1. Introduction
Decision support systems (DSS) form part of the Internet of Things (IoT). IoT refers to a wide range of platforms, devices and technologies which are linked together on the world wide web (WWW); including varying communication patterns in different networks [
1]. The idea of IoT is based on the interaction between a vast array of smart things or objects including, but not limited to, mobile phones, sensors, radio frequency identification (RFID) tags, actuators etc., in order to reach common goals via distinctive addressing schemes [
2]. Therefore, DSS are essentially a part of IoT.
It was forecasted that IoT devices will be the largest category of connected devices in 2018 with 16 billion units [
3]. The Internet is the infrastructure for IoT and WWW is the application that permits access to this infrastructure. DSS is one of the things in the IoT that operates through internal networks, analyzes data to generate reports, and communicates through the intranet as well as the Internet. IoT has numerous field applications, from tracking energy consumption to connecting software applications that optimize the traffic routes, enhancing fuel efficiency by reducing traffic jams [
4] and extending assistance in biological studies [
5].
E-business and the Internet of things (IoT) have enabled businesses to bring innovation to their processes. Schumpeter [
6] defined an innovation as “something that reduces costs and increases quality and performance”. So, adopting Internet based platforms and electronic commerce is primarily an innovative step for any organization. Firms have fallen victim to technological change, sometimes due to a paradigm shift. Kodak is one such example of failure during the transition period to digital cameras and related technologies. Moreover, numerous studies have tried to determine the factors influencing the acceptance of a new technology. The contexts of these studies range from customer’s perspective, industry outlook to organizational context. However, employees’ perspectives are somewhat lacking when it comes to academic research and literature with respect to technology acceptance at individual level.
Adopting modern technology is a matter of survival for e-business firms due to its impact on sustainable business development. Innovation in the workplace is vastly technology-driven in modern business models and depends on information systems [
7]. Sustainability of a business model depends on its ability to adapt and to be flexible towards change, especially technological change in the current business environment. Sustainable management practices, during the continuous technological evolution in the workplace, are dependent on the core change agent i.e., employee behavior in response to such changes [
8,
9]. This rapid change in technological environment requires continuous change management practices in the workplace ensuring that employees are ready for the challenges in future [
10]. E-business information systems have huge potential to bring sustainable business development [
7]. Corporate sustainability is, in part, dependent on employees’ collative use of new and evolving technologies [
11]. Therefore, it is important to ascertain and research such factors that enable or inhibit employee perceptions about new technologies. Employee behavior is a key factor in enabling profitable and sustainable management practices. This study aims to contribute to this effect by studying factors that cause or inhibit employee readiness in order to enable better management of human capital, and to ensure sustainable corporate practices.
Existing literature focuses on understanding the reasons behind adoption and usage of technologies, which inherently means a paperless office. Moreover, the literature is mostly aimed towards sustainable business practices as part of corporate social responsibility (CSR) commitments [
11]. Firms operating in competitive industries must concentrate on managing sustainable human capital proactively through methods other than conservative techniques for a positive impact on innovation capability.
Sustainable management practices require a focus on employees from a holistic point of view. Employees create relational wealth through partnerships with their employers built over time. There must be coordination and motivation among employees at operational as well as strategic levels to ensure that corporate sustainability goals are achieved. Management must make use of employee capabilities in order to be able to work towards the shared vision of corporate sustainability [
12].
Bigi, Hamon-Cholet and Lanfranchi [
13] studied information and communication technology (ICT) from the perspective of human sustainability during computerization in the workplace as well as from a management practice viewpoint related to organizational change. They found out that the human sustainability of ICT and management changes depends on whether or not institutional context is taken into account during the implementation phase for new technologies. Thus, an employee’s role becomes vital in the institutional context.
