Comprehensive Model to use E-Government Services

Introduction

Although information systems research has made substantial strides toward understanding the antecedents of IT adoption through variance-based models such as the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), a stream of existing research (e.g., Beaudry & Pinsonneault 2010; Shareef et al 2011; Shanks et al 2012) shows that usage of new technologies is often complex and multifaceted, hence the need to employ process-oriented models in attempting to understand technology use and adoption. This section not only attempts to review the literature on the process model but also provides a justification for its use in this study.

The remainder of the section is organized as follows: the next part reviews process theory versus variance theory, while the subsequent section presents the relevant models of process theory including the model that is intended for use in this study. Afterward, a rationale for using process theory will be provided.

Process Theory versus Variance Theory

Extant literature demonstrates that theory in IS research can be considered from two broad approaches, namely the variance and process approaches. Markus and Robey (1988) opine that although it may be unjust to consider process models as superior to variance theoretical perspectives, the evidence seems to suggest that the former have been unreasonably neglected at the expense of the latter.

In essence, variance theoretical perspectives endeavor to explain the phenomenon by assessing the relationship between independent and dependent variables based on the principle of objectivity, with related studies assessing the predictor factors (independent variables) that determine the outcome (dependent variable) under investigation (Matayong & Mahmood 2013).

Owing to the fact that variance theoretical perspectives provide a core set of theoretical frameworks that attempt to illuminate a phenomenon such as technology adoption by examining individual or organizational levels with various factors that to a large extent determine outcomes in the studies through noting simple relationships, this approach faces contradictory findings as it merely explains the variation in the magnitude of certain outcomes rather than uncertain outcomes (Markus & Robey, 1988; Tsohou et al 2008).

In contrast, according to Matayong and Mahmood (2013, p. 475), “the process approach provides powerful explanations at both the micro and macro levels, while necessary causes cannot be demonstrated as being sufficient for the outcomes to occur.” Rather than attempting to relate the independent and dependent variables with the view to explaining the phenomenon using the principle of objectivity, the process theoretical perspective explains “how” and “why” interested outcomes of a phenomenon or issue of interest develop through a sequence of events over time. Consequently, the term “process” has three meanings, namely

1. the attempt to investigate the phenomenon, namely the description of how things can change over time through a sequence of time,
2. exploration and investigation of the concepts or variables of related actions, and
3. the rationalization of causal relationships between variables with logical statements (Chiles 2003; Matayong & Mahmood, 2013).

Mohr (1982), cited in Markus and Robey (1988), explicates the difference between the variance theory and process theory in terms of the hypothesized relationships between logical antecedents and outcomes, implying that invariance theory the precursor or the cause is speculated as a necessary and sufficient condition for the outcome, while in process theoretical perspectives the precursor is assumed as insufficient to cause the outcome but is held to be merely necessary for it to occur. Consequently, the process theory assumes that “outcomes are (partially) predictable from a knowledge of the process, not from the level of predictor variables” (Markus & Robey 1988, p. 590).

This view is reinforced by Radeke (2010), who argues that variance theory is grounded on a factor model which seeks to casually link variables with each other and assess the extent of these links to explain variation independent variables as caused by variations of one or more independent variables, while process theory seeks to explain by illuminating sequences of actions that lead to outcomes if specific antecedent outcomes are fulfilled. In other words, process theory is primarily concerned with understanding how phenomena evolve over the course of time, and also why they evolve in this way (Ramiller & Pentland 2009). As demonstrated in Table 1 below, other differences existing in the logical structure of variance versus process models are as follows.

Table 1 Difference between Variance Theory & Process Theory.

Available literature demonstrates that “variance and process theories can peacefully coexist, but that the distinctions between them should not be blurred in an attempt to gain the advantages of both within a single theoretical approach” (Markus & Robey 1988, p. 591). However, as demonstrated in the table, the two models have distinct differences not only in their methodological foundations but also in their logical sequence. In IS research, a dynamic phenomenon such as technology usage, which is known to be dominated by variance theories, has been explained by focusing on static aspects in the form of variances in independent and dependent variables for purposes of determining the degree or extent of relationships between variables.

