ORGANIZATIONAL FACTORS AND HUMAN RESOURCE INFORMATION SYSTEM IN MANUFACTURING FIRMS IN NAIROBI COUNTY, KENYA

CHARITY CHEPCHIRCHIR

MBM/023/13

A RESEARCH THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTERS IN BUSINESS MANAGEMENT, SCHOOL OF BUSINESS AND ECONOMICS, DEPARTMENT OF MANAGEMENT SCIENCE

MOI UNIVERSITY

SEPTEMBER, 2018

DECLARATION

Declaration by the candidate

This Thesis is my original work and has not been presented for a degree in any other university. No part of this thesis may be reproduced without the prior written permission of the author and/or Moi University.

Charity Chepchirchir …………………………	Date…………………..

Declaration by supervisors

This research Thesis has been submitted for examination with our approval as university supervisors.

Prof Michael Korir ………………………	Date…………………

Management Science Department

School of Business and Economics, Moi University

Eldoret, Kenya

Dr. Joel Chepkwony …………………………	Date…………………

Marketing and Logistics Department

School of Business and Economics, Moi University

Eldoret, Kenya

DEDICATION

This thesis is dedicated to my family and friends.

ACKNOWLEDGEMENT

I take this opportunity to express my profound gratitude to Moi University and deep regards to my University supervisors; Prof Michael Korir and Dr. Joel Chepkwony for their exemplary guidance, monitoring and constant encouragement throughout the course of this Thesis.

I also thank almighty God, My guardian, brothers and friends for their constant encouragement without which this assignment would not be possible.

ABSTRACT

Information Technology (IT) is a critical resource for today’s businesses. Due to increased emphasis on a knowledge-based economy, many organizations are realizing that their employees and information resources are critical to survival and success. Therefore, human resource information systems (HRIS) is increasingly becoming important in ensuring and aiding organizations manage their human capital effectively. Despite this realization, Kenyan firms still lag behind in adopting HRIS. The study was guided by technology-organization-environment (TOE) framework and diffusion innovation model (DOI). The study sought to establish organizational factors affecting the use of human resource information systems (HRIS) in the manufacturing sector in Kenya, and it was guided by the following specific objectives: To determine the effect of top management support on application of HRIS, To assess the effect of centralization on HRIS usage, To determine effect of organization culture on application of HRIS and to determine the effect of competitive Pressure on application of HRIS among manufacturing firms in Nairobi County, Kenya. Stratified sampling was used to obtain a sample size of 212 respondents from a target population of 478 manufacturing firms listed in the Kenya manufacturing association list (2014). Descriptive and inferential statistical analyses were used to analyze the quantitative data. Pearson’s correlation coefficient was used to determine the relationship between the independent variables and the dependent variables, while regression analysis was used to predict the relationship. Findings showed that top management support, (β1 = 0.15, p< 0.05), degree of centralization (β2= 0.22, p< 0.05), organization culture (β3 = 0.23, p< 0.05) and competitive pressure (β4 = 0.40, p< 0.05) had a positive and significant effect on the application of HRIS. The study concludes that competitive pressure, degree of centralization, organization culture and top management support are key drivers for application of HRIS. There is therefore need for organizations to have job specialization, formalization and standardization in the organization. Also, the organizations need to have an open and flexible corporate culture, supportive climate and flexible structures that enhance successful deployment of new technologies. Organizations should do what is right in terms of technology adoption despite of competition facing the organization.

TABLE OF CONTENTS

DECLARATION	i

DEDICATION	ii

ACKNOWLEDGEMENT	iii

ABSTRACT	iv

TABLE OF CONTENTS	v

LIST OF TABLES	viii

LIST OF FIGURES	viii

DEFINITION OF TERMS	x

LIST OF ABBREVIATIONS	xii

CHAPTER ONE	1

INTRODUCTION	1

1.1	Overview	1

1.2	Background of the Study	1

1.2.1	Manufacturing Sector in Kenya	4

1.2	Statement of the Problem	6

1.3	Objective of the Study	7

1.3.1	General Objective	7

1.3.2	Specific Objectives	8

1.4	Research Hypothesis	8

1.5	Justification of the Study	9

1.6	Scope of the Study	10

CHAPTER TWO	11

LITERATURE REVIEW	11

2.1	Introduction	11

2.2	Concept of Technology Usage	11

2.3	The Concept of Human Resource Information System (HRIS)	15

2.3.1	Components of HRIS	20

2.3.2	Users and Uses of HRIS	21

2.4	Theories on Information Technology Usage	24

2.4.1	The Diffusion of Innovation Theory	24

2.4.2	The Technology-Organization-Environment Theory	25

2.4	Summary of Theories and Research Gap	26

2.4	Organizational Determinants of Application of HRIS	27

2.4.1	Top Management Support and Commitment	28

2.4.2	Degree of Centralization	31

2.4.3	Organizational Culture	33

2.4.4	Competitive Pressure	34

2.5	Conceptual Framework	35

CHAPTER THREE	37

RESEARCH DESIGN AND METHODOLOGY	37

3.1	Introduction	37

3.2	Research Design	37

3.3	Study Location	38

3.4	Target Population	39

3.4.1	Sample Size and Sampling Procedure	41

3.5	Data Collection, Instruments and Procedure	42

3.5.1	Type of Data	42

3.5.2	Research Instruments	42

3.5.3	Data collection procedure	42

3.6	Reliability of Research Instruments	42

3.7	Validity of Research Instruments	43

3.8	Measurement of Variables.	43

3.9	Data Analysis and Presentation	44

3.9.1	Model Specification	46

3.10	Limitations of the Study	47

3.11	Ethical Considerations	47

CHAPTER FOUR	48

DATA ANALYSIS, PRESENTATION AND INTERPRETATION	48

4.1 Introduction	48

4.2 Response Rate	48

4.3	Data Screening	49

4.4	Sample Characteristics	49

4.5	Descriptive statistics	51

4.5.1	Application of HRIS	51

4.5.2	Top management support	52

4.5.3	Degree of Centralization	54

4.5.4	Organization Culture	55

4.5.5	Competitive Pressure	57

4.6	Reliability Analysis	58

4.7	Factor analysis for independent variables	59

4.7.1	KMO	59

4.7.2	Total Variance Explained	60

4.7.3	Factor Analysis for Independent Variables	60

4.8	Data Transformation and Test of Normality	61

4.9 Correlation Results	63

4.10 Multiple Regression Results	64

4.10.1	Model Summary	64

4.10.2	ANOVA Model	65

4.10.3	Hypothesis Testing	66

CHAPTER FIVE	69

SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS	69

5.1	Introduction	69

5.2	Summary of the Findings	69

5.3	Discussion and Conclusion	70

5.3.1	Top Management Support and HRIS	70

5.3.2	Degree of centralization and HRIS	71

5.3.3	Organizational Culture and HRIS	73

5.3.4	Competitive Pressure and HRIS	73

5.4	Recommendations	74

5.5	Further Research Recommendations	75

REFERENCES	77

APPENDIX I:	LETTER OF INTRODUCTION	89

APPENDIX II: QUESTIONNAIRE	90

APPENDIX V: RECOMMENDED SAMPLE SIZE FROM A GIVEN POPULATION	94

LIST OF TABLES

Table 3. 1 KAM Members Categorization	40

Table 3. 2 Data Analysis Summary	45

Table 4. 1 Response Rate	48

Table 4. 2 Sample Characteristics	50

Table 4. 3 Application of HRIS	52

Table 4. 4 Top management support	53

Table 4. 5 Degree of centralization	54

Table 4. 6 Organization culture	56

Table 4. 7 Competitive pressure	58

Table 4. 8 Reliability analysis	59

Table 4. 9 KMO	59

Table 4. 10 Total Variance Explained	60

Table 4. 11 Factor Analysis for Independent Variables	61

Table 4. 12 Descriptive Statistics Mean Grades	62

Table 4. 13 Test of Normality	62

Table 4. 14 Correlation Results	64

Table 4. 15 Model Summary	65

Table 4. 16 ANOVA Model	65

Table 4. 17 Coefficients of Estimate	68

Table 4. 18 Summary of findings	68

LIST OF FIGURES

Figure 2. 1 Conceptual Framework depicting Organizational factors affecting application of HRIS.	36

DEFINITION OF TERMS

Competitive Pressure - According to Zucchini and Kretschmer (2011), competitive pressure can arise from a firm's awareness of and motivation to respond to a buildup of actions by one or more rivals. Second, firms will seek to relieve competitive pressure by making competitive moves. Third, these moves may be directed towards several rivals at once. This argument is consistent with the finding by Hsieh and Chen (2015) that an increasing number of rival actions increases the likelihood that a firm will make a competitive move to protect its competitive position, and with Reger and Huff (1993), who show that managers' actions are influenced by perceived rivalry.

Degree of Centralization-This refers to the extent to which power and control are concentrated in the hands of few individuals (Rogers, 1995). It also refers to the degree of delegation of authority in an organization, that is, the less authority is delegated in an organization the greater its centralization (Belassi & Fadlalla, 1998).

Human Resource Information System - is considered as a systematic procedure for collecting, storing, maintaining, and recovering data required by an organization about their human resources, personnel activities and organizational characteristics, (Kovach et al., 2002).

Organizational Culture -is associated with the organization’s sense of identity, its goal or core values, its primary ways of working and a set of shared assumptions (Schein, 1996). Organizational culture does not change easily (Leidner & Kayworth, 2006).

Top Management Support- Management participation refers to managers and leaders’ attitude and involvement in designing and developing HRIS with the help of IT professionals and ensuring that HRIS is accepted in an organization. It involves the managements role in ensuring that HRIS is accepted despite the resistance to change that may arise. (Paulk et al., 1995; Project Management Institute – PMI, 2003).

Application of HRIS	- is the act of deploying HRIS as a means of accomplishing or achieving something.

LIST OF ABBREVIATIONS

DOI –	Diffusion of Innovation

HR-	Human Resource

HRIS –	Human Resource Information System

HRM -	Human Resource Management

ICT-	Information Communication Technology

IT-	Information Technology

PM-	Personnel Management

SPSS-	Statistical Package for Social Sciences

TOE -	Technology, Organization Environment

CHAPTER ONE

INTRODUCTION

1.1	Overview

This chapter gives an overview of the background of the study, statement of the research problem, justification, research objectives, research hypothesis, the scope and significance of the study.

1.2	Background of the Study

The world of business has been stirred by advent of information technology, information systems and internet technology (Xu, Wang, Luo, & Shi, 2006). Information technology has provided an opportunity to transform organizations and help them achieve competitive advantages (Appah & Emeh, 2012). In current knowledge economy, organizational success is dependent on efficiency of human resources (HR) (Lippert & Swiercz, 2005).

It is opined that IT should play a vital role in Human Resource Management (HRM) domain (Adedoyin, 2010). It is further stated that a successful way to run business in today’s dynamic business environment is by using appropriate application of Information Technology (IT) in HRM (Zhang & Wang, 2006). The use of Human Resource Information Systems (HRIS) has been advocated as an opportunity for human resource (HR) professionals to become strategic partners with top management and to improve their management skills (Lengnick-Hall & Moritz, 2003). The idea has been that HRIS would allow for the HR function to become more efficient and to provide better information for decision making. The question remains whether HRIS has fulfilled its promise. In their view, the adoption of HR as promoted by HRIS evolves from information to automation and from automation to transformation. They note that while HRIS has been widely deployed, a transformation of human resource management has occurred in relatively few organizations. The evolution that Lengnick-Hall and Moritz (2003) propose, along with others (Walker, 2001), revolves around the perspective that HRIS will create informational efficiencies and cost savings such that HR departments can turn their attention to providing better analysis of current data and creative uses of the HRIS to provide better and more accurate data upon which to base strategic decisions.

HRIS is thought to contribute to overall business service delivery by fulfilling or at least supporting the tasks of data storage and retrieval, of serving as primary administrative support tools, of reporting and statistics as well as of program monitoring (Ostermann, Staudinger, & Staudinger, 2009). HRIS plays an important role for any organization to effectively manage its human assets. Many organizations have adopted HRIS to assist their daily human resources operations. HRIS must align and satisfy the needs of the organization and its users in order to be successful (Noor & Razali, 2011).

Human Resource Information Systems is built up on the foundation of human resources and information technology (Hussain et al., 2011). It is the backbone of HRM (Hendrickson, 2003). Technology cannot work alone, people and policies must be given due importance in order to get the results as per spirit of HRIS. Effective utilization of technology in combination with policies and people would give competitive edge to the organization (Hendrickson, 2003). Extant research has examined the usage of HRIS in public sector (Troshani, Jerram, & Gerrard, 2010), benefits and barriers to HRIS implementation. While this is encouraging, HRIS usage and utilization remains under investigated in Kenya.

Information technology has drastically changed the human resource function. Providing support for mainly administrative activities such as, payroll and attendance management in the beginning, information technology today enhances many of the recruitment function’s sub processes such as long and short-term candidate attraction, the generation, pre-screening, and processing of applications or the contracting and on boarding of new hires. Online job advertisements on corporate web sites and internet job boards, online CV databases, different forms of electronic applications, applicant management systems, corporate skill databases, and IS supported workflows for the contracting phase are only few examples of the various ways by which information systems today support recruitment processes (Keim & Weitzel, 2009).