This study mainly focuses on determinants of employee behavior towards technology use in e-business environments. The focus is narrowed down to DSS as the technology in question. DSS is, in essence, an application or software linked with one or more types of information systems. Moja, Passardi, Capobussi, Banzi, Ruggiero, Kwag, Liberati, Mangia, Kunnamo, Cinquini, Vespignani, Colamartini, Di Iorio, Massa, Gonzalez-Lorenzo, Bertizzolo, Nyberg, Grimshaw, Bonovas and Nanni [
14] studied computer-based DSS linked to the health record systems of patients. Masum, Beh, Azad and Hoque [
10] studied how human resource information systems (HRIS), when combined with knowledge discovery in database (KDD), perform the functions of DSS for structured, semi-structured and unstructured decisions related to human resources (HR). Thus, DSS may combine one or more features or software applications such as the web-based information system (WBIS), sales management information system (SMIS), customer relationship management system (CRMS), travel industry-specific global distribution systems (GDS), supply chain management (SCM) systems and online/e-payment systems integrated with each other. Such information systems may have varying levels of access to personnel as determined by the hierarchical levels. The ultimate purpose of these technologies is to help staff and managers make informed and accurate decisions about sustainable technological changes.
In the midst of this fast-paced change in technology, Big Data applications and collaboration initiatives between businesses, it is imperative for e-business managers to be skilled in acquiring information on market needs; analyzing such information systematically and making use of it in the development of novel goods and services [
15]. It is also crucial that managers bring employees onboard by ensuring an infrastructure that keeps employees and the workforce up-to-date with technological use. This is where the decision support systems play a vital role as an assistive technology. The sustainability of all such initiatives still relies heavily on employee behavior and competitiveness for reporting and decision-making.
Although this study’s respondents are employees, the hypotheses and conceptualization do not argue for a difference in outcomes for the core technology acceptance model (TAM). This study emphasizes employees as the users of technology; the difference between the employee and customer is that of the environment and the “choice”. An employee does not have a choice, but a customer does. Therefore, the factor-independent variables in this study are such factors that relate to workplace and job-related issues. It is pertinent to mention that the individual users are inherently the same human beings; the only difference is the environment i.e., the workplace for employees as compared to the marketplace for customers.
Table 1 shows how the management of an organization sees the customers and employees differently.
Although in recent years there has been a plethora of studies on technology readiness and technology acceptance from the consumer’s perspective, industrial context, or in the organizational adoption as a whole [
5,
10,
14,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37], there is scarcity of research from an employee’s perspective as the unit of analysis for readiness and technology acceptance [
11,
36,
38,
39,
40,
41,
42] in e-business environments. Moreover, no studies of such nature are found for technology intensive environments.
Therefore, the objectives of this research are threefold:
The study of employee’s behavioral intention to use DSS through TAM.
To analyze whether the four reflective dimensions of employee readiness for e-business (EREB) i.e., Benefits, Security, Collaboration and Certainty in an e-business environment, have an impact on perceived usefulness and ease of use for DSS.
To establish that the TAM model holds true for the employees as a user of high-end technologies under ‘mandatory’ technology use settings; as opposed to majority of past research which focuses on customers as users who have a choice to not use a given technology.
These objectives will provide an overall perspective on the managerial implications towards the identification of the role of these four dimensions in establishing positive behavioral intention to use DSS among employees for the successful roll out of new technologies in the work place. It shall support the management in proposing such measures that may help in the supervision of the four dimensions of EREB through appropriate managerial actions in order to successfully implement DSS. This study contributes in several ways. First, it combines and analyzes the relationship between two models i.e., EREB and TAM. Second, this study examines the behavior of employees as users and the relationship with perceptions of ease and usefulness. Third, it studies the resistance caused by employee concerns about job security, which may inhibit intention to use technologies such as DSS. Fourth, it shall contribute to existing literature by adding to the knowledgebase on the relationship of the constituent factors of employee readiness for e-business i.e., Benefits, Security, Certainty and Collaboration, with the perceived usefulness and perceived ease of use for DSS, which does not exist yet. Fifth, the individual user sample in our case is predominantly skilled in technology use and innovative to a certain extent because they are in technology-intensive environments where they would not be hired and employed unless and until they are considered technologically savvy; this is not the case in the majority of past studies in extant literature where users are rarely known to be technology-savvy and are not targeted from within a technology-intensive population.
Although it can be argued that employees or customers are both in fact individuals and the expected behavioral outcomes could be similar, this study is carried out in organizations where employees must use new technology in their general tasks; whereas customers are not bound to use technologies whenever they are introduced to them by an organization, they can choose to use them or not.
The remainder of the paper is structured as follows.
Section 2 discusses the relevant concepts from the existing literature related to DSS, TAM, employee readiness for e-business, and change readiness.