Consequently, variance models widely omit any sequential or temporal details that may form a critical component of this relationship (Soh & Markus 1995; Ramiller & Pentland 2009). However, the process theory overcomes these limitations by emphasizing the dynamic view of the phenomena because it “…seeks to explain how independent variables (e.g., the context) shape the evolution of the process and, in turn, how the process influences dependent variables (e.g., outcomes)” (Radeke 2010, p.2).

Relevant Models of Process Theory

Coping Model of User Adaptation (CMUA)

Beaudry and Pinsonneault’s (2005) Coping Model of User Adaptation (CMUA), cited comprehensively in Fadel (2012, p. 2), “provides a useful theoretical basis for understanding users’ adaptive reactions to an IS and their consequent outcomes.” The originators of the model describe user adaptation “as the cognitive and behavioral efforts exerted to manage specific external and/or internal demands that are appraised as taxing or exceeding the resources of a person” (Beaudry & Pinsonneault 2005, p. 495). Building on the coping theory, the theoretical approach frames users’ responses to a novel workplace IS or innovation in terms of four phases namely

1. awareness of a new IS in the work environment,
2. appraisal of the IS, wherein the user assesses the positive and negative ramifications of the IS and the options involved in responding to it
3. adaptive acts in response to the IS which, in turn,
4. generate both external outcomes (e.g., improved efficiency or effectiveness using the IS) or internal outcomes (e.g., restored emotional outcomes).

Figure 1 next page demonstrates a simplified version of the CMUA.

CMUA has all the hallmarks of a process theory as it utilizes the focal actors/users and the events as the initiation of novel systems and/or adjustments of mature systems to explain how users adapt to technological innovations in their organizations. Owing to the fact that the process approach presupposes that entities (or focal actors) shift over time, the introduction of a new technological innovation in the organization might make a user concerned about his job security, which might, in turn, lead the user to react differently to other events (e.g., performance reviews) then he would have normally reacted in the absence of the new technological innovation (Burton-Jones et al 2011). Additionally, one of the outcomes under CMUA entails exiting the organization, which “occurs as a result of the following probabilistic sequence of events:

1. the user becomes aware of an IT event,
2. perceives it to be a threat,
3. perceives that they have little control over it, and
4. engages in self-preservation, by exiting the company” (Burton-Jones et al 2011, p. 13).

Consequently, in terms of theoretical relationships, it is evident that the sequence of events involving the factors under this theory is probabilistic rather than deterministic as it is feasible that a diverse sequence of events might occur (Wimmer 2002; Beaudry & Pinsonneault, 2005).

Lastly, it is important to demonstrate how CMUA is a process model using the causal logic dimension, which presupposes that no single event in a chain is considered sufficient to determine a subsequent event (Burton-Jones et al 2011) and that events can be determined by the goals of focal actors (ultimate causality) and/or their plans (formal causality) (Beaudry & Pinsonneault, 2005).

These four forms of causal relationships are predominant in Beaudry and Pinsonneault’s CMUA; for instance, users are not obliged to leave the entity unless an adverse event threatens them (obligatory causality), while the outcomes that ensue are to a substantial extent motivated by users goals to sustain their happiness (ultimate causality) as well as their approaches to adapt to organizational events (official causality). Similarly, the user may decide to exit the entity owing to an instantaneous antecedent such as self-preservation (efficient causality) (Burton-Jones et al 2011).

The Evolution-Revolution Model

Larry Greiner (1972) comprehensively cited in Van de Ven (2007, p. 24), employs the second meaning of process “as a developmental sequence of events, and proposes that organizational growth progresses through five stages of evolution and revolution:

1. creativity and leadership,
2. direction and autonomy,
3. delegation and control,
4. coordination and red tape, and
5. collaboration and revitalization.”

The framer of the process theory suggests that organizations enjoy growth without major economic setbacks on severe internal disruptions during the evolutionary time-frames, while revolutionary time-frames are usually earmarked by ineffectiveness, hence it should be the primary role of management to discover new practices with which to manage the organization during the next evolutionary time-frame.

