are key to governmental IS, as access to information is the most common reason why citizens use an e-government website (Teo et al., 2009). Moreover, due to the technological trait of mobility in m-government, citizens can access information in a timely fashion; in addition, due to the incorporation of GPS technology, citizens can be easily located and provided with personalized information. At the same time, an individual’s SIM card is exclusive, which improves information security. All these advantages of m-government improve information quality and may increase citizen satisfaction. Hence, we present the following hypothesis:

Perceived value refers to the tradeoff between quality and price (e.g., high quality or low price) (Zeithaml, 1988). This variable is increasingly important because today’s primary economic activity is the provision of services rather than the production of goods, meaning that services are becoming more important than in previous decades. However, Sweeney and Soutar (2001) argued that regarding ‘value’ as essentially a balance between quality and price is an overly simplistic approach that precludes understanding of the term’s rich connotations. Accordingly, these authors asserted that perceived value is an overall assessment of the service or product’s utility, which is rooted in the outcome of user perception of what is received and what is given. Hence, perceived value involves both benefit and sacrifice components (Kim, Chan, & Gupta, 2007). Compared to e-government, m-government can be considered as an innovation that confers many advantages (e.g., mobility and localizability), which means that it conveys more value. For example, mobility is regarded as the most important technological trait of m-government (Wang, 2014), as it enables access to m-government without time and distance limitations and can also provide time-critical services (such as mobile communication and mobile information searching) (Yuan, Archer, Connelly, & Zheng, 2010). Localizability is considered to be the other important technological trait of m-government, as it enables mgovernment to locate citizens and provide them with location-based services, as well as personalized content and services (Chen, Vogel, & Wang, 2016). More importantly, using m-government (as compared to e-government) requires less technical knowledge and lower costs; this makes m-government services more easily available and accessible, thus reducing the digital divide (Trimi & Sheng, 2008). We use perceived value as a substitution for net benefits in mgovernment success model for the following reasons. First, perceived value can more accurately represent the cost–benefit paradigm. Perceived value adopts the theory of consumer choice and decisionmaking from economics and marketing research, and a rational decision-maker (e.g., an m-government continuance user) tends to choose value maximization over the cost–benefit paradigm, which then leads to continuance usage. Second, using perceived value to assess net benefits is a response to the necessity of developing constructs to gauge the success of new IS (Petter et al., 2008). Compared to net benefits, perceived value is more suitable for measuring the impact of new technology, as it is easy for citizens to perceive the value of these new technological features (Kim et al., 2007). Finally, perceived value more accurately reflects the social objectives of the public sector. While net benefits are more likely to be associated with business activities, perceived value more accurately reflects the strategic goals of public services (Grimsley & Meehan, 2007). Generally, private sector firms pay more attention to efficiency, quality and reliability, while public sector managers must coordinate these concerns with accountability, public trust and differing public preferences (Hefetz & Warner, 2004).

H1. Information quality is positively associated with satisfaction of mgovernment services. 2.5.2. System quality and citizen satisfaction System quality pertains to how well the system transfers information and services to citizens (Maes & Poels, 2007). The key measures of system quality are availability, usefulness, response time, reliability, and flexibility (Delone & Mclean, 2004). Extant studies have found that higher system quality leads to greater citizen satisfaction in both ecommerce (Wang, 2008) and e-government (Teo et al., 2009). Compared to e-government, using m-government requires little technological knowledge and less cost, which makes m-government services more easily available and accessible (Trimi & Sheng, 2008). Furthermore, the mobile phone is a personal device and can be used as a digital token for authentication, which enhances m-government’s reliability and security (Cao, Lu, Gupta, & Yang, 2015). Finally, m-government dispenses with time and space limitations, allowing users to enjoy public services anytime and anywhere, which enhances m-government’s flexibility and responsiveness (Ishmatova & Obi, 2009). These advantages of m-government improve information quality and may increase citizen satisfaction. Hence, we present the following hypothesis: H2. System quality is positively associated with satisfaction of mgovernment services.

2.5. Research model and hypotheses 2.5.3. Service quality and citizen satisfaction Service quality captures the degree of difference between consumer perception and expectation (Parasuraman et al., 1988), and is measured by tangibles, reliability, responsiveness, assurance, and empathy. In the e-commerce context, service quality can also be assessed through the effectiveness of online support features such as frequently asked questions (FAQs), custom website intelligence and order tracking (Molla &

Fig. 1 presents our research model. Our m-government success model includes information quality, system quality, online and offline service quality, citizen satisfaction, and perceived value. Here, information quality refers to the degree and direction of discrepancy between citizens’ perceptions and expectations of the content of mgovernment; system quality refers to the degree and direction of 4

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Fig. 1. Research Model.