Section 3 explains the hypotheses, methodology and measures used in this study to analyze the data.
Section 4 constitutes discussion on the measurement model, structural model and mediation results along with the model strength and quality.
Section 5 elaborates the application and implications of the results of our study and presents relevant suggestions for managers and practitioners.
Section 6 provides concluding remarks with limitations of this research and proposed future research.
4. Results
SmartPLS 3.2.7 (Boenningstedt, Germany) [
109] and IBM SPSS Statistics 22 (Armonk, NY, USA) were used in this study. However, data analysis was conducted using smartPLS primarily because PLS-SEM is a preferred option due to its better predictive power over factor-based SEM [
109]. Choosing PLS was encouraged over other CB-SEM softwares because smart-PLS can simultaneously estimate relationships between several independent as well as dependent variables in a structural model and multiple latent observed or unobserved variables in a measurement model [
110].
Furthermore, PLS is believed to be a preferable approach for decision-making and management-oriented problems; it is also preferred when the study focuses on prediction [
9]. In addition, PLS is the best choice in situations where other methods fail to converge; or when developed solutions are inadmissible. This holds true regardless of whether a common factor or composite model data is used [
108,
111]. Moreover, normality is required, and is a critical assumption of CB-SEM based software. Normality was a problem in our data when the Shapiro–Wilk test was conducted to find out if our data departed from normal distribution. Therefore, PLS was a preferred option because PLS can deal with skewed data and multi-colinearity issues more robustly [
101].
4.1. Measurement Model
The assessment of the measurement model was carried out using the reliability and validity of the reflective indicators. The internal consistency reliability was established using composite reliability (CR) which has been recommended by scholars as being a better representative measure for reliability as compared to earlier practices that use Cronbach’s alpha, as PLS does not require all indicators to have equal reliability [
112], which is a limitation in other softwares.
Although the majority of the items displayed outer loading of above 0.70 to ascertain the reliability of latent variables, some weaker indicators with loadings between 0.460 and 0.69 were retained because of their contribution to content validity [
113]. Keeping in mind that the minimum level of 0.40 is an acceptable value for item loading [
107], none of the items had to be removed because all loadings were above 0.40. Moreover, all the constructs showed high composite reliability scores of above 0.8.
Table 4 shows that the values ranged between 0.832 and 0.889, thus confirming sufficient reliability [
112].
To assess the construct validity by examining both the convergent and discriminant validity, suggestions by [
113] were followed and 0.5 or higher was set as the acceptable value of average variance extracted (AVE) [
114].
Table 5 shows that all the constructs had AVE values greater than 0.5 and ranged between 0.504 and 0.729, thus confirming convergent validity [
115,
116]. We assessed the discriminant validity using both Fornell–Larcker and Heterotrait–Monotrait (HTMT) criteria [
111].
Fornell–Larcker criterion and the examination of cross-loadings are the “dominant approaches for evaluating discriminant validity … and do not reliably detect the lack of discriminant validity” [
111,
116]. It was, therefore, decided to additionally report the Heterotrait–Monotrait (HTMT) ratio of correlations given in
Table 6 below. Square roots of AVE values are shown in italics at diagonal.
As shown in
Table 7, all of the variables displayed acceptable discriminant validity using the HTMT test as well as bearing values below thresholds of 0.90 [
111,
112].
4.2. Structural Model
To assess the structural model, a three-stage approach was carried out by the authors [
111,
112,
113]; firstly, the R
2 value was obtained for each latent variable. Secondly, a redundancy check of Q
2 was calculated by using a blindfolding function to ascertain the quality of predictive relevance. Thirdly, the bootstrap function was used to assess whether the path coefficients of the structural model are significant or not and if their effect size is sufficiently big enough. A one-tailed test was used because of the predetermined direction of relationship between all hypothesized variables of the theoretical framework. A 5000 bootstrap sample was used for this study constituting the same number of observations as that of the original sample in order to generate the standard errors and t-values [
101]. In addition, the interaction effect was checked through f
2 values representing effect size. The coefficient of the determinant “R-square” value represents how much variance in a target variable is explained by the effect size of the independent variables linked to it [
113]. Chin (1998) recommended benchmark values for R-square as 0.67 (substantial), 0.33(moderately strong) and 0.19 (weak).