Although this model is firmly grounded on a life cycle theory of change in which the author of the model employs historical events to determine the future growth of organizations, hence demonstrating an underdeveloped teleological component of the theory, the process approach is reinforced by the fact that the future of an organization may be less determined by external forces than it is by the organization’s history (Ionescu & Negrusa 2007), and that behavior is determined primarily by previous events and experiences, not by deterministic factors of what lies ahead (Van de Ven 2007). Consequently, such a model can be used in understanding “how” an organization goes about adopting new technology and “what” processes are important.

E-Learning Success Model

Following DeLone and McLean’s (2003) information systems success model, Lee-Post (2009) develops an e-learning success model which can be employed in the IS domain to guide the design, development, and delivery of e-learning initiatives through the use of six dimensions of success factors, namely system quality, information quality, service quality, use, user stratification, and net benefit. The author is clear that this model not only assumes an unambiguous process approach in appraising and evaluating success but also includes success metrics created exclusively for the e-learning context under examination.

Owing to the fact that the process approach speculates that the overall success of e-learning initiatives depends on the achievement of success at each of the three phases of e-learning systems development (design, delivery, and outcome analysis), it is important to underscore that success at the design phase is assessed along the dimensions of system quality, information quality and service quality, the success of the delivery phase is assessed along the use dimension, while the success of the outcome phase is assessed along the dimensions of user stratification and net benefits (Lee-Post 2009).

Background Theory: Process Model of L2 Motivation

Available literature demonstrates that this particular Process Model of L2 Motivation is substantially inspired by Heckhausen and Kuhl’s Action Control Theory, and “is intended both to account for the dynamics of motivational change in time and to synthesize many of the most important motivational conceptualizations to date” (Dornyei & Otto 1998, p. 43). As postulated by the framers, this motivational model grew out of a research project whose major objective was to devise motivational approaches focused on classroom intervention in the second language (L2) education as none of the previous models were considered entirely appropriate due to

1. inability to provide a sufficiently comprehensive and detailed summary of all the relevant motivational influences on learner behavior in the classroom,
2. absence of motivational theories and influences on action implementation, and
3. lack of motivational theories that demonstrated the fact that motivation is not a static state but rather a dynamically evolving and shifting entity (Dornyei & Otto 1998; Zafiropoulos et al 2012).

The authors also justify the need for the Process Model of L2 Motivation by arguing that

1. motivation is an intensely complex issue and the number of potential determinants of human behavior is extensive,
2. sustained learning processes of skill/knowledge acquisition entail different motivational characteristics, and
3. very few of the existing motivation theories contain a temporal dimension (Dornyei & Otto 1998).

Drawing from Heckhausen and Kuhl’s (1985) Theory of Action Control, Dornyei and Otto (1998) developed the Process Model of L2 Motivation consisting of two dimensions, namely Action Sequence (the behavioral process whereby initial wishes, hopes, and desires are first transformed into goals, then into actions, leading eventually to action and, hopefully, to the accomplishment of the goals, after which the process is submitted to final evaluation) and Motivational Influences (all the energy sources and motivational forces that underlie and fuel the behavioral process). Figure 2 demonstrates the schematic representation of the model.

From the schematic representation, it is evident that the sequencing process in the Process Model of L2 motivation is divided into three stages namely preactional stage, actional stage, and postactional stage. The preactional stage entails three sub-stages, namely goal setting (antecedents include wishes/ hopes, desires and opportunities), intention formation (involving commitment and choice to put the goal into action), and initiation of intention enactment (energizing action by translating the goal into concrete steps through availing the necessary means and resources as well as the start condition) (Dornyei & Otto 1998).

The actional phase takes place when the individual has committed him/herself to action, though the actual enactment of the action may be grounded on sources of motivation that to a large extent differ from those upon which the original decision was based (Dornyei & Otto 1998).

As demonstrated in the schemata, the actional stage consists of three sub-stages, namely subtask generation and implementation (implementing the learning behaviors/subtasks that were specified by the action plan), appraisal process (continuously evaluating the multitude of stimuli coming from the environment and the progress one has made towards the action outcome, comparing actual events with predicted ones that an alternative action sequence would avail), and the application of a multiplicity of action control mechanisms (processes which protect a current intention from being replaced should one of the competing tendencies increase in strength before the intended action is successfully completed) (Dornyei & Otto 1998).

