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6.4 Findings’ Implication on m-Government Service Adoptability
This section responds to research objective four (RO4) by examining and explaining the overall implications of the findings on the adoptability of m-government services in Tanzania. The section seeks to establish the consequences of the research findings, to explain the proven low citizens’ adoption of m-government services in Tanzania as indicated in section 5.4.2. While section 6.2 has extensively discussed relevant factors of adoption, and section 6.3 the prevailing provisioning practices, it is critical to understand how these conditions affect the adoptability of m-government services by citizens. This knowledge is critical for the development of the provisioning framework that addresses citizens' challenges in accessing and using m-government services and, in turn, enhancing adoption. After uncovering knowledge on the implications of these findings, it is essential to note that these implications serve as hindrances to citizens’ adoption of m-government services, thus are clustered and discussed in the context of challenges to citizen’s adoption of m-government services. The challenges identified are based on the findings in the previous discussions on factors of adoption (Section 6.2) and provisioning practices (Section 6.3), in light of the problems that explain the limited citizens’ adoption of m-government services in Tanzania.
The study findings draw implications on three aspects in relation to citizens’ adoption of m- government services, which are emotions, cognitive and functional aspects. Taherdoost (2016) attests that factors of adoption either produce an emotional, cognitive or functional effect, which drives users to adopt. Kourouthanassis et al. (2015) argue that cognitive, functional processes and emotional elements regulate adoption behaviour. While functionality factors remain critical in determining service performance, the study findings discussed in section 6.2 and section 6.3 highlight the importance of non-technical factors in m-government service provisioning and adoption. Therefore, to entice citizens’ adoption, positive emotional and cognitive effects need to be invoked over and above accomplishing functional objectives.
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According to Taherdoost (2016), these factors – subjective norms (SN), financial influence (FI), attitudinal influence (AI) and technological influence (TI) – affect users’ emotions.
Attitudes have an emotional effect, the feeling of likes or dislikes, on adopters. Ham, Jeger &
Ivković (2015) define attitudes as the mental willingness to accept or reject an object, a person or a situation as a result of direct or indirect experience. According to Beaudry &
Pinsonneault (2010), attitudes relative to a particular behaviour are the emotions related to particular consequences, attributes and outcomes that are evaluated either positively (desirable) or negatively (undesirable). Negative emotions towards technology adoption include fear, anxiety and worries, while positive emotions that are said to support adoption include happiness, joy, contentment, enthusiasm and interest (Venkatesh et al., 2003;
Kourouthanassis et al., 2015; Taherdoost, 2016).
An emotion reflects the mental state of willingness to act, thus promoting the activation of certain behaviours and priorities that optimize an individual to adjust to their environment (Bagozzi, 2007; Beaudry & Pinsonneault, 2010; Rodger & Gonzalez, 2013). Emotions develop in response to the internal evaluation of an event considered essential and relevant to an individual. Raffaelli, Glynn & Tushman (2017) attest that technology triggers emotional responses when it assists or disrupts a sequence of events in a routine, thus may inhibit or motivate adoption. Beaudry & Pinsonneault (2010) conclude that emotions bridge the gap between the instant a routine is interrupted, with or without knowledge on future interruptions, and the time either new or old routines are re-established. According to Rodger
& Gonzalez (2014), emotion and memory affects technology adoption and diffusion.
Therefore the mobile technology influences (TI) of mobility, time, and location efficiency shape an individual's emotions towards intention to use, especially in cases where m- government services affect routines.
Additionally, subjective norms (SN), which reflect an individual’s perception of significant others’ approval and support of particular behaviours, affect adopters’ emotions. Similar to Abaza & Saif (2015), Almarashdeh & Alsmadi (2017), and Ham, Jeger & Ivković (2015), findings in section 5.4.3.5 indicate that SN significantly affects Tanzanians’ adoption intentions for m-government services. Ham, Jeger & Ivković (2015) posit that social pressure from significant others invokes emotions through its ability to regulate behaviour and
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motivate compliance. When deconstructing SN, it yields actual actions and behaviours of others (descriptive norms), as well as others’ opinion on how one ought to behave (social norms). Furthermore, the cost of acquiring the device (mobile phone) and the services, namely Internet access and SMS services, significantly predicts adoption behaviour through its effect on adopters' emotions (Beaudry & Pinsonneault, 2010; Venkatesh, Thong & Xu, 2012; Al-Hujra, 2015). Beaudry & Pinsonneault (2010) argue that perception over cost results in formation of emotions that either positively or negatively influence adoption. For instance, when the cost is perceived affordable or the pricing strategy is perceived to be socially inclusive, it triggers positive emotions towards adoption, thus encouraging more citizens to accept and use m-government services.