on the customer's specific situation. Hence, the advantages of offline services are becoming increasingly obvious in the face of citizens' more personalized service needs (Lee et al., 2011). However, on the one hand, citizens might switch to online channels if and when they come to believe that m-government provides improved benefits relative to offline service (Hansen, 2005). On the other hand, once the government provides the same public service through both online and offline channels, citizens may not consider the offline channel as an independent service channel separate from the online channel (KaufmanScarborough & Lindquist, 2002), as citizens' previous experience with offline service quality will improve overall trust in government. Extant research has indicated that citizens' positive offline service experience is helpful in enhancing their awareness of the reliability of online service quality, and can also help reduce their uncertainty regarding the government's ability to provide high-quality services through online avenues (e.g., Madlberger, 2006). We can here recall that H3 hypothesizes that citizens will be more satisfied with m-government services when they perceive high online service quality, as these positive experiences will enhance their satisfaction with the online services provided by the government. In addition, the positive effect of online service quality on citizen satisfaction may also be affected by the level of offline service quality. When offline service quality is high, citizens are more likely to form stable, long-term relationships with service providers (e.g., governments), and also tend to believe that they can acquire high online service quality and satisfaction with services provided by the government (Kim, Malhotra, & Narasimhan, 2005). Conversely, when offline service quality is low, this will weaken the positive relationship between online service quality and citizen satisfaction. Hence, we present the following hypothesis:

Licker, 2001). There is no doubt that the technology that supports services for user transactions via online IS is important; this is true whether the support is offered through help desks, hotlines, service centers, or online communications (Delone & Mclean, 2004). Extant research indicates that service quality is an important antecedent of both satisfaction and measures of IS success in e-commerce (Delone & Mclean, 2004) and e-government (Grimsley & Meehan, 2007). Accessing m-government services via online channels is convenient and flexible, making it easy to search for information and complete business transactions in an efficient and effective way (Shareef, Kumar, Dwivedi, & Kumar, 2014), which may enhance citizen satisfaction. At the same time, all the processes of the online service are visible, and all operations leave traces in the system; this increases the transparency of the service, reduces corruption (Bertot et al., 2010), and is conducive to increasing citizen satisfaction. Hence, we present the following hypothesis: H3. Online service quality is positively associated with satisfaction of m-government services.

2.5.4. Citizen satisfaction and perceived value Referring to the definition of e-commerce satisfaction (Molla & Licker, 2001), we define m-government satisfaction as the reaction or feeling of a citizen in relation to his/her experience with all aspects of an m-government system. The IS success model implies that use and user satisfaction are closely interrelated. For example, user satisfaction is an important variable for use in measuring the actual use of IS in the public sector (Chan et al., 2010). The IS success model suggests that positive experience with use will lead to greater user satisfaction, and that greater user satisfaction will in turn increase use and net benefits in related IS fields, such as e-commerce (Delone & Mclean, 2004; Wang, 2008), e-government (Grimsley & Meehan, 2007; Wang & Liao, 2008), and m-commerce (Chatterjee et al., 2009). Similarly, increasing citizen satisfaction with m-government is beneficial to both intention to use and actual use (Wang, 2014). Because of its time-critical nature, which is rooted in GPS technology and based on mobility and location-sensitive functions, m-government can provide citizens with both time-critical services (such as notices and real-time job dispatching) (Yuan et al., 2010) and location-based services (including location tracking, personalized information and services) (Chen et al., 2016), all of which are beneficial to citizens. M-government can also increase government transparency and citizen participation, as well as reduce the digital divide, which will enhance citizen satisfaction and continuance usage. Hence, we present the following hypothesis:

H5. Offline service quality positively moderates the relationship between online service quality and satisfaction of m-government services.

3. Method 3.1. Measures To ensure content validity, the items used to measure all variables in our model (such as information quality, system quality, online service quality, offline service quality, citizen satisfaction, and perceived value) were adapted from validated instruments in extant literature, although they were reworded to fit our context (see Appendix B). Information quality and system quality were measured using the instrument suggested by Teo et al. (2009). According to the measurement of service quality in e-government (e.g., Teo et al., 2009) or e-commerce (Wang, 2008) research, service quality in fact refers to the quality of online services. Therefore, our measure of online service quality was adapted from service quality in Teo et al. (2009). Moreover, our measure of offline service quality was adapted from Lee et al. (2011) and Teo et al. (2009). Citizen satisfaction was measured using the instrument suggested by Chen et al. (2016). We used public value to measure

H4. Citizen satisfaction with m-government is positively associated with the perception of m-government value.

