The effect of electronic human resource management on electronic human resource management macro-level consequences: the role of perception of organizational politics

Abstract

Purpose

The purpose of this paper is to investigate the mediating role of perceived organizational politics on the relationship between electronic human resource management (e-HRM) use and e-HRM macro-level consequences.

Design/methodology/approach

The paper uses a cross-sectional survey of HR professionals, line managers and information technology specialists. A purposive stratified sampling technique is employed. The analyses of data make use of regression and process macro in SPSS analysis.

Findings

The effect of e-HRM use on e-HRM macro-level consequences is partially mediated by perceived organizational politics.

Practical implications

Organizations can invest in e-HRM use alongside other HR practices such as, emotional intelligence training, to reduce the negative effects of perceived organizational politics and in the process enhance employee attitudes and performance.

Originality/value

The study enriches the scope through which the interaction between e-HRM use and perceived organizational politics is viewed. The study was conducted in Zimbabwe, demonstrating that the indirect effect of e-HRM use on e-HRM macro-level consequences is not limited to developed economies.

Citation

Nyathi, M. (2024), "The effect of electronic human resource management on electronic human resource management macro-level consequences: the role of perception of organizational politics", African Journal of Economic and Management Studies, Vol. 15 No. 1, pp. 1-14. https://doi.org/10.1108/AJEMS-04-2022-0168

1. Introduction

Electronic human resource management (e-HRM) is defined as “the implementation and delivery of HR functionality enabled by a HR Information System that connects employees, applicants, managers, and the decisions they make” (Johnson et al., 2016, p. 282). The introduction of this phenomenon (e-HRM) in organizations is premised on the attainment of intended organizational outcomes, herein referred to as e-HRM macro-level consequences. Empirical findings point to a combination of intended and unintended consequences (Strohmeier, 2009; Parry, 2011; Parry and Tyson, 2011; Bondarouk and Ruel, 2013; Marler and Fisher, 2013; Bondarouk et al., 2017, 2019; Galanaki et al., 2019). e-HRM use has been found to increase and decrease efficiency (Parry, 2011), empower and disempower employees (Strohmeier, 2009), reduce and increase headcount (Parry and Tyson, 2011). It is still unclear how these inconsistencies could be minimized (Parry, 2011; Strohmeier and Kabst, 2014; Obeidat, 2016), as their existence is detrimental to the investment in e-HRM applications that operationalizing the phenomenon. Factors that may play a mediating or moderating role have been muted to ameliorate the challenge (Strohmeier and Kabst, 2014; Obeidat, 2016).

Research on the effect of e-HRM use on e-HRM macro-level consequences has so far focused on the technology imperative, at the exclusion of employee behavior (Strohmeier, 2009). The indirect relationships between e-HRM use and e-HRM macro-level consequences have been largely ignored (Obeidat, 2016). With the inconsistencies prevalent, several authors have proposed the need to embrace an emergent perspective (Strohmeier and Kabst, 2014; Bondarouk et al., 2017; Galanaki et al., 2019). According to the latter perspective, information technology can alter the occupational and organizational structure of work, leading to enhanced employee and organizational performance. These outcomes are not due to the inherent characteristics of e-HRM, but emerge from an unpredictable interaction between information technology and employees (Strohmeier, 2009; Galanaki et al., 2019).

One such variable emanating from this interaction is employees' perceived organizational politics. Viewed in a number of studies, as an organizational stressor (Vigoda, 2003; Ram and Prabhakar, 2010; Perrewe et al., 2012; Bodla et al., 2014; Rosen and Hochwarter, 2014; Saleem, 2015; Chinomona and Mofokeng, 2016; Mazzola and Disselhorst, 2019; Opoku et al., 2020), perceived organizational politics alongside e-HRM use can also be viewed as a necessity for organizational growth (Maslyn et al., 2017; Mazzola and Disselhorst, 2019). This positive aspect of perceived organizational politics has received little attention from researchers. The few studies that have focused on the positive aspects of organizational politics have been confined to developed economies (Khan et al., 2019). The focus on Zimbabwe, a developing economy, has been largely motivated by an increase in the adoption rate of e-HRM systems. The increased adoption rate, has taken place against the backdrop of scarce resources, within the organizations. Khan et al. (2019) argue that the intensity of organizational politics is higher in settings where resources are scarce, as this is fertile ground for power conflict. The scarcity causes anxiety amongst employees leading to power conflict. Such work environment is likely to be pregnant with challenge stressors, antecedents for a positive contribution by organizational politics to organizational success.