The application of HRIS has been advocated as an opportunity for human resource professionals to become strategic partners with top management. HRIS allows HR function to become more efficient and to provide better information for decision making (Beadles, Lowery, & Johns, 2005).	However, with their widespread application over many years, the implementation of Human Resource Information Systems continues to be faced with challenges. According to Ball (2001), businesses cannot afford the write offs associated with Information Technology project failures not to mention secondary effects such as lost opportunities, reputations and staff morale.

According to Skitmore and Sharma (2001), the main factors that cause failure of Information Technology Systems are mainly human as opposed to materialistic or malfunction of the actual systems. Ngai and Wat (2006) in a study on Human Resource Information System implementation found out that many organizations have problems when implementing new technologies including Human Resource Information System due to many barriers. These barriers include; lack of sufficient capital and skills, cost of setting up and maintaining the system, lack of top management support and commitment, lack of human resource knowledge by system designers and lack of applications for human resource users. According to Troshani, Jerram, & Rao (2011), Human resources information systems (HRIS) are becoming increasingly important in helping modern organizations manage their human assets effectively. Yet, HRIS adoption remains an under‐researched phenomenon. Aynul (2018) in his study on manufacturing firms in Sri-lanka noted that Adoption of HRIS helps to achieve competitive advantage in the industry. HRIS is used extensively in organizations of all sizes and it is adopted and implemented in organizations generally.

1.2.1	Manufacturing Sector in Kenya

The manufacturing sector in Kenya dates back to the end of World War II (GoK, 2007). The sectors gross domestic product is estimated to have expanded by 4.5 per cent during the review quarter of 2014 compared to a revised growth of 6.2 per cent in the same period of 2013. The growth was mainly supported by significant expansions in assembly of motor vehicles, production of soft drinks, processing of maize meal and production of cement which expanded by 41.3, 10.8, 10.3 and 7.8 per cent, respectively (Kenya National Bureau of Statistics,2014). The sector is expected to play a critical role in propelling the economy to a 10 per cent growth rate, in line with the aspirations of vision 2030 and in supporting the country’s social development agenda through the creation of jobs, attracting foreign direct investment, export of goods and services. Manufacturing is identified in Vision 2030 as key to addressing incidences of high poverty levels and unemployment all this can be achieved through human capital.

To meet these goals, the sector has to become more efficiency-driven and enhance a competitive advantage. Human resource information system (HRIS) allows HR function to become more efficient and to provide better information for decision making (Beadles, Lowery, & Johns, 2005), raising productivity per unit of input closer to those of external competitors. One of the strategies that, Kenya government should put in place in order to build knowledge, technology and innovation through training and research and development.

To capitalize on the synergy between these two assets, human resource information systems is an emerging area that may lead human resource management into a new era. Yet perhaps human resource information systems is an important function of the manufacturing sector that may foster creativity and competitiveness of the sector, which will give a single clear voice that will truly represents transformation in the Manufacturing sector in Kenyan through effective and efficient human capital.

1.2	Statement of the Problem

With the Manufacturing Sector in Kenya having been identified in Vision 2030 as key to addressing incidences of high poverty levels and unemployment which can be achieved through human capital, the sector needs to adopt Information Systems (IS) in business functions that help ensure cost management effectively and specifically in labour-intensive factories across the manufacturing sector in Kenya.

In the past three decades a substantial amount of research has been undertaken in an attempt to study and isolate the main factors that determine the adoption of information technology (IT) innovation behavior at the level of the individual firm. Several researchers have developed conceptual models to verify the adoption of IT behavior aspects (Xu, Wang, Luo, & Shi, 2006). However, the majority of these studies have failed to give a complete account of the factors underlying the adoption and Use of IT innovation behavior, in general and the Use of Human Resource Information Systems (HRIS) in particular as is evident with majority of the Kenyan firms are reluctant to adopting HRIS. (Kinyua, 2012)in a study on the challenges facing state corporations in Kenya in the implementation of HRIS noted that the adoption and use of information communication technology remains a major challenge in the implementation of human resource management information systems in Kenyan state corporations.

Furthermore, in light of the few of empirical studies available to assist in the identification of the most significant factors for HRIS use in organization, all relevant factors have been identified and grouped into the broad categories of internal and external environment factors. This distinction was made in order to distinguish between factors determined by the organization and factors imposed from outside the organization, in the deployment process of HRIS. According to organizational behavioral theories an organization’s behavior is inseparable from the environment in which it operates. The organization's usage of HRIS is determined not only by its internal environmental characteristics such as Organizational Culture, Top Management Support, User Training, degree of Centralization, but also through forces present in the external environment including; Legal Environment, Competitive Pressure, Economic Factors and Political Factors.

There is inadequate research concerning HRIS adoption in the manufacturing sector and particularly Kenya, which presents unique challenges. The study therefore sought to establish organizational factors affecting the application of HRIS. This thesis contributes to the existing body knowledge by investigating the role of technological, organizational, and environmental factors and their interactions in the application of HRIS. It provides an improved understanding of the challenges related to HRIS adoption in Manufacturing Firms.

1.3	Objective of the Study

1.3.1	General Objective

The general objective of the study was to examine Organizational factors and Human Resource Information System (HRIS) among manufacturing firms in Nairobi County, Kenya.

1.3.2	Specific Objectives

The study sought specifically;

To determine the effect of top management support on application of HRIS among manufacturing firms in Nairobi County, Kenya.

To assess the effect of degree of centralization on application of HRIS among manufacturing firms in Nairobi County, Kenya.

To determine the effect of organization culture on application of HRIS among manufacturing firms in Nairobi County, Kenya

To investigate the effect of competitive pressure on application of HRIS among manufacturing firms in Nairobi County, Kenya.

1.4	Research Hypothesis

H01	Top management support has no significant effect on application of HRIS in manufacturing firms in Nairobi County, Kenya

H02	Degree of centralization has no significant effect on application of HRIS in manufacturing firms in Nairobi County, Kenya

H03	Organization culture has no significant effect on application of HRIS in manufacturing firms in Nairobi County, Kenya

H04	Competitive pressure has no significant effect on application of HRIS in manufacturing firms in Nairobi County, Kenya

1.5	Justification of the Study

The world of business has been stirred by advent of information technology, information systems and internet technology (Xu, Wang, Luo, & Shi, 2006). Researchers have highlighted the fact that Information technology has provided opportunities to transform organizations and help them achieve competitive advantages. In current knowledge economy, organizational success is dependent on efficiency of human resources (HR) (Lippert & Swiercz, 2005). It is opined that IT should play a vital role in Human Resource Management (HRM) domain. Similarly, it is shared that businesses gain competitive advantages not only with Information Technology (IT), but with usage of IT to complement resources (Powell & Dent-Micallef, 1997). It is further stated that a successful way to run business in today’s world is using appropriate application of Information Technology (IT) in HRM (Zhang & Wang, 2006).

While a lot of attention has been directed towards the adoption of human resource information system in both public and private organizations in many parts of the world, little has been done to establish the use and determinants of usage of such technologies in Kenyan organizations. The study sought to determine the effect of organizational factors on Application of HRIS among manufacturing firms in Nairobi County, Kenya. The findings of the study will be useful to both private and public organizations since it can be used in strengthening the acquisition, implementation and integration of HRIS in administration so as to ensure effectiveness and efficiency. The study also forms a basis on which other academic researchers can do further studies.

1.6	Scope of the Study

The study was basically concerned with investigating the effect of organizational factors on the use of human resource information system (HRIS) among manufacturing firms in Nairobi County, Kenya. Although there are several factors influencing application of HRIS in organizations, the study was basically concerned with the internal specific factors; Top Management Support, Degree of centralization, Organization culture and external factor Competitive Pressure. The study involved the human resource managers.

CHAPTER TWO

LITERATURE REVIEW

2.1	Introduction

This chapter presents relevant literature review gathered in relation to the subject under study by various researchers, scholars, analysts and authors. It covers areas on human resource information system, its contents, its benefits and factors affecting its use and implementation.

2.2	Concept of Technology Usage

Organizations Today live in the information age where Technology and more so information technology (IT) has been one of the major causes of changes in the world. According to Scott (2011), the information technology industry is the largest and fastest–growing industry in the world. Prior to World War II, HR professionals performed basic employee record keeping as a service function with limited interaction on core business mission. Initial efforts to manage information about personnel were frequently limited to employee names and addresses, and perhaps some employment history often scribbled on 3x5 note cards (Nwakanma et al., 2014). Information Communication Technologies (ICTs) have assumed a central position in the development agenda of most organizations due to their critical role in facilitating business operations. Throughout the world, there has been a paradigm shift where organizations have realized the importance of information and communication technology (ICT) as a strong strategic tool for improving their performance (Walker, 2010).

Wenger (2010), argues that the contribution of new technology to economic growth can only be realized when and if the new technology is widely diffused and used. Diffusion itself results from a series of individual decisions to begin using the new technology, decisions which are often the result of a comparison of the uncertain benefits of the new invention with the uncertain costs of adopting it.

Organizations have become more aware of human capital and are developing formal processes for selection and development of employees. At the same time, organizations have begun to recognize the importance of employees’ morale on the firm’s overall effectiveness and the Human Resource Department has become one of the most important users of the costly computing systems of the day, often edging other functional areas for computer access. Although HRIS systems were computerized and grew extensively in size and scope during this period, they remained (for the most part) simple record-keeping systems (Wolff, 2010). Johnson and Diman (2017) recently highlighted the term of Human Resource Information Systems (HRIS) as a critical success factor for any larger organizations contributed with streamlining HR operations and improving productivity, efficiency and performance.

Scott and Bohlandern (2007), noted that information technology has changed the face of human resource management (HRM) in organizations. With the most obvious impact being operational, that is, automating routine activities, alleviating administrative burdens, reducing costs, and improving productivity internal to the HR function itself. According to Lucas (2005), information technology (IT) has contributed in providing new ways of designing organizations and new organizational structures which have presented; new opportunities for electronic commerce, contributed to the productivity and flexibility of knowledge workers.

Jorgenson and Kick (2003), also noted that there is well-developed macroeconomics evidence that information technology investments are likely to have “paid-off” with higher levels of productivity growth in industries that invested more heavily in IT. Despite this, Scholars have detected trends towards Information Technology use in Human Resource function and argue that though investments in IT are in top gear, information tools applied to employees pale in comparison with those used in other functional areas like Marketing and Finance (Emeka, 2013).

The trend of lagging behind was noticed in the 1990’s but it has also been noticed as recent as beyond 2000. According to Grobler and Warnich (2006) technology has recently come into HRM in a major way. Despite, over almost four decades experience of using and investing in information systems, it might appear that organizations have made little progress with project failure rates doggedly sticking to around 70 % (Ward & Daniel, 2006).

Ngai and Wat (2006) carried out a survey of the implementation of HRIS in Hong Kong organizations and they found the greatest benefits to be the quick response and access to information that it brought, while the greatest barrier was the insufficient financial support from the top management. Diez and Mclntosh (2009), in their study found that users characteristics, user participation and perceptions, intentions, user computer experience, top management support, training, external pressure, and the availability of external information sources have significant effect on successful use of Information Technology and on its’ widely acceptance.

Grant et al. (2006) in studying the impact of human resource information system (HRIS) in organizations established that the potential of human resource information system to deliver the strategic competencies promise remains unrealized. Specifically, Grant et al. (2006) suggested that the opportunity of enhancing human resource’s role as strategic partner as a result of the use of the system was hindered by three main challenges: first, was the ability to maintain the levels of senior management commitment and resources needed to implement and manage new or upgraded HRIS, second concern was managing the complexity of the system and its associated functionality and the third challenge arose from barriers associated with the acceptance of the system among key managers and employees along with the importance attached to managing the change processes associated with the implementation and introduction of the new or upgraded systems.

Several studies have also been done on human resource information system locally, For instance, Atika (2011) carried out a study on factors influencing the effectiveness of human resource information system at the National Cereals and Produce Board, Kenya in which the study concluded the following; user satisfaction, attitudes, beliefs, cultural, and behavioral issues as key areas that influence successful implementation of information systems. Kinyua, (2012)in a study on the challenges facing state corporations in Kenya in the implementation of HRIS noted that the adoption and use of information communication technology remains a major challenge in the implementation of human resource management information systems in Kenyan state corporations.

Therefore, worth noting that despite the fact that most of these studies focused on the status of human resource information system and its uses and implementation, little research has been conducted to examine the similar or different characteristics in developing countries since the notion of human resource information system is still new and just in its initial years of implementation. Consequently, the success of this trend towards IT adoption in HRM practices can only be guaranteed if there is proper understanding of the critical factors in use of Information Technology.

Aynul (2018) found that internal environmental factors such as socio technology skills of CEO, top management support, centralization and HRIS experience of employees have a positive and significant effect on the level of adoption of HRIS on the adoption of HRIS.