Therefore, with the adoption of new technology being complex and multifaceted, emotions of the new technology become powerful in determining its adoption. Consequently, emotions are critical in modelling provisioning for enhanced citizen adoption. The above discussion elaborates how the influence of AI, SN, FI and TI on behaviour intention navigates as emotions, emphasizing the need to incorporate emotional invoking attributes in m- government service provisioning for an enhanced citizen adoption.
6.4.2 Cognitive Implications
According to Taherdoost (2016), attitudes have a cognitive effect on adopters; that is, the information held regarding an object, person or issue. Cognitive factors refer to individuals’
characteristics regarding information and abilities they hold about an object, a situation or a person, which affects their performance and learning. Raffaelli, Glynn & Tushman (2017) assert that the behavioural regulating effect of cognitive factors facilitates technology adoption. Technology adoption is associated with learning and using; therefore, its success largely depends on how the cognitive component is positively affected. Furthermore, the cognitive component is fundamental, invoking and facilitating one’s capacity to self-regulate emotions, thoughts, instincts and actions (Gurbin, 2015; Rana & Dwivedi, 2015; Raffaelli, Glynn & Tushman, 2017). Relevant information about m-government service initiatives and services affects users’ cognizance (Henningsson & van Veenstra, 2010). Therefore, access to information regarding service attributes and support mechanisms regulates the effect of emotions on adoption. Savoldelli, Codagnone & Misuraca (2014) ascertain that access to relevant information and facilitating conditions affects citizens’ mental willingness to adopt
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m-government services. While triggering positive emotions is crucial in influencing adoption, stimulating user’s cognizance about m-government services is also essential.
Users’ cognizance entails both users’ awareness and knowledge capabilities acquired through m-government services orientation and training. The more socio-inclusive and transparent the m-government service provisioning process is, the more the desirable emotions are formed on m-government services; thus more users are attracted to adopt (Savoldelli, Codagnone & Misuraca, 2014; Henningsson & van Veenstra, 2010).
Likewise, Abaza & Saif (2015) affirm the importance of awareness on adoption of m- government services; they established that increasing awareness increases the chances of positive emotions being formed, thereby significantly affecting behaviour intention to use m- government services. Awareness marks the first step for a user to know about m-government services. Awareness building approaches may include public announcements, posters, and advertisement in newspapers, on television or on radio. However, while awareness stimulates initial intention, knowledge capability building needs to progress before and during service provision. The knowledge capability component reflects activities aimed at imparting basic skills sets for operating or navigating through m-government services as well as capturing and providing user experience feedback for m-government services. Orientation or a knowledge capability building phase must be reflective of issues concerning manageability, affordability and interest building for m-government services. To enhance m-government service adoption, user cognizance, that is, awareness and knowledge capabilities, are essential components for both design and provisioning of m-government services.
6.4.3 Functionality Implications
Functional factors related to service performance are essential and are the pinnacle of any service provisioning. While findings identify four factors as significant in predicting adoption, they also support the influence, although insignificant, of other factors related to service performance, which loaded on the structural model (Appendix I and Appendix J).
The postulated factors include performance efficiency (PE), self-efficacy (SE), hedonic value (HV) and facilitating conditions (FC). Venkatesh, Thong & Xu (2012) posit that factors related to performance are critical for the technology to be meaningful and useful. However, e-government deployment and provisioning have focused mostly on functional issues (technological and operational), ignoring non-technical aspects that might favour adoption
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(Savoldelli, Codagnone & Misuraca, 2014). Also, Ogunleye & van Belle (2014) noted that most m-government service deployment fails as a result of negligence of non-technical factors in service designing and provisioning.
User emotions and their cognizance need to be integrated with primary functionality motives in order to enhance adoption of m-government services. Functionality traits or instrumentality traits encompass both usefulness and usability motives for m-government services. Abaza & Saif (2015) argue that awareness needs to be complemented with positive emotions on service functionality for adoption to take place. Similarly, Almarashdeh &
Alsmadi (2017) and Venkatesh, Thong & Xu (2012) confirm the significance of functionality attributes on adoption by demonstrating the significant effect of perceived usefulness or performance expectancy on user intention to use m-government services. Contrary to study findings on performance expectancy, the proposed solution also incorporates functionality attributes, as they are the basis upon which services are developed and evaluated.
Thus, from the discussion above, three components, emotions, cognizance and functionality, are essential to be considered in designing a solution to enhance m-government service adoptability. The effect of not achieving these factors becomes a hindrance to adoption; it is thus critical to identify challenges facing citizens as a result of the implications of the study findings. Identifying components upon which adoption decisions are based on is one step;
however, there is a need to understand further the implications of these findings as a hindrance to citizens’ adoption of m-government services.