(Security β = −0.121; Collaboration β = 0.296; Certainty β = 0.256) along with PEU (β = 0.085) explained 50.2% variance in PU. Whereas 58.2% of the variance in PEU was explained by the four EREB dimensions (Benefits β = 0.102; Security β = −0.023; Collaboration β = 0.529; Certainty β = 0.230). Moreover, PEU (β = 0.243) and PU (β = 0.555) explained 51.8 % of the variance in IU.
Benefits had a significant positive effect on PEU (t = 2.295, p = 0.022) as well as PU (t = 3.383, p = 0.001). Security concerns of employees showed an insignificant (although negative) relationship with PEU (t = 1.577, p = 0.115); however, it did prove to have a significant negative relationship with PU (t = 3.050, p = 0.002) as initially hypothesized. Certainty had a significant positive relationship with PEU (t = 4.046, p < 0.001) and PU (t = 3.669, p < 0.001). Collaboration also had a significant positive effect on both PEU (t = 10,914, p < 0.001) as well as PU (t = 5.677, p < 0.001). Moreover, PEU (t = 5.479, p < 0.001 and PU (t = 13.182, p < 0.001) both had a significant positive effect on intention to use.
The t-value test for level of significance has been calculated by using two-tailed estimation (Hair et al. 2013).
Table 6 shows the t-values and p-values indicating that Security did not prove to have a significantly negative relationship with perceived ease of use (t = 0.596,
p = 0.551) and PEU did not prove to have any significant effect on PU (t = 1.577,
p = 0.115). All other direct relationships proved to be significant with t-values well above a threshold of 1.96 and p-values of less than 0.05.
Keeping this in mind, and based on the t-value rule of thumb for interpretation of a two-tailed test i.e., t = 1.96, all the hypotheses were supported with two exceptions, namely H1 and H5a.
Figure 2 displays the path coefficient values and t-values (in parentheses) along with the R-square variance in perceived ease of use, perceived usefulness and intention to use, as explained by other independent variables.
4.3. Model Strength and Quality
As recommended by [
105], f
2 values of 0.02, 0.15 and 0.35 indicate that the interaction term is low, medium, or large on the criterion variable respectively. A Q
2 value of greater than zero implies that the model has good predictive relevance [
116].
Table 8 shows the results of the model obtained through PLS Algorithm function under smartPLS software calculate the table.
Table 9 displays the values for f
2 were obtained from the measurement model results and the Q
2 values obtained through the blindfolding function. It is evident from the figures that the relationship paths from BEN to PEU (f
2 = 0.017), SEC to PEU (f
2 = 0.001) and PEU to PU (f
2 = 0.006) bear a low interaction because f
2 values are below the minimum 0.02 threshold. The strongest interaction term was between PU and IU with a substantially large f
2 value of 0.439. Similarly, the COL to PEU path was also very strong with an f
2 value of 0.362.
The R-square values for IU, PU and PEU were 0.515, 0.494 and 0.577 respectively, displaying a moderately strong explanation of variance by the independent variables. The Q2 values for all the relationships were above zero, thus meeting the minimal criterion as required by the existing literature.
4.4. Higher Order Construct of EREB
In order to check whether the relation between EREB and intention to use is partially or fully mediated by PEU and PU, higher order construct of EREB was created by using a two-step approach recommended by Becker, Klein and Wetzels [
117] in cases where the different constructs have different numbers of items in order to assure relatively lesser bias in results. To obtain this, we convert the latent variables into items for the higher order construct, i.e., in this study, Benefits, Security, Collaboration and Certainty are four variables with varying numbers of items, we shall use them as items for a higher order construct EREB. In order to achieve this, we run the four lower order variables in the model using the PLS Algorithm in SmartPLS. From the measurement model results output, all the ‘Latent variable scores’ were copied into the original data file and saved as new items. Then this newly changed data file is used as the source data file to create EREB as a latent variable using the latent variables Benefits, Security, Collaboration and Certainty as items. As Becker, Klein and Wetzels [
117] recommended, the measurement model is run again and the reliability and validity of the higher order model is also checked, just as it was done above for the lower-order constructs.
Table 10 displays the relevant values for measurement model results.
The internal consistency reliability of the higher order construct EREB was established using a composite reliability value of 0.826. The rho A value was 0.767 while Cronbach’s Alpha was 0.714. Moreover, the convergent validity was established through the AVE value, which was 0.552.