2.3	The Concept of Human Resource Information System (HRIS)

Kovach and Cathcart (as cited in Benfatto, 2010, p.7) was the first to define HRIS as “a system for ‘collecting, storing, maintaining, retrieving and validating data needed by an organization about its human resources’”. However, The HRIS definition has had an expanded meaning from time to time in accordance with the technology advancement in today’s digital era. In this study, HRIS is defined as “a system that is used to ‘acquire store, manipulate, analyze, retrieve, and distribute information about an organization’s human resources’” (Tannenbaum, as cited in Benfatto, 2010, p. 7). Tyagi, (2017) defines HRIS as ‘a system that enables storing of information of Human Resource in every aspect such as Personal, Academic, Qualification, Family, Medical, Career and Performance Evaluation, Training & Development & Wage and Salary of individuals’. He also highlights that unlike manual systems the HRIS enables availability of all such information in a single screen. Various authors have noted some benefits of Human Resource Information; For example, (Ngai & Wat, 2006) opined that HRIS offers improvement of accuracy in operating, controlling, and planning activities in human resources. It also allows users to access the information faster and on time, and it saves cost.

According to Ankrah and Sokro, (2012) “The application of HRIS comes with the following benefits; Increases competitiveness by improving HR practices, Produces a greater number and variety of HR operations; Shifts the focus of HR from the processing of transactions to strategic HRM, Makes employees part of HRIS, and Reengineers the entire HR function” (p. 9). The effect of globalization and technology growth today makes the organizations start to use the information system for their HR functions in their departments. Another successful perception is the usefulness of the system that can benefit the organization and can satisfy their employees, staff, and all the users (Bal, Bozkurt, & Ertemsir, 2012)

According to Kovach et al. (2002), HRIS is considered as a systematic procedure for collecting, storing, maintaining, and recovering data required by an organization about their human resources, personnel activities and organizational characteristics. Human resource information system varies among organizations in relation to their size; it can be informal as the payroll records and time cards in a small organization. For huge organizations corporate success depends on the coordinated strategic management and integration of the company's human resources and information technology.

Some authors have proposed that the use of a Human Resource Information System (HRIS) would reduce Human Resource (HR) costs by automating information and reducing the need for large numbers of Human Resource (HR) employees; by helping employees to control their own personal information thus improving job satisfaction; and by allowing managers to access relevant information and data, conduct analysis, make decisions, and communicate with others without consulting a Human Resource (HR) professional (Awazu & Desouza, 2003; Ball, 2001). Ideally, with appropriate application of HRIS, less people should be needed to perform administrative tasks such as record keeping and more time would be made available for Human Resource (HR) Managers to assist by providing data on a strategic level. Many of these authors believe the future to be bright for HRIS as it creates new paths for human resources and for the organizations that effectively use HRIS.

Dessler (2005) stated that through computerized human resource information systems employees are recruited, better working environment is made and motivation is made, since their job assignments are clearly defined and outlined thus a significant improvement in employee job satisfaction. It is also informed of what the expected outcome is, that is; getting work done. This leads to better performance and an organization is in a good position to achieve its strategic goals.

According to Lippert and Swiercz (2005), computerized human resource information systems help human resources of an organization in performing different processes thus leads to integration to overall strategy of an organization and job satisfaction. Competitive advantage can be attained by applying Strategic approach to Human resources and thus getting benefit from the abilities of People. A transaction processing system is a computerized system that performs and records the daily routine transactions necessary to conduct business and is used at the operational level of an organization. Examples are skills inventory, employee training data, performance data and payroll. Management information system serves the management level of the organization, providing managers with reports and often online access to the organization’s current performance and historical records. It primarily serves the functions of planning, controlling, and decision making at the management level.

Hussain et al. (2011), studied the impact of computerized human resource information systems on job satisfaction in banks of Iran and concluded that computerized human resource information systems have a major impact on job satisfaction. They deducted that computerized human resource information systems can be applied for making various strategic human resource decisions and computerized HRIS in conjunction with strategic human management makes a remarkable difference in the performance of a bank.

The implementation and application of HRIS varies between organizations. Some use it to reduce costs, others to facilitate better communication, and some use it to re-orient HR operations to increase the department’s strategic contribution (Parry, Tyson, Selbie, & Leighton, 2007). An HRIS can have a wide range of usage from simple spread sheets to complex calculations performed easily (Parry, 2009). Through a proper HR management, firms are able to perform calculations that have effects on the business as a whole. Such calculations include health-care costs per employee, pay benefits as a percentage of operating expense, cost per hire, return on training, turnover rates and costs, time required to fill certain jobs, return on human capital invested, and human value added.

It is very difficult to ascertain the value addition made by HRIS on the revenues and profits of an organization since strategic HRIS is beneficial in facilitating the decision-making process. These decisions can result in greater employee motivation and satisfaction and both are extremely difficult to quantify (Kovach, Hughes, Fagan, & Maggitti, 2002). Mayfield and Lunce (2003), came to a similar conclusion that while administrative activities can be quantified and measured such as reduction in turnover and efficiency of HR department, it is very difficult to attribute certain gains such as motivation and morale directly to the implementation of HRIS.

HRIS implementation has however faced numerous challenges, available literature shows that many organizations have problems when implementing new technologies including HRIS, due to many barriers including; lack of sufficient capital and skills (Ngai & Wat, 2006), cost of setting up and maintaining HRIS (Beckers & Bsat, 2002), lack of top management support and commitment, lack of HR knowledge by system designers and the lack of applications for HR users (Kovach & Cathcart, 2002).

Krishnan and Singh (2006) explored the barriers faced by Indian organizations in implementing and managing HRIS. The main HRIS problems were; lack of knowledge of HR department about HRIS and lack of importance given to HR department in these organizations. Cooperation is required across various functions and divisions of the organization for proper implementation of HRIS. Ball (2001) justifies the low-level usage of HRIS output by HR practitioners by organizational size, HRIS time in use, organization culture and strategy, and IT skills.

A study on implementation level of HRIS applications and its effectiveness in business organisations in Jordan by Al-Dmour (2014) suggested that Top Management Support influences the decision to adopt HRIS as well as moderately impacts the extent of HRIS implementation.

2.3.1	Components of HRIS

Human Resources Information System will work if the human resource manager is competent (Al-Mobaideen et al., 2013; Gardner et al., 2003), human resources department and information technology are involved right from the process of formation and implementation of Human Resources Information System. These two divisions are in charge of holding trainings, making documentation and developing applications. Implementation of Human Resources Information System is hard since it is “a part of” information system, not human resources and it is seen as transactional-oriented, stiff and inﬂexible section (Stone & Dulebohn, 2013). HRIS shall play a strategic role for human resource department when process of services is increasing (Ruel et al., 2004; Strohmeier, 2007; Schalk et al., 2013). Unclear influence of information system on professionalism of human resource makes HRIS fail (Zhang & Wang, 2006; Basoglu et al., 2007).

The major functional components of human resource information system (HRIS) are; input, data maintenance and output (Kovach, Hughes, Fagan, & Maggitti, 2002). Personnel information is always entered into HRIS through input function. Before HRIS data entry was one way; however, modern technologies used for scanning enabled scanning and storing actual image of original documents. After entry of information into the database of information system through input function, the data maintenance function updates the database.

The human resource information system output is the most visible function of HRIS. Generation of valuable output requires HRIS to process, make calculations, and then present output in a comprehendible way (Kovach & Cathcart, 1999). Moreover, HRIS is comprised of Management and IT. Human resource information system management include; employees, policies made to run organizations effectively and data; while IT is having computer hardware and software. (Hendrickson, 2003).

2.3.2	Users and Uses of HRIS

Human resource information system (HRIS) can meet the needs of stakeholders of the organization. There are three groups of people who interact with HRIS in an organization; human resource professionals, functional managers and employees (Anderson, 1997). The HR professionals rely on HRIS to perform even elementary tasks. The functional managers rely on HRIS for provision of functionality to achieve the objectives of the unit. Managers are also dependent on the HRIS for superior data collection and analysis. Individual employees acquire more awareness of HRIS applications due to their need to control category selections as employee benefit options become complex.

Hendrickson (2003), large and small businesses are now using HRIS as their HR function in the organization. Furthermore, in the 21st century, according to Edelhauser (2012), “Advances in technology have made focus on the human computer interface a prime objective. The Internet has changed the way most businesses engage with customers and even their own employees” (p. 756). Thus today’s business organizations are dependent on technology such as HRIS for better information gathering, processing and dissemination leading to better HR processing (Arnold, 2007).

According to some studies, technology usefulness is related to HRIS performance. It is understandable that technology usefulness is important and has a major part in achieving HRIS performance. Technology is not only important to enhance organizational performance, but it is supposed to be consistently useful. According to a study by Normalini et al. (2012), technology usefulness is positively related to HRIS usage. The findings of this study are based on a questionnaire used to collect data through purposive sampling technique, which includes selected companies using HRIS in 35 Penang Malaysia. Another study by Shibly (2011), which used quantitative research design among 18,000 full-time staff members in Jordan, found that perceived HRIS usefulness is positively and indirectly linked to HRIS performance. Therefore, it can be said that HRIS performance is directly and indirectly affected by technology usefulness because if the system is useful, the users can maximize their productivity.

Therefore, several HR functions are automated to some degree for improving both efficiency and effectiveness. To enhance the effectiveness of personnel functional departments, organizations are relying more on HRIS (Ball, 2001; Lippert & Swiercz, 2005; Troshani, Jerram, & Gerrard, 2010). Moreover, multifold advantages of HRIS are both strategic and administrative (Kovach, Hughes, Fagan, & Maggitti, 2002). At the strategic level, HRIS is recorded to effect performance positively (De Pablos, 2004; Katou & Budhwar, 2006). HRIS tools are used to help in making a decision in HR functions (Farndale, Scullion, & Sparrow, 2010). There is widening reliance on HRIS (Hendrickson, 2003).

While at the administrative level, the system is able to record complete information of potential or current employees (Harris & Desimone, 1995). It enhances the efficiency of employees and reduces the operational costs (Beadles, Lowery, & Johns, 2005; Dery, Grant, & Wiblen, 2009; Dery, Grant, & Wiblen, 2010). It is identified that enhanced productivity, lower costs, return on investment, and effective employee communications are the top four benefits of automated system (Wyatt, 2009). HRIS supported human resource management of 60 percent of Fortune 500 companies (Ball, 2001). It is suggested that HRIS is used for administrative, strategic and decision-making purposes (Kovach, Hughes, Fagan, & Maggitti, 2002). Rahman, Islam, & Qi, (2017 in their study established three broad inhibiting factors termed as Financial, Management related, and Organizational; specifically, High investment, Costly maintenance, Long-term benefit, Organizational Structure, Culture of the Organization, Top management support, lack of experts and user, are found as major barriers to implementation of HRIS in Garments Factories.

2.4	Theories on Information Technology Usage

There are many theories used in Information System research (Wade, 2009) which include: Diffusion of Innovation DOI Model (Rogers, 1995), and the Technology Organization Environment TOE framework (Tornatzky & Fleischer, 1990). The DOI model and the TOE framework are especially the only ones that are used at the firm level while the rest are used at the individual level. The researcher was specifically interested in adopting the TOE framework and DOI Model to determine the Organizational factors affecting the use of Human Resource Information System (HRIS) among manufacturing firms in Nairobi County, Kenya.

2.4.1	The Diffusion of Innovation Theory

DOI is a theory of how, why, and at what rate new ideas and technology spread through cultures, operating at the individual and firm level. DOI theory sees innovations as being communicated through certain channels over time and within a particular social system (Rogers, 1995). Individuals are seen as possessing different degrees of willingness to adopt innovations, and thus it is generally observed that the portion of the population adopting an innovation is approximately normally distributed over time (Rogers, 1995).

Rogers (1995) clustered the normal distribution into segments that led to segregation of individuals into five categories of individual innovativeness (from earliest to latest adopters): innovators, early adopters, early majority, late majority, laggards. The innovation process in organizations is much more complex. It generally involves a number of individuals, perhaps including both supporters and opponents of the new idea, each of whom plays a role in the innovation-decision.

Based on DOI theory at firm level (Rogers, 1995), innovativeness is related to such independent variables as individual (leader) characteristics, internal organizational structural characteristics, and external characteristics of the organization; (a) Individual characteristics describes the leader’s attitude toward change. (b) Internal characteristics of organizational structure includes observations according to Rogers (1995) whereby: “centralization is the degree to which power and control in a system are concentrated in the hands of a relatively few individuals”; “complexity is the degree to which an organization’s members possess a relatively high level of knowledge and expertise”; “formalization is the degree to which an organization emphasizes its members’ following rules and procedures”; “interconnectedness is the degree to which the units in a social system are linked by interpersonal networks”; “organizational slack is the degree to which uncommitted resources are available to an organization”; “size is the number of employees of the organization”. (c) External characteristics of organizational

2.4.2	The Technology-Organization-Environment Theory

According to Tornatzky and Fleisher (1990) the process by which a firm uses/adopts and implements technological innovations is influenced by the technological context, the organizational context, and the environmental context. The technological context includes the internal and external technologies that are relevant to the firm. Technologies may include both equipment as well as processes. The organizational context refers to the characteristics and resources of the firm, including the firm’s size, degree of centralization, degree of formalization, managerial structure, human resources, amount of slack resources, and linkages among employees.

The environmental context includes the size and structure of the industry, the firm’s competitors, the macroeconomic context, and the regulatory environment. These three elements present both constraints and opportunities for technological innovation. Thus, they influence the way a firm sees the need for, searches for, and adopts new technology. Technology usage is complex and context-sensitive (Wolfe, 1994).