2.5.5. The role of offline service quality Offline channels facilitate citizens’ ability to physically access public services and interact with government staff (Grewal et al., 2004). More importantly, offline services can adopt targeted service measures based 5

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perceived value in mobile government in order to distinguish the difference between IS in the public context and IS in the consumer context. Hence, perceived value was adapted from Scott, DeLone, and Golden (2016). These items were modified to make them relevant to the mgovernment context (specifically, m-police) when we translated the items into Chinese by means of a back-translation method (Brislin, 1970). Pretesting of the measures was conducted using 60 respondents. The results of Cronbach’s α of all variables (0.75-0.89) and factor loadings (above 0.75) via confirmatory factor analysis were deemed acceptable. Accordingly, the items were further adjusted to make their wordings as clear and accurate as possible. Likert scales (1–7), with anchors ranging from ‘strongly disagree’ to ‘strongly agree’, were used to measure all constructs. Appendix B lists the final items used in this study. Moreover, to account for differences among users, we included three control variables (i.e. age, gender, and education level) suggested by the extant literature (e.g., Teo, 2001).

Table 1 Demographics of the two group samples (N = 286). Characteristics Gender (GEN) Age (year)

Education (EDU) Online time per day (hour)

Male (0) Female (1) 20–29 30–39 40–49 ≥ 50 Below college College and above < 1 1–3 4–8 > 9

Frequency

Percentage

165 123 86 116 55 29 129 157 38 86 127 35

57.69 43.01 30.07 40.56 19.23 10.14 45.10 54.90 13.29 30.07 44.41 12.24

variable method to test the research model. Results of this testing indicate that there were no differences between the research model (the average factor loading is 0.836) and the marker variable model (the average factor loading is 0.15) (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003), which confirmed that CMV was not a serious problem in our study.

3.2. Data collection The police station is one of the government departments contacted most frequently by Chinese citizens, as citizens need to go to their local police station to update their Hukou (household registration) in the area where the residence is located whenever their Hukou changes throughout their lives. Under normal circumstances, citizens need to go to the police station to handle this business, which is a time-consuming and laborious process. In order to improve service efficiency, PoliceCivilian Communication, an m-government service (hereafter referred to as m-police), was developed by the Zhengzhou Public Security Bureau of Henan Province in 2018. This service aims to break the restrictions of local area networks, create a new-generation police service platform, and provide high-quality police services to stakeholders by facilitating two-way communication between police staff, police and citizens, and police and firms. M-police provides a range of public services: these include police news, online reservation, WeChat notifications, residence permit online applications, inquiries regarding motor vehicle infractions, driver's license score inquiries, entry and exit certificate inquiry, inquiries regarding ID card and residence permit processing progress, vehicle inspection point distribution queries, security officer test results and card progress queries, online video consultations, online reminders, and other services. Therefore, it can be said that the services provided by m-police are closely related to the needs of citizens and have become one of the most important forms of m-government application (Firoozy-Najafabadi & Pashazadeh, 2011; Yin et al., 2006). The questionnaire was pilot-tested among a group of 60 citizens, who were not included in the main survey. All reliability measures using Cronbach’s alpha were above 0.70 (Fornell & Larcker, 1981). Construct validity was evaluated using factor analysis, and all items loaded on their expected constructs. The formal questionnaire survey was conducted via a tax information provider whose customers are located in all cities in Henan province. The tax information provider randomly invited 700 citizens, all of whom came to the company to handle their business, to fill out the questionnaire. Potential respondents were reminded not to participate in the survey if they had no experience with m-police. Each of the respondents was paid RMB50 ($7) as an incentive. We collected 320 respondents within one month. There were 286 valid questionnaires (Table 1) in total, as 34 questionnaires had missing data. T-tests (respondents versus non-respondents) suggest that there were no significant differences in terms of gender, age and education. One limitation of self-reported data is that it may be affected by common method variance (CMV). First, we used Harman’s one-factor test to evaluate the CMV; the results showed that no single factor accounted for the majority of variance (e.g., the most covariance explained by one factor is 25.38 %) (Harman, 1976), which indicated that CMV was not a threat in this study. Furthermore, we used the marker