A 5000-sample bootstrap was run to check the model with higher order construct. Results displayed in
Table 11 and
Figure 3 below indicate that that EREB explained 54.3% variance in PU and 46.9% variance in PEU. Moreover, PEU, PU and EREB together explained 48.4 % variance in IU. All the relationship paths had f-square values of above 0.15, which signifies a moderately strong relationship [
105], except for PEU to PU (f-square = 0.014) which means the relationship is not as meaningful as it was below the minimum 0.02 benchmark. This was consistent with the lower order model run previously with four latent variables of EREB.
4.5. Mediation Results
Mediation was assessed using the variance accounted for (VAF) method recommended by Preacher and Hayes [
118] and also suggested by Hair, Hult, Ringle and Sarstedt [
106]. PU and PEU were run together in the model instead of separately. They were added one-by-one to the model, which is similar to the method adopted in a recent study by Martinez-Martinez, Herrera Madueno, Larran Jorge and Lechuga Sancho [
119]. It was found that PEU and PU both fully mediate the relationship between EREB and IU, as the variance accounted for value exceeds the 80% benchmark devised by Preacher and Hayes [
118]. The variance accounted for is calculated by dividing the point estimate (multiplication of path values) by the total effect of the independent variable (IV) on the dependent variable (D.V).
As per the method devised by Preacher and Hayes [
118], there is no mediation if the VAF value is less than 20%; full mediation if the VAF value is 80% or above and partial mediation when the values of VAF lies between 20% and 80%. The individual paths for PEU and PU were observed to partially mediate the relationship between EREB and IU. PEU’s VAF value was 29.45%, which suggests partial mediation. PU’s VAF value was 60.80%, which also indicates partial mediation. The sequential path through both PEU and PU showed no mediation as the VAF value was 9.73%.
Table 12 shows the values and calculations involved. Individual mediations via PEU and PU were separately calculated as below:
5. Discussion
Firstly, the results show that employees who display e-business readiness tend to focus on the benefits of the new technology or software and are more inclined to utilize it in job tasks for improved performance, efficiency and gaining greater control in job tasks leading to greater job satisfaction [
18]. This helps expedite the DSS implementation process. Expected benefits from the use of technology are that it motivates employees and creates a perception of ease of use and usefulness, as found in our study. As Lee, Park and Bakers [
93] argue, in the present day competitive job market, employees must enhance skills and abilities to be of value to their employer, which is why the employees place more value on any learning opportunity that could enhance their competency. New technology introduced in the workplace is one such opportunity that brings benefits for employees and leads to satisfaction from learning achievement outcomes and job performance; as a result, bringing a perception of usefulness leading to intention to use of DSS.
Secondly, this study shows a positive relationship of Certainty with PEU and PU, portraying that employee trust in management creates better readiness. Previous research conducted from an employee perspective [
9] also showed similar results where trust showed a significant effect on e-business value creation. Whenever an infrastructure for training and support is available to employees, it builds trust in management and creates certainty in their perceptions towards DSS adoption and use; employees receive it well with the certainty that the organization and management have the capability [
18] to carry out the successful implementation of DSS. Employees who display higher certainty about management’s capabilities and trust that e-business readiness will be helpful in supporting change initiatives, including technology changes such as DSS, simultaneously creating intention to use DSS.
Thirdly, based on the results, collaborative efforts create an environment of learning and knowledge sharing, which promotes the perception of ease of use and usefulness through shared experiences by employees. It implies that participative behavior promotes perceptions of ease and usefulness. Lai, Kan and Ulhas [
9] also found that participation by employees was significant in creating e-business readiness. Management should thus motivate employees through a systematic method [
120] both emotionally as well as technically. Encouraging employees to collaborate and participate could be done through training specific to e-business processes involving the use of DSS. This in turn can result in greater commitment towards e-business readiness and technology use while aligning business goals with employee job satisfaction.
Fourthly, contrary to the other three dimensions of e-business readiness, employee security concerns play a volatile role if not addressed properly by management. Results show that concerns were negatively related to perceived usefulness of any given technology. This brings resistance to change by employees. However, this can be countered by maintaining a healthy environment in the workplace by ensuring an individual-technology fit and task-technology fit through training. This is in line with previous studies stating that employee professional-development opportunities are important for employees to maintain their job security [
93]. By knowing the capabilities of the workforce, identifying the technological knowledge gap and eliminating these gaps through training, monitoring and feedback, employee security concerns can be mitigated.