Available literature demonstrates that “the postactional stage begins after either the goal has been attained or the action has been terminated; alternatively, it can take place when action is interrupted for a longer period” (Dornyei & Otto 1998, p. 51).

The main sub-stages in this phase include the formation of causal attributions ( actor or user comparing initial expectancies and plans of action to how they turned out in reality, with the view to developing causal attributions about the extent the intended objective has been achieved), elaborating standards and strategies (actor or user elaborates his/her internal standards and the repertoire of action-specific strategies so as to develop a stable identity as a successful learner), as well as dismissing intention and further planning to give way to new wishes, goals, and intentions to achieve other super-ordinate goals (Dornyei & Otto 1998).

The motivational influences on the different action phases include goal setting (e.g., subjective values and norms, environmental stimuli, family expectations), intention formation (e.g., self-efficacy, self-confidence, anxiety, perceived competence), initiation of intention enactment (e.g., perceived behavioral control, perceived consequences for not acting), actional processes (e.g., selective sensitivity to aspects of the environment, perceived contingent relationship between action and outcome, motivational influence, performance appraisal), and postactional evaluation (e.g., self-concept beliefs, self-confidence, attributional factors) (Dornyei & Otto 1998).

A Rationale for using Process Theory

In order to provide a better understanding of the complete picture of usage decision of e-government services by citizens, the study proposes to build a comprehensive usage process model of e-government services, which ensures that citizens’ readiness and adoption factors and events are fully integrated into one usage process. Usage decisions of e-government services are behavioral outcomes or social phenomena that may not be guided by an invariant relationship between antecedents and outcomes as is the case with variance theories (Markus & Robey 1988), hence the justification to employ a process model.

Moreover, within the context of establishing relationships, it can be argued that a process model may have a distinct advantage over a variance model if a behavioral outcome occurs only some of times when its antecedents are present owing to the fact that not all real-world phenomena will ultimately end up becoming deterministic if researchers spend enough time analyzing them (Radeke 2010).

Another justification for process-oriented theoretical perspective in this study is grounded on the fact that while process theories “…retain the empirical fidelity of the emergent perspective, they also preserve the belief in the regularity and predictability of social phenomena that characterizes the technological and organizational imperatives” (Markus & Robey 1988, p. 592).

Indeed, the process approach does not assume that there exist treated variable of the impact that can be isolated and independently quantified as models within this approach assume that the proposed factors or variables are no doubt important but they are far from being the only determinants as new factors may arise particularly in different environmental backgrounds (Burton-Jones et al 2011). Consequently, in the context of the current study, it may be difficult to understand what sequence of citizen readiness and adoption factors/events lead to a successful usage process of e-government services by citizens in the absence of prediction of patterned regularities over time, hence the justification for using a process model.

Additionally, unlike variance theory which attempts to explicate the variation in a dependent variable as a direct consequence of the variation in an independent variable(s), process theory attempts “to address the complex dynamics of a variety of fundamental organizational processes including adaptation, co-evolution, improvisation, selection, and self-organization, illustrating how a favored paradigm holds powerful sway over what we can and cannot see” (Chiles 2003, p. 288).

Consequently, it is generally felt that a process model would assist the researcher in integrating individual readiness and/or adoption factors and molding them into a holistic usage process, especially in terms of “what” factors can practically be used to increase the uptake of e-government services from the citizens’ perspective. It may be impossible to achieve this through examining invariant relationships between antecedents and outcomes that are inherent invariance theories based on the fact that such relationships may be too stringent to successfully explain social phenomena or a behavioral outcome which occurs only some of the times when its antecedents are present (Ramiller & Pentland 2009; Shuler et al., 2010).

Section’s Conclusion

Overall, this section has assessed existing literature on the process theory versus variance theory, as well as demonstrated the relevance of using process theory in this study. Additionally, the section has assessed the literature on four distinct process theories, namely the coping model of user adaptation, the evolution-revolution model, the process model of L2 motivation, as well as the e-learning success model. The process model of L2 motivation has received considerable attention in this section based on the fact that it is adopted by the researcher to form the background theory in assisting the researcher to build a comprehensive usage process model of e-government services that will provide a better understanding of the complete picture of usage decision of e-government services by citizens.

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