4. Results Similar to some extant studies (e.g., Teo et al., 2009; Tan et al., 2013), partial least squares (PLS) was used to test our model. This method employs a component-based approach with fewer restrictions on sample size and residual distributions, and has thus been recognized as an effective method for measuring construct reliability and validity (Chin, Marcolin, & Newsted, 2003). Using the Smart-PLS 2.0, we first evaluated the measurement model to assess reliability and validity, then tested the structural model. 4.1. Measurement model Tables 2 and 3 present the measurement model results, including information about reliability, validity, correlations, and factor loadings. In Table 2, Cronbach’s alphas are between 0.75 and 0.92 in our model, which is well above the 0.70 criterion for internal consistency reliability (Cronbach & Furby, 1970). The average variance extracted (AVE) was greater than 0.50 (ranging from 0.69 to 0.85) in all cases and also greater than the square of the correlations, suggesting discriminant validity (Chin et al., 2003). In Table 3, a further confirmatory factor analysis conducted using PLS indicates that all items had high factor loadings (ranging from 0.76–0.90) in their corresponding constructs, which supports convergent and discriminant validity (Tan et al., 2013). 4.2. Structural model Fig. 2 illustrates the standardized path coefficients in our model. H1, H3, H4, and H5 were supported, although H2 was not. H1 was supported because the results indicated that information quality (b = 0.32, p < 0.001) was positively associated with citizen satisfaction. System quality (b = 0.12, p > 0.05) was positively associated with citizen satisfaction, but the result was not significant; thus, H2 was not supported. H3 was supported in that online service quality was positively associated with citizen satisfaction (b = 0.43, p < 0.001). H4 was supported because citizen satisfaction (b = 0.36, p < 0.001) was significantly associated with perceived value. Finally, H5 was supported because we found that offline service quality (b = 0.15, p < 0.05) positively moderated the relationship between online service quality and citizen satisfaction. We used a slope test to illustrate the results of the moderation analysis. For H5, following simple slope analyses, the slopes for low 6

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Table 2 Correlations between constructs. Constructs

Mean

SD

AVE (> 0.50)

CR (> 0.70)

Cronbach’s α (> 0.70)

1

2

3

4

5

6

1.INQ 2.SYQ 3.ONQ 4.OFQ 5.SAT 6.PEV

4.42 4.78 5.98 4.23 4.38 5.24

1.25 1.32 1.09 1.57 1.02 1.16

0.76 0.73 0.82 0.69 0.85 0.73

0.75 0.84 0.85 0.78 0.83 0.89

0.76 0.79 0.92 0.75 0.85 0.86

0.87 −0.09∗∗ 0.17∗ −0.12∗∗∗ 0.18∗∗ 0.21∗∗

0.85 0.12∗∗ 0.09∗∗∗ −0.25∗∗ 0.16∗

0.91 −0.15∗ 0.31∗∗ 0.12∗∗

0.83 0.15∗∗ −0.29∗

0.92 0.07∗∗

0.94

Note: ∗p < 0.05; ∗∗p < 0.01, ∗∗∗p < 0.001.

that the control variables did not affect path weights among the major constructs in the research model. We used the four-step method approach proposed by Baron and Kenny (1986) to test the mediation effect. We further examined the significance of the mediating effect by conducting a Sobel test (Sobel, 1986). The results showed that information quality (T = 29.78, p < 0.01) and online service quality (T = 35.24, p < 0.001) had a significant positive relationship with citizen satisfaction and perceived value respectively; this indicated that citizen satisfaction partially mediated the relationship between information quality/online service quality and perceived value.

Table 3 Item loadings and cross-loadings. Items

INQ

SYQ

ONQ

OFQ

SAT

PEV

INQ 1 INQ 2 INQ 3 INQ 4 SYQ 1 SYQ 2 SYQ 3 SYQ 4 ONQ 1 ONQ 2 ONQ 3 ONQ4 OFQ 1 OFQ 2 OFQ 3 OFQ 4 SAT 1 SAT 2 SAT 3 SAT 4 PEV 1 PEV 2 PEV 3 PEV 4 PEV 5 PEV 6

0.83 0.82 0.78 0.82 0.13∗∗∗ 0.14∗∗ 0.27∗∗∗ 0.24∗∗∗ 0.08∗∗ 0.36∗∗∗ 0.24∗∗ 0.27∗∗∗ 0.24∗∗∗ 0.17∗∗ 0.12∗ 0.16∗∗∗ 0.32∗∗∗ 0.27∗∗∗ 0.21∗ 0.25∗∗∗ 0.24∗ 0.18∗∗∗ 0.27∗∗ 0.08∗∗ 0.32∗∗ 0.16∗∗∗

0.25∗∗∗ 0.09∗∗ 0.12∗∗∗ 0.05∗ 0.85 0.84 0.83 0.78 0.12∗ 0.24∗∗∗ 0.14∗∗ 0.24∗∗∗ 0.25∗∗∗ 0.21∗∗∗ 0.17∗∗∗ 0.16∗∗ 0.16∗∗ 0.22∗∗∗ 0.19∗∗∗ 0.03∗ 0.16∗∗∗ 0.16∗ 0.21∗∗∗ 0.28∗∗ 0.32∗∗ 0.13∗