Results also showed job security has no significant impact on PEU for DSS, mainly because of the underlying fact that in case of job security concerns, an employee would not be bothered as to whether a technology is easy to use or not as they are more concerned with retaining the job role; it is not an immediate concern for an employee since it could cause them loss of influence, change in their job role or even unemployment. Employees’ perceptions about job insecurity are closely associated with their behavior towards technology use [
76]. It causes stress for employees and can cause anxiety and other negative outcomes [
79]. Once an employee sees the technology as a threat or feels insecure, the focus shifts from the technology to his or her own survival and fear of unemployment in the long run [
78]. It can, therefore, be argued that when an employee is concerned that new technology may not be useful for them, the focus shifts to job security. Stress and anxiety stemming from a new technology in the workplace make an employee indifferent to the fact of whether it is easy to use or not. Similarly, the same line of argument holds true for the insignificant path from PEU to PU. If a technology is easy to use, employees will continue to use it; they will not stress about whether it is useful or not. Job insecurity results in feedback seeking behavior by employees [
82], usefulness of a technology will not depend on whether it is easy to use or not and an employee’s technology usage behavior remains stable if the new technology does not threaten their job security [
76].
6. Theoretical and Practical Implications
The present study contributes in three ways. Firstly, it combines and analyzes the relationship between two models i.e., EREB and TAM, which has not been done before. Secondly, this study examines the level of employees’ e-business readiness and its relationship with technology acceptance. Moreover, it studies the resistance to DSS because of being inhibited by employee security concerns. Thirdly, the results above show that three constituent factors of EREB have positive effects on the perceived usefulness and perceived ease of use in the use of decision support systems.
Information systems, unlike the personal use technologies, are primarily utilized in work-settings for performance of job tasks. By assessing the IS adoption and its use, this study makes a contribution to technology acceptance model literature by empirically showing that adoption and continued usage of group-based technologies in the e-business workplace is dependent on employee readiness. This is determined by individual employee’s perceptions about the four dimensions i.e., Benefits, Collaboration, Certainty and Security, stemming from existing organizational culture regarding employee preparedness.
This study not only affirms the past findings for the core constructs of the TAM model i.e., PEU and PU; it also further introduces the four constructs of EREB as predictors of PEU and PU. It studied PU and PEU strictly from an employee’s point of view as a user of technology. Moreover, PEU and PU fully mediate the relationship between higher order EREB constructs and intention to use DSS. The results affirm that perceived benefits of a technology shall create a positive effect on PEU and PU. Expected benefits from the use of DSS create positive intention to use DSS through perceived usefulness. Certainty and employees’ perception about the management’s ability to successfully implement DSS shall lead to positive perceptions about PEU and PU and creates positive intention to use DSS. Collaboration and participation by employees accelerates the adoption as it creates a harmonious perception among employees. An environment of collaboration and sharing in the workplace shall lead to intention to use DSS and is mediated by perceived usefulness and ease of use.
Although technology has been discussed in literature with ample evidence provided by proven studies, the evaluated phenomenon on most occasions tends to be the overall ability of the organizational adaptive capability, the customer’s readiness for a particular technology, or the supplier’s integration with the system between buyer and supplier from a supply chain point of view. However, there is lack of substantive research on readiness of employees for e-business in conjunction with technology acceptance for a decision support system.
More specifically, technology acceptance research is comparatively much less focused on the employee as the unit of analysis. This issue lacks academic research from an employees’ perspective when it comes to technology-intensive e-business firms as well. This research studies the construct “employee readiness for e-business” (EREB) proposed by [
18] and developed as a multiple-item measurement scale in order to assess the level of employee readiness for e-business tasks while purely focusing on the employee’s perspective as the user of technology. Thus, this study adds to the knowledgebase on the subject of technology usage behavior in e-business firms from a change management viewpoint and the employees’ perceptions about new technology in the workplace. It will be useful for researchers and practitioners interested in designing, implementing, and managing e-business technologies.