2.5	Summary of Theories and Research Gap

Innovation adoption, particularly technology usage, has drawn much attention of researchers. This research study was theoretically founded on literature of innovation usage. Numerous studied have focused on innovation adoption in not only the developed but also in the developing countries. Different models to address innovation adoption have been derived from four theories: Roger’s innovation diffusion theory, Feishbein’s theory reasoned action, Theory of planned behavior, and technology acceptance models. These theories have focused on individual as well as organizational levels.

Roger’s theory included the benefits, barriers, and other factors, that affect adoption. He linked decision of adoption to the following innovation-related attributes; relative advantage, complexity, compatibility, trial ability, observability. According to Kwon and Zmud (1987), framework which attributed usage of information technology (IT) as part of process of implementation of information systems, which innovation and IT usage is influenced by several major contextual factors; innovation characteristics, organizational characteristics, environmental characteristics, task characteristics, individual characteristics. It was suggested that all work done on innovation could be analyzed from two distinct perspectives: adoption and usage (Attewell, 1992).

In examining organizational factors and application of HRIS among manufacturing firms in Kenya, the study adopted the technology-organization-environment (TOE) framework and DOI model as a suitable analytical tool for establishing the study construct and concept. The TOE framework has been applied to investigate the adoption of a wide variety of technological innovations. The study sought therefore to establish the relationship between organizational specific characteristics and the organization’s openness to change on application of human resource information system.

2.6	Organizational Determinants of Application of HRIS

HRIS is referred to as the systematic computerized processing of human resource functions in an organization. The major function of HRIS is to retrieve and disseminate appropriate information pertaining to human resources. However, similar to the adoption/use of any technology, the application of HRIS in organizations has also met with debate and resistance. At individual level, there was defense of personal interest and values.

Human resource managers have to adhere to HRIS usage and effectively utilize innovations within the organization to enhance organizational effectiveness and productivity. However, there has been debate to the potential advantages of using such innovation, along with barriers to design and utilization. The study sought to establish the organizational factors influencing HRIS usage, since organizations are ready to adapt changes if they feel the need to have competitive advantage. Nevertheless, many organizations resist new technology implementation, including HRIS, unless benefits were professed.

2.6.1	Top Management Support and Commitment

Top management support, involvement and participation plays a large role. The strong commitment of top management, especially of a particular ‘innovation champion’, leads to early usage, while a lack of top management commitment will inhibit usage (Mehrtens et al., 2001; Chan & Mills, 2002). Hendrickson (2003), noted that large and small businesses are now using HRIS as their HR function in the organization. Furthermore, in the 21st century, according to Edelhauser (2012), “Advances in technology have made focus on the human computer interface a prime objective. The Internet has changed the way most businesses engage with customers and even their own employees”. Thus today’s business organizations are dependent on technology such as HRIS for better information gathering, processing, and dissemination leading to better HR processing (Arnold, 2007).

The support of top management can demonstrate their confidence in HRIS by personally utilizing the system. Their frequent personal HRIS usage may result in sufficient delegation of resources and an increased pressure for HRIS success. Ang et al. (2001) examined the usage of IT in support of total quality management (TQM). Among the organizational factors explored, top management support for IT applications as the highest predictor of IT usage.

Shrivastava et al. (2003) concluded that there is universal agreement that large-scale technology projects generally fail due to managerial, and not technical, reasons. Additionally, they identified that climate conduciveness for technology implementation related positively to the extent of neutralization of inhibitors. Lado and Wilson (1994) realized that conditions in the firm’s external and internal environment might enable or constraint the capacity of HR systems to develop and exploit organizational competencies.

Razali and Vrontis (2010) indicated that top management involvement and organizational commitment appeared as the two largest coefficients for the impact on the acceptance level of employees toward the new HRIS implemented. Ngai and Wat (2006) also indicated that support of top management was one of the most important factors in successful implementation of HRIS in Hong Kong. It is also worth noting that the use of an innovation process may vary across cultures in the rate of innovation activity and in the importance placed on management decisions (Murphy & Southey, 2003).The literature has also acknowledged the critical nature of the ‘innovation champion’ - “a charismatic individual who throws his or her weight behind an innovation” (Rogers, 2003) in the successful adoption of HRIS.

According to a study by Altarawneh (2010), top management support is related to HRIS performance; however, a lack of management support and commitment is one of the four HRIS implementation barriers in an organization. The findings of their study were derived using quantitative analysis of ANOVA among 230 HRIS users in Jordanian public universities. Another study by Hussein (2005) among 201 users from four central Malaysian government agencies using perceptual measures found a direct influence of top management support on HRIS performance. Moreover, Wong et al. (as cited in Lin, 1997) among a sample of 240 managers from directories of the Human Resources Development Association and Chinese Human Resource Management Association in Taiwan found that the context of the findings reported is deemed important to ensure the success of HRIS implementation with the acceptance for “the most needed support comes from top management” Furthermore, according to Lin (1997), the implementation of HRIS depends upon the support of the management, the IS department, the human resource leaders, the human resource staff, the computer knowledge of HR staff, and training the user in order to achieve the HRIS performance.

In addition, top management support is like a guarantee or assurance that should be given to the users. As the organization adopts a new system, it needs backing. Without any support from the management, the ideas, inputs, or suggestions coming from the workers will never be heard, and there will be no changes at all. In addition, support from the top management is one of the important factors for the adoption of technology and will affect user acceptance. In a study by Rouibah, Hamdy, and Al-Enezi (2009), using SEM among 382 information system users in public organizations in Kuwait, it was concluded that among the organizational variables, top management support was found to have the strongest effect on HRIS performance. For that reason, top management support is needed and is important to assure the users’ positive attitude and perception in adopting technology through new innovation to achieve HRIS performance. Commitment is a two-way process between one person engaged with another or with an organization. Meyer and Allen (as cited in Dixit & Bhati, 2012) define commitment as “a psychological state that characterizes the employees relationship with the organization and has implication for the decision to continue membership in the organization”. In addition, Sarwar and Khalid (2011) state that user commitment had important impacts on organization through its effects on employee performance, turnover, and absence, and it influences customer attitudes to the bottom line. Furthermore, there are three forms of commitment: namely, affective commitment, which refers to an employee’s emotional attachment to, identification with, and involvement in the organization; continuance commitment, which refers to commitment based on the costs that the employees associate with leaving the organization; and normative commitment, which refers to an employee’s feelings of obligation to remain with the organization (Meyer & Allen, 1991). Therefore, it can be said that user commitment is one of the important factors after user knowledge and skill in user characteristics. If the users have the knowledge and skills to do the job, but they do not have commitment to do their work properly, it is not acceptable to the organization

2.6.2	Degree of Centralization

This refers to the extent to which power and control are concentrated in the hands of few individuals (Rogers, 1995). It also refers to the degree of delegation of authority in an organization, that is, the less authority is delegated in an organization the greater its centralization (Belassi & Fadlalla, 1998). The relationship between centralization and technology adoption has been previously examined though findings have been found to be inconsistent (Belassi & Fadlalla, 1998).

While the extent literature agrees that the degree of centralization in decision making plays a large role in IT/IS usage (Hage & Aiken, 1969), opinions on the optimal degree of centralization differ from study to study. Previous studies found decentralized decision-making as one of the strongest facilitators of customer-based information inter-organizational system (CIOS) usage (Grover, 2003) and IT use in large and complex organizations (Boynton et al., 1994). Zaltman, Duncan, and Holbek (1973) conclude that more formalized and centralized organizations have lower levels of innovativeness. Arad et al. (1997) asserted that a flat structure, autonomy, and work teams will promote innovation, whereas specialization, formalization, standardization and centralization will inhibit innovation.

Conversely, several studies indicated that a highly centralized organizational design leads to more effective end user computing (Brown & Bostrom, 1994) and the use of more successful strategic information systems applications (King & Sabherwal, 1992). Pierce and Delbecq (1977) suggest centralization of decision-making may reduce conflict between organizational units and foster innovation usages. In support of this proposition, Ettlie et al. (1984) found that organizations with a centralized structure were more likely to use new technologies. Burns and Stalker (1961) discuss two different types of organizational structure: mechanistic and organic. A mechanistic structure is more rigid, consisting of clearly delineated jobs, a well-defined hierarchical structure, and a formal chain of command.

An organic structure is more dynamic, decentralized, flexible, and informal. Daft and Langel (1986) argue that organizations with a more organic organizational structure are more readily willing and able to use/adopt new technology. Even though some studies have attributed technology usage/adoption to be facilitated in organizations that exhibit high degree of centralization because top management can make adoption/usage decisions irrespective of resistance from lower level managers or employees (Shaukat & Zafar 2010; Williams, 1994). By contrast, other studies attributes the high degree of centralization to impact negatively the decision to adopt/use technologies that are more compatible with the interests of lower level employees than those of higher level employees (Yang et al., 2007).

2.6.3	Organizational Culture

Organizational culture is associated with the organization’s sense of identity, its goal or core values, its primary ways of working and a set of shared assumptions. Organizational culture does not change easily (Leidner & Kayworth, 2006). Older firms usually favor the status quo and have structural inertia from entrenched processes, which hinders adoption of new technologies. Jones et al. (2005) similarly suggests that organizational cultures with a more supportive climate and flexible structures might be more advantageous to successful deployment of new technologies, than organizations with less flexible and more mechanistic cultures. Although some staff may be resistant to changing their ways, empowering them gives a sense of involvement in the shift away from manual systems. According to Jackson (2011), organizational culture continues to be cited as an important factor in the success or failure of information systems (IS) usage. This is evidenced by the growing trend in the number of studies that address cultural issues in IS literature over the last several decades.

Chi et al. (2007) small businesses and any businesses that have high levels of employee autonomy or highly value employee welfare are more likely to introduce employee involvement programs, have a TQM program, or utilize diverse planning teams. Business or organizations that have values such as flexibility, freedom, and cooperative teamwork will promote innovation, while values such as rigidity, control, predictability, and stability will hinder innovation. Hoffman and Klepper (2000) also established that organizations that portray low in sociability and high in solidarity so-called ‘mercenary cultures’ will experience more favorable outcomes with technology assimilation and its usage. Jackson (2011) states that organizational culture continues to be cited as an important factor in the success or failure of information systems (IS) adoption. This is evidenced by the growing trend in the number of studies that address cultural issues in IS literature over the last several decades.

2.6.4	Competitive Pressure

Several empirical studies show that competitive pressure is a powerful driver of IT usage, adoption and diffusion (Murphy& Southey, 2003; Rogers, 2003; Urbano & Yordanova, 2008). As organizations move towards a knowledge-based economy, the pressures continue to grow for HR to reduce costs and serve a more strategic role in the organization. As competitive pressures increase, the importance of managing human resources well becomes more apparent. Organizations are thus using HRIS to help make more informed decisions, get the most out of their employees, streamline HR processes, and better allocate HR resources. Hence, the drive to be competitive in all business aspects will lead to the adoption and application of HRIS.

According to Arad et al. (2007) competition is not a significant factor influencing the application of HRIS, indicating that competition does not truly provide any direct ‘push’ for organizations to use HRIS. This result may be due to the fact that many top managers and boards of directors perceive HRIS as more administrative than strategic and therefore do not view HRIS as being able to deal with the competition in the external environment (Urbano & Yordanova, 2008). This implies that competition in the external environment would not induce organizations to use HRIS, however, once they have used HRIS and are more familiar with it and aware of its benefits, companies are willing to invest even further in HRIS when faced with additional external environmental pressures.

In his overview, Richard (2006) argued that competition may reduce the incentive to innovate if intellectual property rights are nonexclusive but will foster innovation if they are exclusive. Schmutzler (2010) identified the countervailing effect of own demand shifts following a competitor adopting a cost-reducing innovation if products are complements, although the effect becomes re- enforcing if they are substitutes. Finally (although this review is not exhaustive), Vives (2008) stressed that incentives to innovate in competitive or monopolistic settings depend on whether entry is free or restricted.

2.7	Conceptual Framework

In this section, the conceptual framework is presented in a schematic interpretation as shown in Figure 2.1 below. It identifies the variables (independent and dependent) that when put together explains the issue of concern.

Independent Variable	Dependent Variable

Application of HRIS Top management support

Degree of centralization

Organization culture

Competitive pressure

Figure 2. 1 Conceptual Framework depicting Organizational factors affecting application of HRIS.

Source: Researcher (2016)

CHAPTER THREE

RESEARCH METHODOLOGY

3.1	Introduction

This chapter describes research design, study location, target population, sample size and sampling techniques, research instruments, validity of instruments and reliability of instruments, data collection procedures, data analysis techniques and ethical considerations.

3.2	Research Design

Research design is a master plan of how the study will answer the research question (Saunders et al., 2007). In other words, it provides the procedural structures that the researcher follows, including data collection and data analysis (Leedy & Ormrod, 2005).

An explanatory research design was used in the study. Explanatory research design shows the cause effect relationship between the independent variables and dependent variables. It gathers data at a particular point in time with the intention of describing the nature of the existing conditions, identifying the standards against which existing conditions can be compared and determining the relationship that exists between specific events (Orodho, 2005).

3.3	Study Location

The study was carried out within the county of Nairobi which is the most growing city in Kenya according to World Bank report, (2015) .Nairobi is the capital and largest city of Kenya. During Kenya's colonial period, the city became a centre for the colony's coffee, tea and sisal industry. Nairobi is one of the most prominent cities in Africa, both politically and financially. Home to thousands of Kenyan businesses and over 100 major international companies and organizations, including the United Nations Environment Programme (UNEP) and the United Nations Office at Nairobi (UNON), Nairobi is an established hub for business and culture. Nairobi is the regional headquarters of several international companies and organizations.