0.14∗ 0.23∗ 0.08∗∗∗ 0.15∗∗∗ 0.28∗∗∗ 0.32∗∗ 0.17∗∗ 0.25∗∗∗ 0.86 0.82 0.80 0.88 0.02∗∗∗ 0.12∗∗∗ 0.32∗∗∗ 0.28∗ 0.13∗∗ 0.12∗ 0.26∗∗ 0.25∗ 0.21∗∗∗ 0.15∗∗∗ 0.23∗∗ 0.20∗∗∗ 0.12∗ 0.22∗∗∗

0.15∗ 0.17∗∗ 0.12∗ 0.28∗ 0.26∗∗ 0.32∗∗∗ 0.09∗∗ 0.25∗ 0.28∗∗∗ 0.07∗∗∗ 0.26∗∗ 0.25∗∗∗ 0.81 0.84 0.88 0.76 0.08∗∗∗ 0.12∗∗∗ 0.24∗∗ 0.02∗∗ 0.07∗ 0.23∗ 0.25∗∗∗ 0.09∗ 0.12∗∗∗ 0.07∗∗

0.23∗∗∗ 0.26∗ 0.14∗∗ 0.25∗∗∗ 0.08∗ 0.19∗ 0.15∗∗ 0.21∗∗∗ 0.16∗∗ 0.23∗∗∗ 0.16∗∗∗ 0.05∗∗∗ 0.23∗ 0.12∗∗∗ 0.16∗∗ 0.23∗∗∗ 0.86 0.79 0.82 0.89 0.17∗∗∗ 0.27∗ 0.12∗∗∗ 0.25∗∗ 0.17∗ 0.23∗∗∗

0.28∗∗ 0.25∗∗∗ 0.17∗∗∗ 0.08∗∗∗ 0.19∗∗∗ 0.28∗∗∗ 0.17∗∗ 0.25∗∗∗ 0.04∗ 0.14∗∗ 0..17∗∗∗ 0.28∗∗ 0.05∗ 0.21∗∗ 0.23∗ 0.21∗ 0.05∗ 0.05∗∗∗ 0.06∗ 0.17∗∗∗ 0.86 0.85 0.76 0.79 0.82 0. 78

5. Discussion Grounded in DeLone and McLean’s (2003) updated IS success model, we constructed an m-government success model and examined the relationship between constructs related to quality, citizen satisfaction, and perceived value. The results indicated that our research model were suitable measures of m-government system success. Consistent with prior IS success model research (Floropoulos et al., 2010; Wang & Liao, 2008; Wang, 2008), both information quality and online service quality were found to be positively associated with citizen satisfaction. Contrary to our expectations, however, system quality did not have a significant positive relationship with citizen satisfaction, which is different from the trend in the results of the extant research (e.g., Teo et al., 2009; Lee et al., 2011). Moreover, we also found that offline service quality positively moderated the relationship between online service quality and citizen satisfaction; however, we found that offline service quality did not have a significant positive relationship with citizen satisfaction. First, DeLone and McLean’s (2003) updated IS success model was found to be important in measuring m-government success. Specifically, citizen satisfaction was positively related to perceived value (measuring net benefits in the updated IS success model). This is because user satisfaction with the m-government system is conducive to enhancing the continued use of m-government. The more users use mgovernment, the greater the benefits they receive, which facilitates the successful implementation of the m-government system (DeLone & McLean, 2003). The antecedents of citizen satisfaction – information quality and online service quality – were important factors associated with citizen satisfaction; of these, online service quality had the strongest relationship with user satisfaction, while information quality had the second strongest relationship with user satisfaction. In contrast to the updated IS success model, we regard service quality as a multi-

Note: ∗p < 0.05; ∗∗p < 0.01, ∗∗∗p < 0.00.

offline service quality (t = 16.45, p < 0.001) and high offline service quality (t = 19.32, p < 0.001) were found to be significant. This finding indicates that at high levels of offline service quality, citizen satisfaction increases rapidly as online service quality increases, while at low levels of offline service quality, citizen satisfaction increases only marginally as online service quality increases (Appendix C). However, while offline service quality (b = 0.09, p > 0.05) was positively associated with citizen satisfaction, this result was not significant. Altogether, the model accounted for 42 % of the variance in perceived value, with online service quality contributing much more to citizen satisfaction than other quality-related constructs. Moreover, we tested the control variables – gender (b = 0.08, p > 0.05), age (b = 0.14, p > 0.05), and education (b = 0.03, p > 0.05) – and found them not to be significant. We also tested the research model with all control variables excluded and found no difference in the results. This indicates

Fig. 2. The results of research model. Note: ∗p < 0.05; ∗∗p < 0.01, ∗∗∗p < 0.001.