The study suggests a mediation effect of “perception of organizational politics” on the relationship between e-HRM use and e-HRM macro-level consequences The goal is to find out the existence of an indirect effect of e-HRM use on e-HRM operational, relational and transformational consequences, through perception of organizational politics, in developing economies. Operational consequences focus on improving organizational efficiency and effectiveness, by automating administrative HR tasks (Galanaki et al., 2019; Bondarouk, 2020). Relational e-HRM allows managers and employees “remote access to HR information, empowering them to perform HR tasks themselves and extending their ability to connect with other parts of the company and outside organizations” (Galanaki et al., 2019, p. 5). Transformational consequences capacitate the HR function's ability to contribute to organizational performance through supporting an organization's business strategy (Panos and Bellou, 2016; Galanaki et al., 2019; Bondarouk et al., 2019).

The model under study is shown in Figure 1. The study took place among Zimbabwean organizations. Based on previous research on e-HRM, it relies on the unified theory of acceptance and use of technology (UTAUT) to examine the indirect relationship between e-HRM use and e-HRM macro-level consequences (Odeidat, 2016).

2. Theoretical framework

This study is premised on three theories: the transaction cost theory (1991), the institutional theory with sensemaking theory (2009) and the challenge-hindrance model (2000). The transaction cost and institutional theories underpin our understanding of the effect of e-HRM use on e-HRM macro-level consequences, whilst the challenge-hindrance model helps justify the mediating role of perception of organizational politics variable.

1. The transaction cost theory

The transaction cost theory (1991) states that organizations' adoption of information technology (IT0 is motivated by a desire for cost-minimization). Cost saving “is the driving force behind organizations’ complex, partially outsourced, partially decentralised and partially delegated e-HRM systems” (Poisat and Mey (2017, p. 2). The adoption of IT is seen as lowering transaction costs of carrying out HR functions. IT allows information to be communicated in real-time and at much lower costs, thereby facilitating informed decision making, and ultimately organizational efficiency and effectiveness. The theory explains operational goals of introducing e-HRM systems (Strohmeier, 2009; Bondarouk, 2020).

1. Institutional theory with sensemaking theory

The sensemaking theory addresses mechanisms for dealing with change such as the introduction of IT (Jensen et al., 2009). The implementation of e-HRM interrupts ways in which employees work. This interruption causes “shock” that triggers an intensified period of sensemaking the rationale of the change effort.

The theory posits that employees interact with technology and try to make sense of it. This sensemaking causes them to either develop positive or negative expectations toward it (Jensen et al., 2009). When positive expectations develop, there is user technology acceptance and support (Bondarouk and Ruel, 2013). This causes employees to believe that using a new system would enhance job performance. If users of e-HRM systems are involved in the adoption process, employees are able to attach meaning to the technology (Bondarouk and Ruel, 2013). In this study, the employees' sense-making of IT induced change, results in information system ownership, understanding and utilization, leading to intended e-HRM macro-level consequences.

1. Challenge-hindrance stress model

Research has so far portrayed perceived organizational politics as an organizational stressor (Landells and Albrecht, 2017; Opoku et al., 2020). Stressors are characteristics of the work environment that cause strain, and strains are the label for the resulting poor psychological or physical well-being (Opoku et al., 2020). Ill-health, anxiety and burnout are examples of strains that could result from experiencing workplace stressors. The challenge-hindrance model identifies workplace stressors as falling into two categories: hindrance stressors and challenge stressors. Hindrance stressors interfere with performance goals. They do not provide opportunities for growth and development, e.g. poor equipment, ambiguity and interruptions that prevent employees from performing their jobs well (Opoku et al., 2020).

Challenge stressors on the other hand, contribute to performance opportunities. They provide opportunities for feelings of accomplishment, growth and development, e.g. workloads, and deadlines (Mazzola and Disselhorst, 2019). Successful management of high workloads and scheduled deadlines by employees create a sense of achievement resulting from high performance. This success also enhances the promotional prospects (Mazzola and Disselhorst, 2019). The phenomenon could therefore play a mediating role between variables under study. Contrary to numerous conclusions on the negative effect of stressors, positive outcomes have been recorded. Challenge-related stressors have been shown to be positively related to job satisfaction (Opoku et al., 2020), organizational loyalty (Spurk et al., 2021) and job performance (Abbas and Raja, 2019). This study posits that the work environment is characterized by the presence of challenge stressors. Consequently, these challenge stressors induce increased job satisfaction from opportunities of accomplishment, growth and development.