In 2007, General Electric, Young & Rubicam, Google, Coca-Cola, IBM Services, Airtel, and Cisco Systems relocated their African headquarters to the city. Goods manufactured in Nairobi include clothing, textiles, building materials, processed foods, beverages, and cigarettes. Nairobi has a large tourist industry, being both a tourist destination and a transport hub. Kenya Airways, Africa's fourth largest airline, uses Nairobi's Jomo Kenyatta International Airport as a hub. Nairobi is currently undergoing major road constructions to update its infrastructure network. The new systems of roads, flyovers, and bridges would cut outrageous traffic levels caused the inability of the current infrastructure to cope with the soaring economic growth in the past few years. It is also a major component of Kenya's Vision 2030 and Nairobi Metropolis plans. Most roads now, though, are well lit and surfaced with adequate signage.

Nairobi is home to most of Kenya's news and media organizations. Several multinational media organisations have their regional headquarters in Nairobi. These include the BBC, CNN, Agence France-Presse, Reuters, Deutsche Welle, and the Associated Press. It is the centre of Kenya's music scene, majority of music videos of leading local musicians are also filmed in the city and many foreign musicians who tour Africa perform in Nairobi. It is also the African Great Lakes region's sporting centre. The premier sports facilities include: Moi International Sports Centre, The Nyayo National Stadium, Nairobi City Stadium and several golf courses. The city also hosts numerous private and public institutions of Higher learning. (http://www.nairobi.go.ke/)

3.4	Target Population

Target population is a set of people or objects the researcher would want to generalize the results of the research (Jwan, 2010). The target population for the study comprised of manufacturing firms in Nairobi, Kenya. The manufacturing sector in Kenya is currently facing stiff competition from imports. To enhance effectiveness and competitiveness the firms need to manage employees (human capital) well with an aid of integrating information technology into the organization. The sentiments are in line with Jenkins and Lloyd (1995), Human resources and Information Technology are the two elements that many firms are learning to use as strategic weapons to compete. The study targeted all the 478 manufacturing firms in Nairobi (Kenya Association of Manufacturers, 2015). Therefore 478 human resource managers were the target population of interest for the study and they were selected purposively. The choice of HR Managers as respondent’s aids because of their knowledge on application of HRIS in their organizations hence are better placed to address the issues sought by this study.

KAM members are categorized into 14 sectors, 12 of which are in processing and value addition while the other two offer essential services to enhance formal industry. Sub-sectors are defined by the type of raw materials companies import or the products they manufacture. (http://kam.co.ke/sectors/, 2014)

Table 3. 1 KAM Members Categorization

SECTOR/STRATA	PERCENTAGE (%)
BUILDING, MINING & CONSTRUCTION	3
CHEMICAL & ALLIED	9
ENERGY, ELECTRICAL & ELECTRONICS	5
FOOD & BEVERAGES	22
LEATHER & FOOTWEAR	1
METAL & ALLIED	9
AUTOMOTIVE	6
PAPER & BOARD	8
PHARMACEUTICAL & MEDICAL EQUIPMENT	3
PLASTICS & RUBBER	3
SERVICES & CONSULTANCY	10
TEXTILES & APPAREL	7
TIMBER, WOOD & FURNITURE	2
AGRICULTURE/FRESH PRODUCE	1

Source (http://kam.co.ke/sectors/, 2014)

3.4.1	Sample Size and Sampling Procedure

The larger the samples size the lower the likely error in generalizing the population (Saunders, Lewis, & Thornhill, 2003). According to Gupta (2002), sampling is a technique of data collection from part of the universe of study and conclusions about the entire universe are drawn on that basis of the sampled responded. The sample size of the study was obtained using, Krejcie and Morgan’s (1970, see appendix V) Table. Therefore the sample size of the study were 212 human resource managers.

Sampling Procedure involves selecting a given number of respondents from a representative of a defined population (Orodho, 2004). The main function of sampling is to allow the researcher to conduct the study on individuals from the population so that the result of the study conclusion be applied to the entire population. The study adopted a Stratified sampling, design which allowed the segregation of population into several mutually exclusive sub-populations or strata which aided in increasing the sample statistical efficiency. The study categorized the manufacturing firms into different subsectors according to the Kenya Association of Manufacturers under the Ministry of Industrialization which divided the manufacturing firms into the sub-sectors, using International Standard Industrial Classification. The manufacturing firms were then selected systematically and the HR managers were selected purposively.

3.5	Data Collection, Instruments and Procedure

This section discusses the type of data, data collection tools and data collection procedure.

3.5.1	Type of Data

The study utilized a self-administered questionnaire to collect primary data from the respondents.

3.5.2 Research Instruments

Research questionnaires are tools that are used by the researcher to collect data from the sampled respondents in a study (Kombo & Tromp, 2006). The main instruments for data collection were Likert scale questionnaires. The questionnaire was a convenient tool especially where there are large numbers of subjects to be handled.

3.5.3 Data collection procedure

The researcher used both drop and pick method and mailing method to distribute the questionnaires.

3.6	Reliability and Validity of Research Instruments

3.6.1 Reliability of Research Instruments

Reliability is the ability of the instrument to consistently yield the same results when repeated measurements are taken of the same individual under the same conditions. Reliability test was conducted in line with Patton (2002), the study accepted Cronbach Alpha values of 0.7 and above as recommended by Hair et al., (2010).

3.6.2 Validity of Research Instruments

According to Fraenkel and Wallen (2000), validity is the quality attributed to proposition or measures to the degree to which they conform to established knowledge or truth. In order to further ascertain validity of the research instruments, the researcher pilot tested the instruments by distributing questionnaires to other manufacturing firms within Thika Town, Kiambu County.

The content validity of the instrument was determined in two ways. First the researcher discussed the items in the instrument with the supervisor and lecturers from the department and colleagues. The advice given by these people helped the researcher in determining the validity of the research instruments. While the results of the piloted questionnaires enabled in determining the consistency of responses made by respondents and adjusted the items accordingly by revising the document. Further factor analysis was used to validate the variables.

3.7	Measurement of Variables.

The questionnaire was both structured and unstructured. It facilitated easy and quick derivation of information within a short time (Patton, 2002).The study instrument was adapted from previous research with a little modification. The utilization of previous studies’ instruments not only assisted in the reliability and validity of the instruments, but it also helped reduce the amount of work needed in developing and testing new instruments and thus manages to save time in conducting the research (Morgan & Hunt, 1994).

A three-part questionnaire was used to collect data from the respondent. The first part of the questionnaire was a five-point Likert scales, ranging from (1) “Strongly disagree”, (2) “Disagree”, (3) “Neutral”, (4) “Agree” and (5) “Strongly agree” which was used to measure the dependent variables constructs (use of human resource information system) adopted from Beckers and Bsat, (2002), while the second part was a five-point Likert scales, ranging from (1) “Strongly disagree”, (2) “Disagree”, (3) “Neutral”, (4) “Agree” and (5) “Strongly agree” which was used to measure the independent variables constructs (Top management commitment, organization degree of centralization, organization culture, and organization competitive attitude). The third and last section was used to collect the demographic characteristics of the organization and respondents’ bio information.

3.8	Data Analysis and Presentation

The study employed a quantitative approach where initial data analysis was done using simple descriptive statistical measures such as, mean and standard deviation to give a glimpse of the general trend. While correlation analysis was used to determine the nature of the relationship between the independent and dependent variables. In addition multiple regression analysis was also employed to test the study hypotheses. Multiple regression analysis was applied to analyze the relationship between a single dependent variable and several independent variables (Hair et al., 2010). The study used Variance Inflation Factor (VIF) to handle the issue of Multicollinearity,The study also tested for Normality, Multi-collinearity and linearity.

Table 3. 2 Data Analysis Summary

The table below summarizes the various tests undertaken, the tools used and how the data analysis was performed.

Respodents’ Responses	Items	Type of data	Software	Descriptive statistics
Demographic characteristics	Age, Education Gender, No. of Employees	Discrete	Spss	Mean, percentages, frequency
Variable Characteristics	Several Variables	Continuous	Spss	Mean, Standard deviations, skewness
Hypothesis testing/Test of assumptions	Type of data	Statistical test
Data reduction/Factor Analysis	Several variables	Continuous	spss	Kaiser- Meyer- Olkin Measure (KMO )
Test of Normality	Several Variables	Continuous	spss	Shapiro Wilk
Significance of the Model	Several Predictors	Continuous	spss	Anova
Degree of relationships Strength & direction	A pair of variables X and Y	Continuous	spss	Pearson's r – simple regression
Prediction/causation	Several predictors	Continuous	spss	Multiple regression

3.8.1	Model Specification

The regression model that was used to test the hypothesis was as shown below:

Where, y = Use of Human Resource Information Systems

α = Constant

β1… β4= the slope representing degree of change in independent variable by one unit variable.

X1= Top management

X2= Degree of Centralization

X3= Organization culture

X4= Competitive pressure

ε = error term

All the above statistical tests were analyzed using the Statistical Package for Social Sciences (SPSS), version 20. With significant differences recorded at p < 0.05.

3.9	Limitations of the Study

A study of this magnitude cannot be possible without limitations. The questionnaire was used to collect data; which relied heavily on the honesty of the respondents. Some respondents might hesitate to respond to the questions for fear of victimization. To counter this, the researcher assured the respondents that the data will only be used for academic purposes and that their identity would be treated with utmost confidence. The sample size was drawn from manufacturing firms in Nairobi County and the generalizations of the study findings was limited to those manufacturing firms in Nairobi County.

3.10 Ethical Considerations

Kombo and Tromp (2006), note that researchers whose subjects are people or animals must consider the conduct of their research, and give attention to the ethical issues associated with carrying out their research. In line with the sentiments of Kombo and Tromp (2006) the respondents were given a clear indication of the purpose and outcomes of research. Permission to conduct the research was granted by Moi University and the participants were given confirmation that the data collected will be used for academic purpose only will be treated with confidential. Privacy and confidentiality was observed at all level of data collection and through analysis.

CHAPTER FOUR

DATA ANALYSIS, PRESENTATION AND INTERPRETATION

4.1 Introduction

This chapter covers the results and interpretation of the data analysis presented in sections as: response rate, data screening, reliability test, data transformation and descriptive statistics of study variables and bio-data, testing of assumptions of regression and testing of hypotheses.

4.2 Response Rate

Out of the 212 questionnaires administered a total of 166 were received back translating to an overall response rate of 78.3%. Questionnaire response rate is important in achieving dependable, valid and reliable results (Hair et al., 2007; Saunders et al., 2006).

Table 4. 1 Response Rate

Response	Number	Percentage
Administered questionnaires	212	100
Returned questionnaires	166	78.3

Source; (Survey Data, 2017)

4.3	Data Screening

Statistics analysis assumes sound measurement relatively free of coding errors, therefore a run of descriptive statistics on the data was done to ensure that it was generally as expected in terms of means, and standard deviations and that there were no out of bound entries beyond the expected range. This involved screening for missing values and no missing values were found.

4.4	Sample Characteristics

In this section of the analysis, the study takes into consideration the sample characteristics. The results are as presented in table 4.2. Variables included here are the gender, education level attained, firm size and age.

The study put into account the gender of the respondents. From the results, 62.7(104) of the respondents were male and 37.3% (62) were female. The results indicate that male human resources managers of manufacturing firms in Nairobi, Kenya comprise the majority.

The study put four variables to depict the education level attained by the respondents. The variables were technical training, undergraduate degree, graduate degree and other. The findings were technical training 10.8 percent, Undergraduate degree were 71.7 percent, Graduate degree were 16.9 percent and human resource managers with education levels other than the ones mentioned were 0.6 percent.

In terms of the firm size, 54.2% (90) of the respondents stated that their firm has over 400 employees, 41% (68) employees in the range of 301 to 400, 0.6% (1) 201 to 300 employees, and 4.2% (7) between 10 to 200 employees.

The study sought to establish firm age since older firms usually favor the status quo and have structural inertia from entrenched processes, which hinders adoption of new technologies as opposed to younger firms. With reference to firm age, 24.1% (40) of the human resource managers noted that the firm has been in existence for less than 5 years, 14.5% (24) for 6 to 10 years, 31.9% (53) for 11 to 15 years,16.9% (28) for 16 to 20 years and 12.7% (21) for over 20 years.

Table 4. 2 Sample Characteristics

Frequency	Percent
Gender	Male	104	62.7
Female	62	37.3
Total	166	100
Education Level Attained	Technical training	18	10.8
Undergraduate Degree	119	71.7
Graduate degree	28	16.9
Other	1	0.6
Total	166	100
Firm Size(number of employees)	10-200	7	4.2
201-300	1	0.6
301-400	68	41
Above 401	90	54.2
Total	166	100
Firm Age	less than 5 years	40	24.1
6-10 years	24	14.5
11-15 years	53	31.9
16-20 years	28	16.9
above 20 years	21	12.7
Total	166	100

Source; (Survey Data, 2017)

4.5	Descriptive statistics

This section discusses the descriptive statistics results on the application of HRIS, and the effect of Top management support, degree of centralization, organizational culture and competitive pressure on application of HRIS.