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dimensional construct that includes both online and offline service quality. Moreover, compared to the existing IS success models (e.g., Wang & Liao, 2008; Rana et al., 2015), we use perceived value rather than net benefits or continuance intention to measure IS success, which is more in line with the purpose of the public service sector in promoting the government information system. Second, an unexpected result of our research is that the relationship between system quality and citizen satisfaction was found not to be significant. This result is both surprising and contrary to previous findings in the online service context (e.g., Wang & Liao, 2008; Wang, 2008; Teo et al., 2009). In order to clarify the reasons for this insignificant relationship, we interviewed 16 respondents by telephone to answer questions pertaining to their experience of using the m-government system. These interviews found that the main reasons for user dissatisfaction include, for example, that the system often suddenly exits during the use process, that the response time is slow, and that system updates are not timely. Based on these results, we believe that the following two factors may lead to the current poor perceived quality of the m-government system. First, compared to the private sector, the provision of information online started relatively late in the public sector. Consequently, there may not be sufficient experience within this sector in developing system quality for m-government services. Second, due to the strict management of government budgets, many IS are developed internally and staff may not have sufficient experience with them, which results in a poorly designed system that leads to citizens being dissatisfied with system quality. Further analysis indicated that the relationship between system quality (b = 0.06, p > 0.05) and perceived value was not significant. This result suggests that the quality of the m-government system did not meet citizens’ expectations because the system quality did not increase citizens’ perceived value. Hence, it is unsurprising that the relationship between system quality and citizen satisfaction was not significant. Finally, our results indicated that online service quality had the strongest relationship with citizen satisfaction among the quality-related constructs; however, the relationship between offline service quality and citizen satisfaction was not significant. One possible reason for this is that transparency is critical to citizen satisfaction in the Chinese context because of the relatively weaker institutional and legal environments in the country (Chen et al., 2016). In general, people prefer self-service and online services over face-to-face services because of the time savings, increased personal control (Chan et al., 2010), and reduced opportunity for corruption (Bertot et al., 2010). Another potential reason is that the online services provided by m-government do have many benefits for users, including greater efficiency and effectiveness, a cheaper, easier and faster channel for information access, government services anytime and anywhere, personalized information and services, and so on (Trimi & Sheng, 2008). Compared with previous studies that focused on offline service quality (e.g., Lee et al., 2011), and online service quality (e.g., Tan et al., 2013), our research examines whether offline service quality moderates the relationship between online service quality and citizen satisfaction. These results will help the public service sector to improve service quality through the provision of online and offline channels to citizens.

government (e.g., Wang & Liao, 2008) and e-commerce contexts (e.g., Wang, 2008). Extant research suggests that the desire to search for useful information is a key reason why citizens use m-government systems. Contrary to our expectations, however, the relationship between system quality and citizen satisfaction was not significant, which is different from most prior research (e.g., Wang, 2008; Wang & Liao, 2008). Taken as a whole, when considering how service quality might be adapted to the m-government context, the empirical results of this study provide insights into how service quality based on online and offline service channels are associated with citizen satisfaction and perceived value. The second contribution of our research is that we examine the moderating role of offline service quality in m-government success, which expands our understanding of IS success from the perspective of service channels. Although extant research indicates that service quality is a multi-dimensional construct (see Appendix A and Section 2.3), it is considered to be a single construct in most IS success studies (e.g., Teo et al., 2009). Most existing research has tested the role of offline service quality (Lee et al., 2011) or online service quality (Rana et al., 2015) rather than the contingent role of offline service quality. To fill this gap, our study regards offline service quality as a moderator; in so doing, it expands our understanding of the updated IS success model in the multi-channel services context. In practice, these results are important for service providers, as online and offline services are becoming increasingly popular as a means of enhancing public service quality and value along with the rapid development of in-store technology (Lee et al., 2011). The third contribution is that we use perceived value to measure net benefits, which creates a public value-based construct (net benefits) that measures IS success from the citizen perspective within the context of m-government systems. Measuring net benefits is an important issue in the updated IS success model because the creation and selection of success constructs are critical to the effective application of the updated IS success model. Previous research often used constructs including continuance intention (e.g., Teo et al., 2009), satisfaction (e.g., Floropoulos et al., 2010), and net benefits (Wang, 2008). However, most studies measuring the success of public sector IS (e.g., e-government) still follow the logic of measuring business systems. Moore (1995) asserted that creating public value is an important strategic goal of the public service sector. Public value refers to the value attached to the outcome of government policies and citizens experience of public services. Scott et al. (2003) proposed success measures designed to assess net benefits from the public sector perspective based on public value theory. We followed the method proposed by these authors, thereby introducing perceived value into the m-government success model and validating it as a measure. Note that it is beneficial to distinguish IS in the public sector from that in business systems in the private sector; this is because m-government is different from business IS, as the former frequently encompasses social goals (e.g., public trust, sustainability, and well-being) (Grimsley & Meehan, 2007). Our study also answers the call to develop constructs capable of measuring the success of new technologies, which is a continuing need in the IS field (Petter et al., 2008).