2.1 Conceptual development

2.1.1 e-HRM use

e-HRM is defined as “as a set of configurations of computer hardware, software and electronic networking resources that enable intended or actual HRM activities (e.g. policies, practices and services) through coordinating and controlling individual and group-level data capture and information creation and communication within and across organizational boundaries (Marler and Parry, 2016; Galanaki et al., 2019). The phenomenon consists of elements, features and characteristics that have to be integrated in order to deliver intended organizational outcomes. e-HRM should blend the HR activities and information technology tools for desired effects (Galanaki et al., 2019).

2.2 Perception of organizational politics

Perception of organizational politics is defined as a process through which employees give meaning to their environment after organizing and interpreting their sensory impressions (Landells and Albrecht, 2017). It looks at ways in which people at work, influence their colleagues, subordinates and even superiors to obtain personal benefits or to satisfy organizational goals. This perception influences employee attitudes and behavior and ultimately organizational performance.

2.3 e-HRM macro-level consequences

e-HRM macro-level consequences consist of all organizational outcomes that accompany and/or follow the application of information technology, whether helpful or harmful (Strohmeier, 2009; Galanaki et al., 2019). The helpful outcomes are the intended consequences whereas the harmful outcomes consist of unintended consequences. Macro-level consequences are differentiated into operational, relational and transformational.

2.4 e-HRM use and e-HRM macro-level consequences

e-HRM use enhances organizational performance by improving cost efficiencies and HRM processes (Marler and Fisher, 2013), empowering line managers and employees to perform HR activities (Galanaki et al., 2019) and supporting the business strategy (Bondarouk et al., 2019). When viewed as a way of performing HR administrative tasks, e-HRM use could lead to lower HR staff headcount as generic labor is substituted by information technology (Bondarouk et al., 2017).

Literature also shows that e-HRM supports a strategic orientation of the HR function (Bondarouk et al., 2019). As time is freed, HR professionals find time to embark on strategic activities such as strategic planning and talent management for competitive alignment of organizations. These activities help organizations move into new markets by providing managers with better information for effective decision making (Parry, 2011; Bondarouk et al., 2019). This study hypothesizes that in organizations employing e-HRM, organizational performance gains should be realized. Whilst this relationship has been found to exist in developed economies, it is assumed that this relationship also obtains in developing economies as well. The first hypothesis, therefore, is;

H1.

There exists a direct effect of e-HRM use on e-HRM macro-level consequences.

2.5 e-HRM and perception of organizational politics

The e-HRM system is a socio-technical sub-system (Strohmeier, 2009). As such there is need to align e-HRM system with culture, strategy, structure and power distribution for better prospects for success. The introduction of e-HRM systems is seen affecting the perception of organizational politics in a number of ways. First, e-HRM use is seen as redistributing power within organizational members: conferring more power on some and reducing the power of others. The beneficiaries of this power redistribution are likely to engage in positive organizational citizenship behavior, such as improved individual performance, job satisfaction and lower labor turnover (Opoku et al., 2020). Second, e-HRM use is seen as distributing non-randomly, the information required for coping with uncertainty, role ambiguity and role conflict (Opoku et al., 2020). By distributing information to e-HRM actors, the use of e-HRM therefore, allows organizational members to cope with uncertainty brought about by the work political environment. Third, the use of information technology enhances feedback as well as access to information regarding organizational policies and practices (Maslyn et al., 2017). When employees are afforded a chance to scrutinize organizational policies and practices, the perception of fairness and justice develops. Positive organizational politics develops, with its concomitant benefits of work attitudes and behaviors (Soares, 2018). Fourth, e-HRM promotes centralized structures, leading to a clarity of organizational rules (Maslyn et al., 2017). This again reduces role ambiguity and lowers the negative perception of organizational politics. Collectively, these effects result in e-HRM use reducing the negative perceptions of organizational politics in the workplace. The second hypothesis is therefore:

H2.

There exists a direct effect of e-HRM use on perception of organizational politics

Strohmeier and Kabst (2014) argue that the introduction of e-HRM systems alone will not automatically lead to intended organizational outcomes. There is need for intervening variables in order to enhance such an effect, such as employee performance, job satisfaction, strategic orientation of the HR function (Marler and Fisher, 2013). A number of studies have pointed to the positive effects of perception of organizational politics as an intervening variable between HR practice variables and employee outcomes. Such mediation enhances the effect of HR practices on wider organizational outcomes (Mazzola and Disselhorst, 2019; Opoku et al., 2020).