4.5.1	Application of HRIS

This section of the analysis highlights the results on the application of HRIS. Basing on the results in table 4.3, the organization uses human resource information system in personnel and salary administration (mean = 4.28, SD = 0.58). Also, the organization uses human resource information system in organization skill inventory (mean = 4.38, SD = 0.74). Further, the organization uses human resource information system in medical history (mean = 4.41, SD = 0.79). Besides, the organization uses human resource information system in manpower planning (mean = 4.5, SD = 0.67).

Moreover, the organization uses HRIS in career planning (mean = 4.42, SD = 0.83), decision- making and control (mean = 4.38, SD = 0.76). Additionally, the organization uses HRIS in employee selection and placement (mean = 4.42, SD = 0.80). Likewise, the organization uses HRIS in ingestion and training projections (mean = 4.48, SD = 0.78). Finally, the organization uses HRIS career-pathing, equity monitoring and productivity evaluation (mean = 4.48, SD = 0.69).

On average the human resource information system is used in in personnel and salary administration, organization skill inventory, medical history, manpower planning, career planning, decision- making and control, employee selection and placement, ingestion and training projections, career-pathing, equity monitoring and productivity evaluation. The results on human resource information system summed up to a composite mean of 3.97, standard deviation 0.50, skewness -1.59 and kurtosis 2.50.

Table 4. 3 Application of HRIS

N=166	Min	Max	Mean	SD	Skewness	Kurtosis
Our organization uses human resource information system in Personnel and salary administration	2	5	4.28	0.58	-0.69	2.58
Our organization uses human resource information system in organization skill inventory	2	5	4.38	0.74	-1.46	2.66
Our organization uses human resource information system in Medical history	2	5	4.41	0.79	-1.55	2.39
Our organization uses human resource information system in Manpower planning	2	5	4.50	0.68	-1.37	1.97
Our organization uses human resource information system in career planning	2	5	4.42	0.83	-1.61	2.23
Our organization uses human resource information system in decision-making, and control.	2	5	4.38	0.76	-1.52	2.73
Our organization uses human resource information system in employee selection and placement	2	5	4.42	0.80	-1.57	2.31
Our organization uses human resource information system in ingestion and training projections	2	5	4.48	0.78	-1.75	2.98
Our organization uses human resource information system Career-pathing, equity monitoring, and productivity evaluation.	2	5	4.48	0.69	-1.31	1.72
HRIS	2.4	4.5	3.97	0.50	-1.59	2.50

Source; (Survey Data, 2017)

4.5.2	Top management support

This section of the analysis highlights the results on top management support. Table 4.4 illustrates the results. It is evident from the results that the top management provides a conducive climate to support human resource information system usage (mean = 4.34, SD = 0.55). This implies that the top management has confidence in HRIS hence they have taken an initiative towards ensuring that there is a conducive environment for its use Also, the top management provides adequate resource to support human resource information system usage (mean = 4.52, SD = 0.65) and facilitates technical training to support human resource information system usage (mean = 4.49, SD = 0.73). The implication is that there is visible top management support which sends signal on the importance of the HRIS.

Table 4. 4 Top management support

N=166	Min	Max	Mean	SD	Skewness	Kurtosis
The top management provides a conducive climate to support human resource information system usage	2	5	4.34	0.55	-0.46	2.10
The top management provides adequate resource to support human resource information system usage	2	5	4.52	0.65	-1.58	3.44
The top management facilitate technical training to support human resource information system usage	2	5	4.49	0.73	-1.74	3.44
The top management provides avenues for innovation to support human resource information system usage	1	5	4.58	0.77	-2.33	5.91
The top management positive attitude and interest towards the adoption of human resource information system	2	5	4.26	0.85	-1.30	1.40
The management commitment provide an enabling environment for employees engagement	2	5	4.34	0.86	-1.53	2.02
open management style to allow human resource information system usage	2	5	4.55	0.64	-1.53	2.98
Top management support	2.71	5	4.40	0.46	-1.04	1.23

Source; (Survey Data, 2017)

In addition, the top management provides avenues for innovation to support human resource information system usage (mean = 4.58, SD = 0.77) and provides an enabling environment for employees’ engagement (mean = 4.34, SD = 0.86). The adoption of HRIS is further facilitated by positive attitude and interest by the top management towards its adoption (mean = 4.26, SD = 0.85).

Finally, there is an open management style that allows human resource information system usage (mean = 4.55, SD = 0.64). This, in turn, leads to positive attitudes on the part of users towards the application of HRIS. Generally, the results on top management support summed up to a composite mean of 4.4, standard deviation of 0.46, skewness -1.04 and kurtosis 1.23.

4.5.3	Degree of Centralization

Table 4.5 illustrates the results on degree of centralization. Basing on the findings, the top management solely approves the implementation of HRIS projects (mean = 4.53, SD = 0.69). The implication is that the top management can make HRIS adoption/usage decisions despite resistance from other levels of management or the staff.

Further, job specialization is enhanced through the organization setting of departments (mean = 4.31, SD = 0.97). Moreover, job formalization is enhanced through the organization setting (mean = 4.48, SD = 0.59). Besides, job standardization is enhanced through the organization setting (mean = 4.31, SD = 0.69). In this regard, conflict between organizational units are reduced thus fostering innovation usages.

Table 4. 5 Degree of centralization

N=166	Min	Max	Mean	Std. Deviation	Skewness	Kurtosis
Top management solely approves the implementation of HRIS projects	2	5	4.53	0.69	-1.83	4.25
Job Specialization is enhanced through the organization setting of departments	2	5	4.31	0.97	-1.43	1.00
Job formalization is enhanced through the organization setting	2	5	4.48	0.59	-0.833	0.69
Job standardization is enhanced through the organization setting	2	5	4.31	0.69	-1.27	2.85
There is availability of skilled HRIS professionals in the organization to facilitate implementation and application of HRIS	2	5	4.5	0.74	-1.75	3.32
consultation with other departmental staff does occur regarding legislative, technical, and cultural requirements specifications of HRIS implementation	2	5	4.39	0.67	-0.88	0.71
Training for all users’ levels such as operation and strategic level is done to increase knowledge and skills in using HRIS system effectively	1	5	4.12	0.60	-1.75	0.19
Degree of centralization	2.71	5	4.38	0.42	-1.14	1.52

Source; (Survey Data, 2017)

Also, there is availability of skilled HRIS professionals in the organization because if the users have lack of understanding (mean = 4.5, SD = 0.74). In this manner, users of HRIS can consult on its usage hence making it more effective to the organization.

Furthermore, consultation with other departmental staff does occur regarding legislative, technical and cultural requirements specifications of HRIS implementation (mean = 4.39, SD = 0.67). As well, there is training for all users’ levels such as operation and strategic level is done to increase knowledge and skills in using HRIS system effectively (mean = 4.12, SD = 0.60). To sum up, findings on degree of centralization summed up to a composite mean of 4.38, standard deviation 0.42, skewness -1.14 and 1.52.

4.5.4	Organization Culture

In this section of the analysis, the study highlights the results on organization culture. The findings are as presented in table 4.6. It is clear from the results that the organization culture enhances innovation through flexible organization value (mean = 4.63, SD = 0.49). There is also freedom which promotes innovation and adoption of HRIS (mean = 4.39, SD = 0.6).

Further, the organization culture enhances cooperativeness that promotes innovation and adoption of HRIS (mean = 4.37, SD = 0.73). Besides, the organization culture enhances teamwork that eventually promotes innovation and adoption of HRIS (mean = 4.41, SD = 0.65). Also, the organization culture has enabled sociability and solidarity among employees at all levels (mean = 4.26, SD = 0.77). Moreover, the organizations have open and flexible corporate cultures, as a result employees at all levels tend to view changes positively and are more willing to adapt to the changes from HRIS adoption (mean = 4.4, SD = 0.62).

Table 4. 6 Organization culture

N=166	Min	Max	Mean	SD	Skewness	Kurtosis
Our organization culture enhances innovation through flexible organization value	4	5	4.63	0.49	-0.53	-1.74
Our organization culture enhances freedom that promotes innovation and adoption of HRIS	2	5	4.39	0.6	-0.74	1.40
Our organization culture enhances cooperativeness that promotes innovation and adoption of HRIS	2	5	4.37	0.73	-1.38	2.54
Our organization culture enhances teamwork that eventually promotes innovation and adoption of HRIS	2	5	4.41	0.65	-0.92	0.94
Our organization culture has enabled sociability and solidarity among employees at all levels	2	5	4.26	0.77	-1.21	1.77
Our organization has an open and flexible corporate cultures as a result employees at all levels tend to view changes positively and are more willing to adapt to the changes from HRIS adoption.	2	5	4.4	0.62	-0.83	1.20
Our organizational cultures has a supportive climate and flexible structures that enhances successful deployment of new technologies	2	5	4.31	0.63	-0.80	1.66
OC	3	5	4.38	0.41	-1.13	1.97

Source; (Survey Data, 2017)

Finally, the organization culture has a supportive climate and flexible structures that enhance successful deployment of new technologies (mean = 4.31, SD = 0.63). Generally, the findings on organization culture summed up to a composite mean of 4.38, standard deviation 0.41, skewness -1.13 and kurtosis 1.97.

4.5.5	Competitive Pressure

This section of the analysis highlights the results on competitive pressure. The findings are as presented in table 4.7. The study has established that the organization initiates technological changes (mean = 4.41, SD = 0.56). Technological changes are a means to stay competitive and cope with the innovations in the manufacturing sector.

Also, the organization ignores speculation from technological changes (mean = 4.52, SD = 0.74) and focuses on what’s best for the organization and not competitors (mean = 4.52, SD = 0.73). On the same note, the organization does what is right in terms of technological adoption despite of competition facing the organization (mean = 4.51, SD = 0.74).

Further, the organization adopts the human resource information system because of what competitors do (mean = 4.4, SD = 0.71) and to enhance a more efficient human resource system (mean = 4.48, SD = 0.70). Finally, the organization adopts the human resource information system to enhance information decision and allocation of HR activities (mean = 4.54, SD = 0.68). To sum up, the findings on competitive pressure summed up to a mean of 4.50, standard deviation 0.488, skewness -1.65 and kurtosis 3.12.

Table 4. 7 Competitive pressure

N-166	Min	Max	Mean	Std. Deviation	Skewness	Kurtosis
Our organization Initiates technological changes	2	5	4.41	0.56	-0.67	1.90
Our organization ignores speculation from technological changes	1	5	4.52	0.74	-2.11	5.66
Out organization focuses on what's best for the organization and not competitors	2	5	4.52	0.73	-1.85	3.76
Our organization does what is right in terms of technological adoptions despite of competition facing the organization	2	5	4.51	0.74	-1.79	3.44
Our organization adopts HRIS because of what competitors do	2	5	4.4	0.71	-1.26	1.94
Our organization adopts HRIS to enhance a more efficient human resource system	2	5	4.48	0.70	-1.61	3.28
Our organization adopts HRIS to enhance information decision and allocation of HR activities	2	5	4.54	0.68	-1.63	2.91
CP	2.57	5	4.50	0.49	-1.65	3.12

Source; (Survey Data, 2017)

4.6	Reliability Analysis

Reliability refers to the consistence, stability, or dependability of the data. Whenever an investigator measures a variable, he or she wants to be sure that the measurement provides dependable and consistent results (Cooper & Schindler, 2003). To measure the reliability of the data collection instruments, an internal consistency technique using Cronbach's alpha was applied (Mugenda, 2008). As evidenced in table 4.8, the study variables have alpha coefficients higher than 0.7 meaning that the gathered data are reliable as they have a relatively high internal consistency and can be generalized to reflect opinions of all respondents in the target population.

Table 4. 8 Reliability analysis

Cronbach's Alpha	Cronbach's Alpha Based on Standardized Items	N of Items
Top management support	0.71	0.71	7
Degree of centralization	0.71	0.71	7
Organization culture	0.81	0.80	7
Competitive pressure	0.84	0.84	7
Application of HRIS	0.90	0.89	9

Source; (Survey Data, 2017)

4.7	Factor analysis for independent variables

This section discusses the factor analysis for Independent variable.

4.7.1	Kaiser- Meyer- Olkin Measure (KMO measure)

Sampling adequacy was tested using the Kaiser- Meyer- Olkin Measure (KMO measure) of sampling adequacy. As evidenced in table 4.9, KMO was greater than 0.5, and Bartlett’s Test was significant.

Table 4. 9 KMO

KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.	0.70
Bartlett's Test of Sphericity	Approx. Chi-Square	2731.70
Df	378
Sig.	0.00

Source; (Survey Data, 2017)

4.7.2	Total Variance Explained

The study variables were subjected to a factor analysis and four components with Eigen values ≥ 1 were extracted which cumulatively explained 51.64% of variance. The first factor, accounted for 23.42% of the total variance, the second factor 12.14%, third factor 9.08% and the fourth factor accounted for 7.02% of the total variance as shown in Table 4.10 below.

Table 4. 10 Total Variance Explained

Comp	Initial Eigenvalues	Extraction Sums of Squared Loadings	Rotation Sums of Squared Loadings
Total	% of Variance	Cum %	Total	% of Variance	Cum %	Total	% of Variance	Cum %
1	6.56	23.42	23.42	6.56	23.42	23.42	3.87	13.82	13.82
2	3.40	12.14	35.55	3.40	12.14	35.55	3.81	13.60	27.41
3	2.54	9.08	44.63	2.54	9.08	44.63	3.46	12.33	39.77
4	1.97	7.02	51.64	1.97	7.02	51.64	3.32	11.87	51.64
Extraction Method: Principal Component Analysis.