5.1. Implications for research

5.2. Implications for practice

Our study makes several contributions to the research. The major contribution of this study is the respecification and validation of a theoretical model measuring m-government success. To our knowledge, this research is among the first to theorize about m-government success. Consequently, we extend past research on the updated IS success model by refining service quality from the perspective of service channels and measuring net benefits via perceived value from the public sector perspective, thereby enhancing existing knowledge within the IS success literature. We found that information quality was positively related to citizen satisfaction, which is consistent with prior research in the e-

The results of our research provide governments with a set of rich insights into how m-government services could be improved so as to enhance the successful implementation of the m-government system. First, our study suggests that governments should focus on improving m-government system quality. Our empirical results indicate that the relationships between system quality and both citizen satisfaction and perceived value were not significant. However, the results of subsequent telephone interviews revealed that the main reason for this phenomenon was the poor quality of the m-government system. Hence, governments should take appropriate actions to improve the 8

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was used as the dependent variable to measure net benefits in our study. Future research could use trust, accountability, etc. as a dependent variable to measure net benefits in order to test our research model. Finally, extant research into e-government and e-commerce has indicated that channel integration can improve service quality (Gallino & Moreno, 2014; Li et al., 2018; Shen et al., 2018), since online-offline channel integration can more comprehensively meet the preferences of different citizens. Further research could include this construct in our model and test the impact of these three constructs (online, offline, and integration service quality) on citizens’ perceived value of m-government services.

development and testing of their m-government systems. For example, if the government department intends to develop an m-government system internally, it should seek inputs from experienced employees in order to develop the system and strengthen the management of the development process. The government could also entrust a professional company to develop the system by means of IT outsourcing, or opt to develop the system in cooperation with experienced software companies. Rigorous testing prior to actual implementation is crucial if citizen satisfaction with m-government is to be improved. Second, our study suggests that when the public sector provides mgovernment services with the aim of increasing citizen satisfaction, it should rely more on online channels than on offline channels; this is because our research has identified that online service quality has the strongest relationship with citizen satisfaction. Due to the obvious advantages of online services, which include cost reduction, efficiency, convenience and flexibility (Hung et al., 2013), the government should provide self-service as much as possible through online channels and reduce offline services (e.g., counter services); this can effectively reduce inefficient face-to-face interactions between citizens and officials, and thus alleviate citizens’ dissatisfaction and frustration. Finally, our study suggests that governments should improve information quality when providing m-government services to citizens. More specifically, the relationship between information quality and satisfaction is second only to that between online service quality and satisfaction. This result suggests that obtaining information is an important motivating factor in users’ use of m-government. To improve information quality and citizen satisfaction, governments should ensure that the accuracy, completeness, consistency, uniqueness, and timeliness of the information provided meet the needs of citizens. In addition, governments should provide personalized information to cater to citizens’ preferences.

6. Conclusion When attempting to implement m-government systems, the public sector faces the challenge of measuring IS success. Accordingly, to address this concern, we develop an m-government success model based on DeLone and McLean’s (2003) updated IS success model. Compared with this updated model, which considers service quality as a singledimensional variable, we subdivide service quality into online service quality and offline service quality and use the latter as a moderator. Moreover, considering that the important strategic goal of the public service sector is to create value for citizens, we use perceived value (similar to public value) to measure net benefits. Our results suggest that online service quality and information quality are important antecedents of citizen satisfaction, while the relationships between system quality and offline service quality with citizen satisfaction are not significant. We also find that offline service quality moderates the relationship between online service quality and citizen satisfaction. Our study adapts the updated DeLone and McLean (2003) success model to the context of m-government services. Our results will be useful for public service providers aiming to better understand how to encourage users to continue to use m-government, as it has discerned the key driving factors of this continued usage.

5.3. Limitations There are a few limitations of the present study, along with future research directions, that should be discussed. First, the data were collected from China and based on the m-police in China. It must be noted here that m-police is a typical m-government application that is widely utilized in China. In order to expand the generalizability of our findings, future studies could examine this model using other m-government systems (e.g. m-health, m-traffic, and m-tax) in different nations and at different levels (federal, local, etc.), as cultural values play an important role in users’ adoption and continued use of innovative technologies (Dwivedi, Shareef, Simintiras, Lal, & Weerakkody, 2016). Furthermore, future studies could also examine other variables associated with mgovernment success. Second, our results were based on cross-sectional data, meaning that causality cannot be inferred; future research could use longitudinal data to test our research model. Third, perceived value

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was partly supported by National Natural Science Foundation of China (NSFC) under Grant [NSFC-71403080] and Department of Science & Technology of Henan Province under Grant [172400410135 and 182400410140].