A number of organizational studies have categorized perception of organizational politics as a negative phenomenon, impacting negatively on a number of employee outcomes (Opoku et al., 2020). Other findings have however reflected on a positive relation between perceived organizational politics and employee outcomes (Mazzola and Disselhorst, 2019; Opoku et al., 2020). They have argued that perceived organizational politics is a very important part of organizational life in the context of employee and organizational performance. The phenomenon directly or indirectly, predicts employee commitment and behavior (Opoku et al., 2020), and, job satisfaction and employee performance (Mazzola and Disselhorst, 2019; Opoku et al., 2020). Given that organizations are political systems, “rather than focusing on rather futile attempts to eliminate political behavior, managers ought to focus instead on shaping it toward goals that are beneficial for the organization as well as the individual” (Maslyn et al., 2017, p. 1507). The third hypothesis is therefore:

H3.

There exists an indirect effect of e-HRM use on e-HRM macro-level consequences through organizational politics.

3. Methods

The study focused on organizations using e-HRM systems. A total of 26 organizations from 9 sectors of the economy made up the population of interest. The inclusion criteria for selecting participating organizations for the study were that;

1. The organization should have a minimum of 150 employees.

2. It should have implemented e-HRM applications for at least three years, at the time of determining the sample size.

The inclusion criteria were informed by the resource demands of e-HRM systems. Only big organizations are in positions to meet these demands (Parry, 2011). Three years were deemed long enough for e-HRM systems to be embedded in organizations. Individuals of interest were HR professionals, line managers and employees. A total of 20 organizations, met the inclusion criteria. With sectors making strata, a purposive stratified sampling technique was used to draw a study sample of 200 respondents (Table 1). Onwuegbuzie and Collins (2007) “minimum sample size recommendations for most common quantitative and qualitative research designs” were used to arrive at this sample size.

A structured questionnaire was piloted on 15 respondents. A drop off and pick up method was used to administer the questionnaire (Bryan, 2008). A total of 122 valid responses were received, representing a 61% response rate.

3.1 Measures

A questionnaire, covering e-HRM use, perception of organizational politics, e-HRM macro-level consequences and personal details, was used to collect data.

3.1.1 e-HRM use scale

The instrument designed to measure e-HRM use, had six items, incorporating five-point Likert scales with “strongly agree” and “strongly disagree” anchors. It was developed from validated research instruments used by Wahyudi and Park (2014), and Obeidat (2016). The instrument has two dimensions: system usefulness and perceived ease of use. The sample items include “The e-HRM system is clear and understandable” and “I have the necessary knowledge to use the e-HRM systems”.

3.1.2 Perception of organizational politics scale

A validated six item modified Kacmar and Carlson (1997) scale, with strongly agree and strongly disagree anchors was used to measure perception of organizational politics. This is a 2-dimension instrument: “supervisor behavior” and “pay and promotion policies”. The sample items include “It is best not to rock the boat in this organization” and “When it comes to pay increases/rise and promotion decisions, policies are irrelevant”.

3.1.3 e-HRM macro-level consequences scale

A nine-item e-HRM macro-level consequences scale was developed. The items were developed by Obeidat (2016) and Panos and Bellou (2016). It is divided into three dimensions: operational, relational and transformational consequences. The construct is treated as a dependent variable in this study. It incorporates five-point Likert scales with “strongly agree” and “strongly disagree” anchors. Sample items include “Employees are saving on time spent doing routine tasks as a result of using e-HRM”, “There is increased responsiveness to employee needs” and “e-HRM allows HR staff to redirect time to strategic initiatives”.