Source; (Survey Data, 2017)

4.7.3	Factor Analysis for Independent Variables

Factor analysis was conducted to make sure that the items belong to the same construct Wibowo (2008) as cited in (Wanjala et al., 2017). Factors with factor loadings of above 0.5 are excellent and should be retained for further data analysis. Not all items were retained for further analysis, one item was dropped for organization culture, degree of centralization and competitive pressure since they did not meet the requirement. All the other items met the criterion for communality and were retained.

Table 4. 11 Factor Analysis for Independent Variables

Organization culture (OC)	Competitive Pressure (CP)	Degree of Centralization(DC)	Top Management Support(TM)
TM1	0.62
TM2	0.70
TM3	0.51
TM4	0.72
TM5	0.70
TM6	0.70
TM7	0.53
DC1	0.71
DC2	0.74
DC3	0.73
DC4	0.57
DC5	0.81
DC6	0.69
OC2	0.68
OC3	0.76
OC4	0.73
OC5	0.81
OC6	0.73
OC7	0.71
CP2	0.68
CP3	0.59
CP4	0.68
CP5	0.69
CP6	0.69
CP7	0.75
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.

Source; (Survey Data, 2017)

4.8	Data Transformation and Test of Normality

Based on the results from factor analysis, transformation was done by scoring using means. According to Xuhua et al. (2011), data transformation is used to make data conform to the assumptions of the statistical methods. The assumptions of most parametric methods are; homoscedasticity, normality, multicollinearity and linearity. One type of data transformation that was done was calculating mean grade for the variables. The findings showed that the highest mean score was for CP (mean 4.50) and the lowest was from HRIS (mean=3.97).

Table 4. 12 Descriptive Statistics Mean Grades

N-166	Minimum	Maximum	Mean	Std. Deviation	Skewness	Kurtosis
HRIS	2.4	4.5	3.97	0.50	-1.59	2.50
TM	2	5	4.27	0.66	-1.53	2.54
DC	2.57	5	4.36	0.46	-1.46	2.8
OC	3	5	4.38	0.41	-1.13	1.97
CP	2.57	5	4.50	0.49	-1.65	3.12

Source; (Survey Data, 2017)

Key; HRIS –Human Resource Information System, TM –Top Management Support, DC- Degree of Centralization, OC – Organizational Culture, CP – Competitive Pressure.

The normality tests are supplementary to the graphical assessment of normality. Kolmogorov-Simonov test and Shapiro Wilk was used to test normality of the data. The test statistics are shown in table 4.13. In this study, the p-value is more than 0.05. Therefore the data does not violate the assumption of normality.

Table 4. 13 Test of Normality

Kolmogorov-Smirnova	Shapiro-Wilk
Statistic	Df	Sig.	Statistic	df	Sig.
TM	0.27	166	0.13	0.84	166	0.28
DC	0.15	166	0.10	0.87	166	0.12
OC	0.21	166	.20*	0.87	166	0.60
CP	0.18	166	0.20*	0.83	166	0.41
HRIS	0.22	166	0.07	0.82	166	0.12
* This is a lower bound of the true significance.
a Lilliefors Significance Correction

Source; (Survey Data, 2017)

Shapiro-Wilk was used because the sample size was <2 000 Table 4.13. P-values more than 0.05. Therefore the data does not violate the assumption of normality.

4.9 Correlation Analysis

Correlation analysis is conducted to study the level at which two variables move or diverge together from one case to the next, and to assess the significance of the connection. This analysis generates a correlation coefficient which explains the extent to which the two variables move together. The correlation coefficient is coded as “r”. The “r” value range is between 0 to ±1. The value of zero (0) indicating that there is no relationship between the two variables. The value of ±1 showing that there is a perfect linear relationship between the two variables. A positive value shows that the two variables move together in the same trend, and when the “r” is a negative value, it shows that the variables move in opposite direction or trend.

Table 4.14 illustrates Pearson correlation results of the study of dependent and independent variables to assess the association of the variables. Findings revealed that top management support was positively and significantly correlated with the use of human resource information system (r = 0.58, ρ<0.01). Further, degree of centralization was positively and significantly correlated with the use of human resource information system (r = 0.66, ρ<0.01). Likewise, organization culture was positively correlated with the use of human resource information system (r = 0.58, ρ<0.01). Also, competitive pressure was indicated to positively correlate with the use of human resource information system (r = 0.73, ρ<0.01). This implies that top management support, degree of centralization, organization culture and competitive pressure are expected to influence the use of human resource information system. From table 4.17, the VIF for all the estimated parameters was found to be less than 4 which indicated the absence of multicollinearity among the independent factors

Table 4. 14 Correlation Results

Key; HRIS –Human Resource Information System, TM –Top Management Support, DC- Degree of Centralization, OC – Organizational Culture, CP – Competitive Pressure.

HRIS	TM	DC	OC	CP
HRIS	1
TM	.58**	1
DC	.66**	.19**	1
OC	.58**	.15	.35**	1
CP	.73**	.45**	.12**	.48**	1
* Correlation is significant at the 0.05 level (2-tailed).
** Correlation is significant at the 0.01 level (2-tailed).

Source; (Survey Data, 2017)

4.10 Multiple Regression Analysis

4.10.1 Model Summary

Table 4.15 illustrates the model summary of multiple regression model, the results showed that all the four predictors (top management support, degree of centralization, organization culture and competitive pressure) explained 65.4 percent variation of HRIS (R squared =0.654). In this study both R squared and adjusted R squared are the same indicating the coefficient estimates and predictions are not biased.

Table 4. 15 Model Summary

Change Statistics
R	R Square	Adjusted R Square	Std. Error of the Estimate	R Square Change	F Change	df1	df2	Sig. F Change
.808a	0.654	0.645	0.29	0.654	75.96	4	161	0.000
a Predictors: (Constant), CP, TM, OC, DC
b Dependent Variable: HRIS

Key; HRIS –Human Resource Information System, TM –Top Management Support, DC- Degree of Centralization, OC – Organizational Culture, CP – Competitive Pressure.

Source; (Survey Data, 2017)

4.10.2 ANOVA Model

Analysis of Variance (ANOVA) consists of calculations that provide information about levels of variability within a regression model and form a basis for tests of significance Study findings in table 4.16 indicated that the above discussed coefficient of determination was significant as evidence of F ratio of 75.96 with p value 0.000 <0.05 (level of significance). Thus, the model was fit to predict the application of HRIS using top management support, degree of centralization, organization culture and competitive pressure.

Table 4. 16 ANOVA Model

Key; HRIS –Human Resource Information System, TM –Top Management Support, DC- Degree of Centralization, OC – Organizational Culture, CP – Competitive Pressure.

Sum of Squares	df	Mean Square	F	Sig.
Regression	26.43	4	6.61	75.96	.000b
Residual	14.00	161	0.09
Total	40.43	165
a Dependent Variable: HRIS
b Predictors: (Constant), CP, TM, OC, DC

Source; (Survey Data, 2017)

4.10.3	Hypothesis Testing

The rule of thumb was applied in the interpretation of the variance inflation factor. From table 4.17, the VIF for all the estimated parameters was found to be less than 4 which indicated the absence of multicollinearity among the independent factors (Hair, et al., 2010). This implied that the variation contributed by each of the independent factors was significant independently and all the factors were included in the prediction model.

Zero order correlation was used to test the inter-correlation between the variables. Findings indicated the correlation between the independent variables and dependent variables were above 0.5 indicating a strong relationship and also indication for significance prediction. The table also shows both unstandardized Coefficients and Standardized Coefficients. However, this study used standardized regression coefficients to interpret the results because they are based on changes in standard deviation units.

The first hypothesis of the study stated that top management support has no significant effect on the application of HRIS. However, research findings showed that top management support had coefficients of estimate which was significant basing on β1= -0.15 (p-value = 0.024which is less than α = 0.05) implying that we reject the null hypothesis stating that top management support has no significant effect on the application of HRIS. Consequently, an increase in top management support by 1 unit results to an increase in the application of HRIS by the 0.15. Furthermore, the effect of top management support was stated by the t-test value = 2.27 which implies that the standard error associated with the parameter is more than the effect of the parameter.

The second hypothesis of the study stated that the degree of centralization has no significant effect on the application of HRIS. Nonetheless, the findings showed that the degree of centralization had coefficients of estimate which was significant basing on β2 = 0.22 (p-value = 0.003 which is less than α = 0.05) hence we reject the null hypothesis stating that the degree of centralization has no significant effect on the application of HRIS. This implies that for each unit increase in the degree of centralization, there is up to 0.22 unit increase in the application of HRIS. Also, the effect of degree of centralization is shown by the t-test value of 3.04 which implies that the effect of degree of centralization surpasses that of the error.

The third hypothesis of the study stated that organization culture has no significant effect on application of HRIS. However, the findings showed that organization culture had coefficients of estimate which was significant basing on β3 = 0.23 (p-value = 0.00 which is less than α = 0.05) thus we conclude that organization culture has a significant effect on HRIS. This suggests that there is up to 0.23 unit increase in the application of HRIS for each unit increase in organization culture. The effect of organization is 4 times the effect attributed to the error, this is indicated by the t-test value = 4.00.

The fourth hypothesis of the study stated that competitive pressure has no significant effect on the application of HRIS. However, the findings showed that competitive pressure had coefficients of estimate which was significant basing on β4= 0.40 (p-value = 0.00 which is less than α = 0.05) thus we conclude that competitive pressure has a significant effect on the application of HRIS. This suggests that there is up to 0.40 unit increase in the application of HRIS for each unit increase in competitive pressure. The effect of competitive pressure is 6 times the effect attributed to the error, this is indicated by the t-test value = 6.0000.

Table 4. 17 Coefficients of Estimate

Key; HRIS –Human Resource Information System, TM –Top Management Support, DC- Degree of Centralization, OC – Organizational Culture, CP – Competitive Pressure.

Unstandardized Coefficients	Standardized Coefficients	Correlations	Collinearity Statistics
B	Std. Error	Beta	t	Sig.	Zero-order	Tolerance	VIF
(Constant)	-0.59	0.29	-2.02	0.05
TM	0.11	0.05	0.15	2.27	0.02	0.58	0.53	1.91
DC	0.23	0.08	0.22	3.04	0.00	0.66	0.43	2.34
OC	0.28	0.07	0.23	4.00	0.00	0.58	0.63	1.59
CP	0.41	0.07	0.40	6.00	0.00	0.73	0.48	2.10
a Dependent Variable: HRIS

Source; (Survey Data, 2017)

Table 4. 18 Summary of findings

Hypothesis	Beta (t-value)	Result
H01 Top management support has no significant effect on use of HRIS.	0.15 (p <0.05 )	Rejected
H02 Degree of centralization has no significant effect on use of HRIS.	0.22 ( p >0.05)	Rejected
H03 Organization culture has no significant effect on use of HRIS.	0.23 ( p >0.05)	Rejected
H04 Competitive pressure has no significant effect on use of HRIS.	0.40 (p <0 .05)	Rejected

CHAPTER FIVE

SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS

5.1	Introduction

This chapter presents the summary of findings, conclusion, recommendations and further research recommendations that are deemed important for the extension of the research.

5.2	Summary of the Findings

The first objective was to determine the effect of top management support on application of HRIS, the corresponding hypothesis was HO1Top management support has no significant effect on application of HRIS. Findings showed that top management support had a significant effect on HRIS. This indicates hypothesis 1 was rejected (β1 = 0.15, p-value = 0.02). Hence, increase in top management support will lead to an increase in the application of HRIS.

The second objective; to assess the impact of degree of centralization on application of HRIS corresponded with hypothesis two, HO2 Degree of centralization has no significant effect on application of HRIS. The degree of centralization was also found to have a positive and significant effect on the application of HRIS. This indicates that hypothesis 2 was rejected (β2= 0.22, p-value = 0.00 which was less than α = 0.05). Hence, increase in degree of centralization also leads to an increase in the application of HRIS.

To determine the effect of organization culture on application of HRIS was the third objective, corresponded by the third hypothesis H03 Organization culture has no significant effect on application of HRIS. The study established a positive and significant effect of organization culture on the application of HRIS. Thus, hypothesis 3 was rejected (β3 = 0.23, p-value = 0.00 which was less than α = 0.05). This infers that organization culture enhances the application of HRIS.

The fourth and last objective of the study was to investigate the effect of competitive pressure on application of HRIS, the corresponding hypothesis was HO4 Competitive pressure has no significant effect on application of HRIS which was rejected by the study. An indication that competitive pressure had a positive and significant effect on the application of HRIS (β4 = 0.40, p-value = 0.00 which was less than α = 0.05).

5.3	Discussion and Conclusion

5.3.1 Top Management Support and HRIS

The first objective was to determine the effect of top management support on application of HRIS, the corresponding hypothesis was H01 Top management support has no significant effect on application of HRIS. Findings showed that top management support had a significant effect on HRIS. This indicates hypothesis 1 was rejected (β1 = 0.15, p-value = 0.02). Hence, increase in top management support will lead to an increase in the application of HRIS. The results are in agreement with findings which suggest that strong commitment of the top management to an innovation leads to early adoption while their lack of commitment inhibits the use of the innovation (Mehrtens et al., 2001; Chan & Mills, 2002). To further corroborate this view, Ang et al. (2001) noted that top management support for IT applications is the highest predictor of IT usage. Moreover, Premkumar & Roberts, (1999) elucidated that top management support is essential for creating a supportive climate and providing adequate resources for the adoption and implementation of new technologies.