Appendix A. Literature review quality related constructs in m-government context

Author(s)

Perspective

Quality reTheory and method lated variables

Main findings

Ding, Yang, Chen, Long, and Wei (2019) Li et al. (2018)

Citizen

IQQ

Citizen

IQQ

Stimulus-organism- response framework and theory of uses & gratifications / Survey heory of uses and gratifications / Survey

Citizen

Quality

Theory of diffusion innovation / Survey

Perceived integration positively influence the citizens' perceptions of value. Perceived integration positively influence the citizens' perceptions of value. Quality is positively related to intention to adopt and use.

Citizen

INQ

UTAUT / Survey

INQ is positively related to intention to adopt and use.

Citizen

INQ

Heuristic framework / Survey

INQ is positively related to the attractiveness of m- government.

Citizen

IQQ

Categorization theory Survey

IQQ is positively related to perceived value.

Citizen

SEQ

Theory of diffusion innovation / Survey

SEQ is positively related to intention to adopt and use.

Jaradat, Moustafa, and Al-Mashaqba (2018) Sharma, Al-Badi, Rana, and Al-Azizi (2018) Wirtz and Birkmeyer (2018) Yang, Jiang, Yao, Chen, and Wei (2018) Yeh (2017)

9

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SEQ

N. A / Concept

Shareef, Kumar et al. (2016) Shareef, Kumar, Dwivedi, and Kumar (2016) Chen et al. (2016) Aloudat, Michael, Chen, and Al-Debei (2014) Shareef, Dwivedi et al. (2014) Shareef, Kumar et al. (2014) Shareef et al. (2012)

Citizen

SEQ

N. A / Survey

Citizen

SYQ

citizen attitude intention / Survey

Citizen Citizen

SEQ SEQ

Citizen

SEQ

Procedural fairness theory / Survey Theory of Reasoned Action (TRA) and TAM / Survey Theory of diffusion innovation / Survey

Citizen

SEQ

SERVQUAL /Survey

Citizen

SEQ

EG Adoption-Citizen Intention Model

SEQ is a multi-dimensional construct: Interaction quality, Environment quality, INQ, SYQ, Network quality, and outcome quality. SEQ implies connectivity, personalization, time and location, relevant content, process motivation, entertainment, informativeness. SYQ implies reliability and security.

SEQ implies transparency, information accuracy and voice opportunity. SEQ is positively related to intention to adopt and use. SEQ implies ease of use, usefulness, compatibility, empathy, security, reliability. SEQ contains four dimensions: connectivity, interactivity, authenticity, and understandability. SEQ implies ease of use, usefulness, compatibility, empathy, security, reliability.

Note: INQ — Information quality, SYQ — System quality, SEQ — Service quality, IQQ — Integration of online and offline services quality. Appendix B. Measures Information quality (Teo et al., 2009) INQ1 INQ2 INQ3 INQ4

M-police M-police M-police M-police

system system system system

provides provides provides provides

the precise information you need. sufficient information. up-to-date information. reliable information.

System quality (Teo et al., 2009) SYQ1 SYQ2 SYQ3 SYQ4

M-police system is user friendly. M-police system is easy to use. I find it easy to get M-police system to do what I want it to do. I feel secure to use m-government.

Online service quality (Teo et al., 2009) ONQ1 ONQ2 ONQ3 ONQ4

M-police M-police M-police M-police

provides dependable services. provides services at the times it promises. is responsive to citizen’s request. is designed with citizen’s best interests at heart.

Offline service quality (Lee et al., 2011; Teo et al., 2009) OFQ1 OFQ2 OFQ3 OFQ4

The The The The

police police police police

bureau bureau bureau bureau

provides dependable services. provides services at the times it promises. is responsive to citizen’s request. satisfies the needs of citizens.

Citizen satisfaction (Chen et al., 2016) How do you feel about your overall experience in using M-police service? SAT1 Very dissatisfied/very satisfied. SAT2 Very displeased/very pleased. SAT3 Very frustrated/very contented. SAT4 Absolutely terrible/absolutely delighted. Perceived value (Scott et al., 2016, adapted from public value) PEV1 PEV2 PEV3 PEV4 PEV5 PEV6

Using M-police system saves me money. Using M-police is an effective way of communicating with the police berau. I am comfortable relying on M-police to meet its obligations This website increases my understanding of issues. I felt that I had a lot of control over my experiences with the M-police system. This website makes me feel that decision-makers listen to me.

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Appendix C. The interaction effects of offline service quality

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