3.2 Assessing the measurement model

Although the scales have been reported in literature, a scale validation process was nonetheless carried out. The purpose was to eliminate poorly performing manifest variables for the three constructs. Once the exploratory factor analysis and confirmatory factor analysis were performed, the measurement models were assessed. The models were related to the following variables: e-HRM use, perceived organizational politics and e-HRM macro-level consequences. To validate the measurement models, the following tests were carried out:

1. Reliability test.

2. Manifest variable standardized path loadings.

3. Composite reliability test.

4. Discriminant validity test.

The Cronbach's alpha statistics for the three scales ranged from 0.7 to 0.9, exceeding the recommended value of 0.70 (Bryan, 2008). All the scales are internally consistent. The factor loading of all items exceeded the recommended value of 0.50 (Bryan, 2008). Composite reliability values which depict the degree to which the instrument measures the concept that it is intended to measure, ranged from 0.81 to 0.94, exceeding the recommended value of 0.70 (Bryan, 2008). The average variance extracted which reflects the overall amount of variance in the indicators accounted for by the latent construct, were in the range of 0.60–0.69 which exceeded the recommended value of 0.50 (Bryan, 2008).

Discriminant validity of the scales was also tested. The discriminant values (square roots of average variance extracted) range from 0.77 to 0.83. They are greater than the highest correlations with any other construct. In total, the measurement model demonstrated adequate validity and reliability, as shown in Table 2.

4. Results and discussion of findings

4.1 Factor analysis

The exploratory factor analysis (EFA), principal axis factoring with Promax rotation were conducted to examine the underlying pattern of e-HRM use, perception of organizational politics and e-HRM macro-level consequences variables. The EFA analysis of e-HRM use revealed two latent factors (perceived ease of use and system usefulness). They are meaningful as their eigenvalues are greater than 1 (>1) and they cumulatively explain 71.8% of the variance. Confirmatory factor analysis (CFA) was conducted to confirm the constructs obtained using EFA. The Joreskog and Sorbom's goodness of fit indices were used to evaluate the CFA. The results showed a good fit (CFI = 1.00; RMSEA = 0.041; SRMR = 0.026; GFI = 099; Χ²/df = 1.55; NFI = 0.99).

Concerning perception of organizational politics, two latent factors “Go Along to Get along” and “Pay and Policies” emanated from the EFA exercise. The two factors are meaningful as their eigenvalues are greater than 1 (>1). Factors 1 and 2 explain a cumulative total of 78.66%. CFA was conducted to confirm the constructs obtained using EFA. The model results showed a good fit (CFI = 1.00; RMSEA = 0.029; SRMR = 0.026; GFI = 099; Χ²/df = 1.27; NFI = 0.99).

EFA was also used to identify latent factors of e-HRM macro-level consequences. Three latent factors (operational consequences, transactional consequences and transformational consequences) emanated from an EFA analysis. The three factors are meaningful as their eigenvalues are greater than 1 (>1) and they cumulatively explain 70.31% of the variance. The model results showed a good fit (CFI = 1.00; RMSEA = 0.033; SRMR = 0.027; GFI = 098; Χ²/df = 1.35; NFI = 0.99).

4.2 Descriptive statistics

The mean scores, standard deviation and Pearson correlations for all the three variables were calculated (Table 3). e-HRM use has a positive and significant correlation with perception of organizational politics (r = 0.219, p < 0.05). Perception of organizational politics has a positive and significant correlation with e-HRM macro-level consequences (r = 0.341, p < 0.01). e-HRM use has a positive and significant correlation with e-HRM macro-level consequences (r = 0.467, p < 0.01).

4.3 Regression analysis

To test the hypothesis, regression analysis making use of process macro in SPSS, was done. The lower level and upper level of the regression coefficients were calculated based on 10,000 iterations in a bootstrapping model and 95% level of confidence. The regression outputs were used to test total, direct and indirect effects models (Hayes, 2018).

4.3.1 Hypothesis 1: There exists a direct effect of e-HRM use on e-HRM macro-level consequences

e-HRM use has a positive and statistically significant effect on e-HRM macro-level consequences (β = 0.3566, p < 0.001). Zero falls outside the 95% confidence interval (0.2197–0.4935). e-HRM also has positive and statistically significant effect on the constituent elements of e-HRM macro-level consequences; the operational consequences (β = 0.349, p < 0.001), relational consequences (β = 0.414, p < 0.001) and transformational consequences (β = 0.400, p < 0.001) (Table 4). Hypothesis 1 is accepted.

This result is validated by a number of studies (Parry, 2011; Bondarouk and Ruel, 2013; Wahyudi and Park, 2014; Obeidat, 2016; Bondarouk, 2020). The use of e-HRM enhances the attainment e-HRM macro-level consequences. Investing in e-HRM systems is defensible on the basis of organizational gains such as increased efficiency, effectiveness and better talent management (Bondarouk, 2020). Obeidat (2016), however, argues that the introduction of e-HRM systems alone, will not automatically lead to intended organizational outcomes. There is need for intervening variables in order to enhance the effect, such as employee performance, job satisfaction and the strategic orientation of the HR function (Marler and Fisher, 2013).