The study findings also corroborate that of Thong et al. (1996) which found out that the top management is better able to identify business opportunities for the exploitation of IT and provide appropriate strategic vision and direction for its adoption and usage. Similarly, Razali and Vrontis (2010) indicated that top management involvement is among the largest coefficients for the impact on the acceptance level of employees toward the new HRIS implemented. As well, Ngai and Wat (2006) indicated that support of top management was one of the most important factors in successful implementation of HRIS in Hong Kong. Generally, the extant literature suggests that top management support is key in the adoption and usage of HRIS which is in agreement with the study findings. In conclusion, the study established that the top management support has a positive impact on the usage of HRIS. The results corroborate that of prior literature indicating that top management support is critical in the adoption and usage of HRIS. It can be concluded therefore, that top management support encourages usage of HRIS through sufficient delegation of resources to support its implementation and use.

5.3.2 Degree of centralization and HRIS

The second objective; to assess the impact of degree of centralization on application of HRIS corresponded with hypothesis two, HO2 Degree of centralization has no significant effect on application of HRIS. The degree of centralization was also found to have a positive and significant effect on the application of HRIS. This indicates that hypothesis 2 was rejected (β2= 0.22, p-value = 0.00 which was less than α = 0.05). Hence, increase in degree of centralization also leads to an increase in the application of HRIS. In line with the study findings, Hage & Aiken, (1969) concluded that the degree of centralization in decision making plays a key role in IT usage. Particularly, decentralized decision-making facilitates customer-based information inter-organizational system (CIOS) usage (Grover, 2003). On the other hand, Duncan, and Holbek (1973) conclude that more formalized and centralized organizations have lower levels of innovativeness. In this regard, autonomy enhances innovation while standardization and centralization inhibits innovation.

Further support to the study findings is by Brown and Bostrom, (1994) who indicated that a highly centralized organizational design leads to more effective end user computing. Similarly, Pierce and Delbecq (1977) suggest centralization of decision-making may reduce conflict between organizational units and foster innovation usages. In support of this proposition, Ettlie et al. (1984) found that organizations with a centralized structure were more likely to use new technologies. However, Yang et al. (2007) postulated that high degree of centralization impacts negatively to the adoption and usage of technologies that are more compatible with the interests of lower level employees than those of higher level employees. In terms of the degree of centralization, the study found out that degree of centralization has a positive and significant effect on the application of HRIS. This is because there is job specialization which enhances innovation which in turn enhances the application of HRIS. The application of HRIS is further enhance by the availability of skilled HRIS professionals and consultations within the various departments. It can therefore be said that HRIS usage is evidenced in organizations that exhibit high degree of centralization.

5.3.3 Organizational Culture and HRIS

To determine the effect of organization culture on application of HRIS was the third objective, corresponded by the third hypothesis H03 Organization culture has no significant effect on application of HRIS. The study established a positive and significant effect of organization culture on the application of HRIS. Thus, hypothesis 3 was rejected (β3 = 0.23, p-value = 0.00 which was less than α = 0.05). This infers that organization culture enhances the application of HRIS. Cognate to the results, Zaltman et al. (1973) suggest that for innovation to occur and be successful, the organization culture in place should be in favor of technology adoption/usage.

Moreover, Baer &Frese, (2003) note that the organization climate or culture needs to embody norms and expectations that support openness, change, and risk-taking for technology adoption/usage. To further corroborate the above findings, Jones et al. (2005) suggest that organizational cultures with a more supportive climate and flexible structures might be more advantageous to successful deployment of new technologies. Additionally, the study has established that organizations with an open and flexible culture easily adopt the application of HRIS. This is because organizational member view technology change positively and are more willing to adapt to the changes. In this manner, the organization culture enhances innovation through flexible organization value. Such a culture also promotes cooperativeness and team work which further enhances the adoption of HRIS.

5.3.4 Competitive Pressure and HRIS

The fourth and last objective of the study was to investigate the effect of competitive pressure on application of HRIS, the corresponding hypothesis was HO4 Competitive pressure has no significant effect on application of HRIS which was rejected by the study. An indication that competitive pressure had a positive and significant effect on the application of HRIS (β4 = 0.40, p-value = 0.00 which was less than α = 0.05). Consistently, a number of studies have indicated competitive pressure as a powerful driver of IT usage, adoption and diffusion (Ruppel & Howard, 1998; Murphy & Southey, 2003; Rogers, 2003; Urbano & Yordanova, 2008).

However, Arad et al., (1997) indicated that competition is not a significant factor influencing the application of HRIS. This is because the top management perceive HRIS as more administrative than strategic hence would not view it as a strategy to deal with the competition in the external environment (Urbano & Yordanova, 2008). Lastly, the study found that competitive pressure increases the usage of HRIS. Specifically, with an increase in competitive pressure, organizations deem it more important to use HRIS to make informed decisions and get most out of its staff. Besides, for organizations to be competitive, the adoption and application of HRIS is key. It can therefore be said that the drive to be competitive results in the adoption and application of HRIS.

5.4	Recommendations

Basing on the study findings, a high degree of centralization enhances HRIS usage. There is therefore need for organizations to have job specialization, formalization and standardization in the organization. Also, it is important to have skilled HRIS professionals in the organization and consultations with other departmental staff regarding legislative, technical and cultural requirements specifications of HRIS implementation. Moreover, there is need to train all users to increase their knowledge and skills in using HRIS.

Since organization culture may hinder or enhance the application of HRIS, it is important for organizations to have a culture that enhances freedom, cooperativeness, team work, sociability and solidarity among employees at all levels to promote innovation and adoption of HRIS. Also, the organizations need to have an open and flexible corporate culture, supportive climate and flexible structures that enhance successful deployment of new technologies.

Finally, competitive pressure faced by organizations results in the adoption and application of HRIS. There is therefore need for organizations to focus on what is best for the organization in terms of managing human resources. Organizations should do what is right in terms of technology adoption despite of competition facing the organization. Most importantly, organizations need to adopt human resource information system to enhance a more efficient human resource system.

5.5	Further Research Recommendations

This study expands our knowledge on organizational factors affecting the use of Human Resource Information System (HRIS) among manufacturing firms. Though this study has fulfilled its aim and objectives, there are a number of areas for additional studies and empirical research, given the limitations of the research. Despite the in-depth coverage of this study and its findings, there still exists a gap that future research could explore. On a geographical dimension, this study was primarily limited to manufacturing firms in Nairobi County, Kenya. Therefore, it may not be appropriate to generalize to the whole population of manufacturing firms countrywide. For this reason, further empirical investigations in different Counties and countries are needed.

The methodology that has been chosen to achieve the research objectives was limited to questionnaires. As such, future research could build on this study by examining factors affecting the use of Human Resource Information System (HRIS) in both a qualitative and quantitative way.

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APPENDIX I:	LETTER OF INTRODUCTION

Chepchirchir Charity

Moi University

P.O Box 3900

Nairobi

Dear Respondents,

RE: MASTER OF BUSINESS MANAGEMENT RESEARCH PROPOSAL

I am a graduate student at Moi University Nairobi satellite campus, School of Business and economics wishing to conduct a research on “Organizational Factors and Human Resource Information System in Manufacturing Firms in Nairobi County, Kenya” In partial fulfillment of the requirements for the award of Masters’ Degree in Business Management.

For the purpose of completing my research, I require to collect information through the attached questionnaire. You have been selected to be part of the respondents for the study. Please find time and complete the questionnaire to the best of your knowledge.

Kindly note that the information requested is purely for academic purposes and that it shall be treated with utmost confidentiality.

Thank you for your co-operation.

Yours Faithfully,

Chepchirchir Charity.

APPENDIX II: QUESTIONNAIRE

SECTION ONE

In this section the study is interested in your view on use of human resource information system in your organization. Answer by indicating the appropriate category that best fits your opinion. The categories are:

1-Strongly disagree, 2- Disagree, 3 - Neutral, 4- Agree, 5- Strongly agree

S/No	Use of human resource information system (HRIS)	Rating
Items	1	2	3	4	5
1.	Our organization uses human resource information system in Personnel and salary administration
Our organization uses human resource information system in organization skill inventory
Our organization uses human resource information system in Medical history
Our organization uses human resource information system in Manpower planning
5.	Our organization uses human resource information system in career planning
6.	Our organization uses human resource information system in decision-making, and control
7	Our organization uses human resource information system in employee selection and placement
8	Our organization uses human resource information system in ingestion and training projections
9	Our organization uses human resource information system Career-pathing, equity monitoring, and productivity evaluation

SECTION TWO

Factors influencing the implementation and use of Human Resource Information System.

In this section the study is interested in your view on factors influencing the use of human resource information system in your organization. Answer by indicating the appropriate category that best fits your opinion. The categories are:

Strongly disagree 2. Disagree 3. Neutral 4. Agree 5. Strongly agree

S/No	Top management	Rating
Item	1	2	3	4	5
1.	The top management provides a conducive climate to support HRIS usage
The top management provides adequate resource to support HRIS usage
The top management facilitate technical training to support HRIS usage
The top management provides avenues for innovation to support HRIS usage
The top management portrays positive attitude and interest towards the adoption of human resource information system
Top management commitment to application of HRIS provide an enabling environment for employees engagement
Open management style to allow use of human resource information system.
S/No	Degree of Centralization	Rating
1	2	3	4	5
1.	Top management solely approves the implementation of HRIS projects
2.	Job Specialization is enhanced through organizational setting of departments
3.	Job formalization is enhanced through organization setting and structure
4.	Job standardization is enhanced through the organizational setting and structure
5.	There is availability of skilled HRIS professionals in the organization to facilitate implementation and application of HRIS
6.	consultation with other departmental staff does occur regarding legislative, technical, and cultural requirements specifications of HRIS implementation
7.	Training for all users’ levels such as operation and strategic level is done to increase knowledge and skills in using HRIS system effectively
S/No	Organization Culture	Rating
The organization culture enhances	1	2	3	4	5
1	Our organization culture enhances innovation through flexible organization value addition
2	Our organization culture enhances freedom that promotes innovation and adoption of HRIS
3	Our organization culture enhances cooperativeness that promotes innovation and adoption of HRIS
4	Out organization culture enhances teamwork that eventually promotes innovation and adoption of HRIS
5	Our organization culture has enabled sociability and solidarity among employees at all levels
6	Our organization have an open and flexible corporate cultures as a result employees at all levels tend to view changes positively and are more willing to adapt to the changes from HRIS adoption.
7	Our organizational cultures has a supportive climate and flexible structures that enhances successful deployment of new technologies
S/No	Competitive Pressure	Rating
1	2	3	4	5
1	Our organization Initiates technological changes
2	Our organization ignores speculation from technological changes
3	Our organization focuses on what's technologically best for the organization and not on competitors
4	Organization does what is right in terms of technological adoptions despite of competition facing the organization
5	Our organization adopts the human resource information system because of what competitors do
6	Our organization adopts the human resource information system to enhance a more efficient human resource system
7	The organization adopts the human resource information system to enhance information decision and allocation of HR activities

Any other comments …………………………………………………………………...

……………………………………………………………………………………………

…………………………………………………………………………………………..

Thank you for your co-operation in filling this questionnaire

Section Three: PERSONAL DATA

1.	What is your gender? (Tick one)

(a)	Male

(b)	Female

2.	Your highest level of Education (tick one)

(a) Tertiary/Technical Training

(b) Undergraduate Degree

(d) Graduate Degree

(e) Other

3.	How many employees does your company have? (Tick one)

(a) Above 400

(b) 301 to 400

(c) 201 to 300

(d) 10 to 200

4.	How old is your industry?

(a) Less than 5 years

(b) 6– 10 years

(c) 11– 15 years

(d) 16 – 20 years

(e) Over 20 years

5. Industry sector the organization belongs? ___________________________

APPENDIX V: RECOMMENDED SAMPLE SIZE FROM A GIVEN POPULATION

N	S	N	S	N	S	N	S	N	S
10	10	100	80	280	162	800	260	2800	338
15	14	110	86	290	165	850	265	3000	341
20	19	120	92	300	169	900	269	3500	246
25	24	130	97	320	175	950	274	4000	351
30	28	140	103	340	181	1000	278	4500	351
35	32	150	108	360	186	1100	285	5000	357
40	36	160	113	380	191	1200	291	6000	361
45	40	170	118	400	196	1300	297	7000	364
50	44	180	123	420	201	1400	302	8000	367
55	48	190	127	440	205	1500	306	9000	368
60	52	200	132	460	210	1600	310	10000	373
65	56	210	136	480	214	1700	313	15000	375
70	59	220	140	500	217	1800	317	20000	377
75	63	230	144	550	225	1900	320	30000	379
80	66	240	148	600	234	2000	322	40000	380
85	70	250	152	650	242	2200	327	50000	381
90	73	260	155	700	248	2400	331	75000	382
95	76	270	159	750	256	2600	335	100000	384
Key “N” Population Size “S” Sample Size Source; Krejcie and Morgan (1970)