New technology or software inherently brings about change [
35]. Whenever an information system is introduced into the organizational setting, there is bound to be a change in the previous process and the procedures of running routine business matters and job tasks. Thus, business process re-engineering comes into play to enable a smooth transition into new procedures and processes. Readiness for change is dependent on a multiple number of factors. From the start, managers and practitioners need to create awareness and collaboration through training sessions, closed group discussions, planning and building trust with employees before a new information system is introduced.
Therefore, there is need to build an optimistic environment and minimize resistance towards technological changes in the workplace. This brings us to the widely researched task–technology–fit (TTF) concept [
121] that mainly focuses on “the appropriateness of the technology to the task” and employees’ perceptions towards the degree to which functions of a technology assist them in performing their work.
More specifically, in e-business environments, in particular the benefits of using Decision Support Systems as perceived by employees, and information systems in general, is likely to increase when a task-technology and an individual-technology fit is ensured primarily because it builds a rapport of usefulness as it sits well with employee values and ease of use because they feel it is made specifically for their tasks [
19].
It is argued that DSS implementation can be divided into two dimensions [
122]. The first is termed ‘technology performance’ wherein DSS usage is aimed at fetching better outcomes and recommendations from the use of technology (technology performance). The second is termed as ‘task performance’, which entails utilizing outputs of a given DSS. Past empirical research shows that when technology performance enhances, it leads to better task performance [
57,
122,
123].
Table 13 presents a matrix for a combination of anticipated perceptions that each dimension of EREB creates for employees and what steps management personnel should introduce beforehand to lead to a successful transition to a new technology or to ensure the continued use of existing technology.
Integration of resources and systemic storage, transmission and analysis of business information is an imperative objective at present day organizations as they try to integrate the operations between employees, departments, vendors, and suppliers to optimize processes and add value to business. Thus, there is an increasing focus on implementation of electronic business (e-business) activities. This issue further fuels debate on determining employee readiness towards this new type of organization [
18]. However, the firms who are considered e-businesses have to upgrade existing technology or introduce new technology with the passage of time.
A task-technology fit needs attention at the beginning of the very process of buying/building a new information system. When the task is the center of attention when building the IS, it brings harmony in job task performance without causing employees anxiety. A well-thought out DSS reduces the gap between an individual’s ability to attain a certain level of expertise and the need to achieve certain level of task performance. The individual-technology fit is to be handled on a continuous level [
123]. This fit, if not established by the management for the employees, leaves a gap in the employees’ preparedness and readiness due to probable security concerns related to their job, arising from changes brought by the new technology i.e., information systems or decision support systems.
It is not necessarily the insecurity of losing the job altogether, but in fact, the fear of losing influence amongst peers, losing their power at workplace or simply a change in job role. Before selecting, building or introducing new information, if employee feedback is obtained to make them feel included in the process of technology adoption, it will serve as a mode of building trust, creating readiness for change, and inherently ensuring a task-technology and an individual-technology fit.
Regular and routine inculcation of training, idea-generation and feedback sessions tend to build trust and certainty among the employees and enhances their perception of the management’s ability to successfully implement the new technological changes, leading to improved technology adoption behavior and continued intention towards technology use. Moreover, collectivism built by this trust also helps employees to be more open and more collaborative as a result of the environment of ease and satisfaction. It is always a collaborative effort that results in the successful implementation of technology in any organization, the employee being the basic unit of change.
7. Conclusions, Limitations and Future Research
The management has a key role to play in creating positive perceptions about the EREB dimensions i.e., Benefits, Certainty, Security and Collaboration. This can be achieved through the inculcation of regular trainings, feedback, offering rewards and ensuring a secure job environment. Moreover, employee security concerns are inversely related to ease of use and usefulness of decision support systems. The two hypotheses that were not supported i.e., Security->PEU and PEU->PU, both indicate that once an employee feels anxiety about losing power and authority in their job role or even fears unemployment due to the introduction of a new technology; the primary focus shifts from perceptions about ease of use or usefulness to anxiety, fear and survival.
It can be argued that optimizing output is inherently a key part of human nature. The behavioural outcomes with respect to perceptions about ease of use and usefulness are similar for both employees and customers because they are devoid of any influence from the environment in which a technology may be introduced i.e., whether it is in workplace settings for employees or in marketplace settings for customers. The variables PEU and PU are being measured for employees in this study with the modified version of the same scale and instrument used for customers in past studies. The results are also similar because the TAM model does not differentiate between customers and employees as “users” of technology.