4.3.2 Hypothesis 2: There exists a direct effect of e-HRM use on perception of organizational politics

The effect of e-HRM use on perception of organizational politics is positive and statistically significant (β = 0.2531, p < 0.001). Zero falls outside the 95% confidence interval (0.1427–0.3635) (Table 4). e-HRM use is a significant predictor of perceived organizational politics. The second hypothesis is accepted. This result is validated by a number of studies (Opoku et al., 2020). The use of e-HRM translates negative perceptions of organizational politics into constructive politics. It is when such a change occurs, that perception of organizational politics plays an intervening variable role.

4.3.3 Hypothesis 3: There exists an indirect effect of e-HRM use on e-HRM macro-level consequences through organizational politics

The coefficient of e-HRM use on perception of organizational politics is positive and significant (β = 0.2138, p < 0.05). Zero falls outside the 95% confidence interval (0.0416–0.3860). The use of e-HRM is a significant predictor of perception of organizational politics. The coefficient of the mediator variable on e-HRM macro-level consequences is also positive and significant (β = 0.1793, p < 0.001). Zero falls outside the 95% confidence interval (0.0941–0.2646). Perception of organizational politics mediates the relationship between e-HRM use and e-HRM macro-level consequences.

The total effect of e-HRM use on e-HRM macro-level consequences is positive and significant (β = 0.5254, p < 0.001). The direct effect of e-HRM use on e-HRM macro-level consequences is positive and significant (β = 0.4801, p < 0.001. The indirect effect of e-HRM use on e-HRM macro-level consequences (Table 5) is positive and statistically significant (β = 0.0454), with a 95% bootstrap confidence of 0.0195 (lower limit) and 0.0787 (upper limit). Zero lies outside the interval, indicating that perception of organizational politics plays an intervening role. This model is significant and indicates a good overall model quality, explaining 0.2776 (27.76%) of the variance in e-HRM macro-level consequences. The third hypothesis is accepted.

These findings are validated by a number of studies (Meisler and Vigoda-Gadot, 2013; Eldor, 2017; Mazzola and Disselhorst, 2019) which have used perception of organizational politics as a mediating variable. A new addition to the current knowledge is the positive and significant role of “perception of organizational politics” as a mediating variable. Although there is no direct support of this finding from literature in terms of these two specific variables, a few sources allude to the mediating role of organizational politics in enhancing e-HRM macro-level consequences (Eldor, 2017; Mazzola and Disselhorst, 2019). When employees receive emotional intelligence training, they are able to cope with and survive in negative political situations. The political behavior is a reality within organizations, and it can be detrimental and beneficial at times. Effective support from management is needed in order to turn the perceptions, positive.

4.4 Implications for theory and practice

The study contributes to theory and practice in a number of ways. First, the findings broaden the scope through which e-HRM use and perceived organizational politics are viewed. Information technology use partly shapes the way employees perceive organizational politics. Little research has explored the positive effect of positive perception of organizational politics (Opoku et al., 2020). Second, the findings show that e-HRM use directly predicts e-HRM macro-level consequences. It indirectly affects organizational efficiency, effectiveness and strategic orientation through the mediation role of perception of organizational politics. Third, the study offers insights into the effect of perceived organizational politics in influencing employee attitudes and behavior and ultimately organizational outcomes. The current study is one of a few to explore the mediating role of perceived organizational politics between e-HRM use and e-HRM macro-level consequences. Lastly, the developed world has dominated research on the positive indirect effects of e-HRM use on organizational outcomes. The findings of this study confirm that such effects are present in the developing countries as well.

The study also has practical implications for management. The study provides further evidence of the inadequacy of the technology imperative in explaining successful implementation of information technology. Management should identify intervening variables needed for effective e-HRM deployment. There is also need for management to work on changing the negative perception of organizational politics through emotional intelligence training for enhanced e-HRM effects. Information technology is an empowering tool for the HR function, helping change attitudes and behaviors. Perceived organizational politics, for long, regarded as the nemesis to effective management of organizations, is capable of adding value (Meisler and Vigoda-Gadot, 2013; Maslyn et al., 2017). Managers ought to deploy HR practices that positively shape perceived organizational politics (Maslyn et al., 2017).

5. Limitations

The study was cross sectional in nature. It is possible that outcomes of some variables had not yet manifested. A longitudinal survey is therefore more appropriate. Furthermore, the data were collected from the same respondents who made up the sample, giving rise to single source bias. This limitation can be ameliorated by the use of mixed methods research. Future studies could expand the model to include other variables, such as emotional intelligence.

6. Conclusion

The main objective of this study was to investigate the mediating role of perceived organizational politics in the relationship between e-HRM use and e-HRM macro-level consequences. The research proposed an integrated model wherein perceived organizational politics mediated the relationship. This model enhances the chances of e-HRM use resulting in enhanced e-HRM macro-level consequences. e-HRM use and perception of organizational politics have the capacity to engineer intended e-HRM operational, relational and transformational consequences.

Figures

Figure 1

Research model

Table 1

Distribution of respondents

Sectors	Beverages	Retail	Mining	Banking	Agro-industrial	Food	Insurance	Building	Education	Total
Organizations	3	2	3	2	2	2	2	2	2	20
HR Professionals	25	12	23	15	14	12	11	14	15	141
IT Professionals	5	2	7	5	3	2	2	3	2	31
Line Managers	17	10	16	12	10	10	7	10	11	103
Population (N)	47	24	46	32	27	24	18	27	28	275
Sample
HR Professionals	17	11	17	13	12	11	10	10	12	113
IT Professionals	3	2	4	3	2	2	2	2	2	22
Line Managers	6	7	10	9	7	7	5	7	7	65
Sample Size (n = 200)	26	20	31	25	21	20	17	19	21	200

Table 2

Scales' internal consistencies

Construct	AVE ≥ 0.50	CR ≥ 0.70	α ≥ 0.7	DV	R	Loadings>0.50
e-HRM use	0.60	0.81	0.80	0.77	0.50
Minimum						0.53
Maximum						0.93
Perception of Organizational politics	0.60	0.94	0.70	0.77	0.09
Minimum						0.73
Maximum						0.97
e-HRM macro-level consequences	0.69	090	0.9	0.83	0.50
Minimum						0.70
Maximum						0.96

Note(s): DV: Discriminant Value (square root of AVE); AVE: Average Variance Extracted; CR: Composite Reliability; R: Correlation among latent variables/constructs

Table 3

Means, standard deviations and correlations for variables under study

	Variable	Mean	SD	1	2	3	4	5
1	Age (years)	45	0.929
2	Experience (tenure–years)	13	1.212	0.814**
3	Position			−0.244**	−0.165
4	Organizational politics	4.12	0.464	0.088	0.000	0.000
5	e-HRM macro-level consequences	4.32	0.411	0.152	0.030	−0.041	0.341**
6	e-HRM use	4.46	0.475	0.076	0.001	−0.051**	0.219*	0.467**

Note(s): **Correlation is significant at the 0.01 level (2-tailed)

*Correlation is significant at the 0.05 level (2-tailed)

n = 122

Table 4

Variable effect of e-HRM use on e-HRM macro-level consequences

Variable effect	β	SE	t	p	95% confidence interval
					LCCI	ULCI
e-HRM → e-HRM macro	0.4801	0.0451	10.6503	p < 0.001	0.3914	0.5687
e-HRM → Operational consequences	0.349	0.060	5.833	p < 0.001	0.231	0.466
e-HRM → Relational consequences	0.414	0.064	6.435	p < 0.001	0.287	0.540
e-HRM → Transformational consequences	0.400	0.074	6.039	p < 0.001	0.270	0.530
e-HRM → POPs	0.2531	0.0561	4.5097	p < 0.001	0.1427	0.3635
e-HRM → POPs → e-HRM macro	0.1793	0.0434	4.1371	p < 0.001	0.0941	0.2646

Note(s): POPs: Perception of organizational politics

e-HRM macro: e-HRM macro-level consequences

Based on 10,000 bootstrap samples

Table 5

Total, direct, and indirect effects of e-HRM use on e-HRM macro-level consequences

	β	SE	t	p	LCCI	ULCI
Total Effect of X on Y	0.5254	0.0448	11.7296	0.0000	0.4373	0.6136
Direct effect of X on Y	0.4801	0.0451	10.6503	0.0000	0.3914	0.5687

Indirect effect of X on Y	β	SE	BootLCCI	Boot-ULCI
Perception of organizational politics	0.0454	0.0153	0.0195	0.0787