This study is unique because it introduces EREB as predictor of PEU and PU in technology adoption literature. Moreover, PEU and PU fully mediate the relationship between higher order EREB construct and intention to use DSS. The results are unique and significant because they provide four new dimensions of employee behavior that are vital in building perceptions about ease and usefulness. Firstly, if employees perceive that there will be higher benefits from use of a new technology, they will be more willing to adopt it because of higher PEU and PU. Secondly, another unique and significant finding of this study is the relationship of certainty with PEU and PU. If the employee’s perception about management capability to successfully implement changes at work (certainty) is high, then it shall lead to a higher level of PEU and PU leading to intention to use DSS. Collaboration and participation by employees accelerates the adoption as it creates a harmonious perception among employees. An environment of collaboration and sharing at workplace shall lead to intention to use DSS and is mediated by perceived usefulness and ease of use.
The two hypotheses that were not supported i.e., Security->PEU and PEU->PU, both indicate that once an employee feels anxiety about losing power and authority of his job role or even fears unemployment due to introduction of a new technology; the primary focus shifts from perceptions about ease of use or usefulness onto anxiety, fear and survival.
This study adds new theoretical perspective of employee behavior in technology adoption instead of consumer behavior. Employee is studied as a unit of analysis as against past literature which focuses on consumer as a unit of analysis for technology adoption for e-business firms. It is a theoretical contribution of this research because it uses TAM model to study user behavior from employee’s perspective. Past literature on technology adoption shows studies from consumer behavior, student behavior or teacher’s behavior. However, it lacks studies on behavior of employees of e-business firms.
The methodological contribution of this study is that it was known that the sample (respondents) is experienced and expert in technology use and works in a technology-intensive organization. This is different from past studies where mixed respondents have been used as sample. Either past studies do not bifurcate between experienced or non-experienced users or it is not known in majority of studies whether the sample is made of respondents who are adept in use of technology and whether they work in a technology-intensive environment or not.
Mandatory use of technology vs. choice of use; it is mandatory for individual users who do not have a choice in adoption of technology; instead they have to comply with company-wide regulations. So, this aspect makes it a certain factor that it will affect the job security of employees.
This study not only identifies four factors that shape employee behavior towards technology adoption in e-business firms, but also suggests a specific managerial course of action for each of these four factors. Managerial actions proposed above aim at creating a positive employee behavior by paying attention to the extrinsic as well as intrinsic motivational factors behind these four specific dimensions that constitute EREB.
This study also has its limitations. Firstly, this study followed a cross-sectional study design; although it is a common practice in similar research, researchers still consider this a limitation. Secondly, even though the sample size in this study was appropriate from the analysis and theoretical point of view, the authors consider the sampling method to be a limitation. Secondly, the sample was taken from the UK, a larger sample size with a more diverse geographical range of respondents from another country or multiple countries shall further enhance statistical power to achieve more generalizable results. Thirdly, the sample was also restricted to technology-intensive travel and tourism companies; it does not cater for the businesses that are not categorized as e-businesses and are potentially looking forward to becoming e-businesses. Fourthly, this paper studied the impact on employee-perceived benefits of e-business—Certainty, Security and Collaboration—as antecedents of TAM. However, these are not the sole determinants of employee readiness in a firm’s overall technology readiness; the technology readiness index is one such example of another measure. Thus, technology acceptance could also be driven by certain other employee-specific or firm-specific antecedents which were not part of our study. Last but not least, this study’s sample is limited to SMEs, it does not cover micro-sized organizations in the UK.
Keeping the aforementioned limitations in mind, we suggest using a wider variety of firms with a broader number of fields such as SEOs, social media marketing as well as other digital advertising and service-oriented firms for possible future research. It is suggested that micro-sized organizations in the UK may also be included in future research. We also suggest including factors other than institutional support from a firm-specific viewpoint; employees’ level of ‘Readiness for Change’ and ‘Perceived Personal Competence’ in technology as antecedents of EREB or mediators/moderators between EREB and TAM. Furthermore, considering that there has been some research carried out in previous studies on relationship between the TAM and TTF models, future research may be carried out to check a moderating effect of TTF on the relationship between the dimensions of EREB and TAM.