THE STUDY OF THE ORGANIZATIONAL FACTORS AFFECTING ON EMPLOYEE PERFORMANCE; WITH SPECIAL REFERENCE TO CINNAMON HOTELS

IN SRI LANKA

By

Isuru Suriyabandara, BSC (Mgt), PGDip (DM), MBus (Finance)

August 2022

ABBREVIATIONS

HR - Human Resource

EP - Employee Performance

CONTENTS

Ceritificate ii

Declaration Form iii

Acknowledgement iv

Abbreviations iv

Chapter One – Introduction 1

1.1. Background Study 1

1.2. Problem Statement 1

1.3. Research Questions 2

1.4. Research Objectives 2

1.5. Significance Of The Study 2

Chapter Two – Literature Riview 3

2.1. Introduction 3

2.2. Theoretical And Empirical Review 3

Chapter Three – Research Methodology 7

3.1. Introduction 7

3.2. Research Design 7

3.2.1. Philosophy 7

3.2.2. Approaches 7

3.2.3. Strategy 8

3.2.4. Methodological Choice 8

3.2.5. Time Horizon 8

3.3. Technique And Procedures 9

3.4. Ethical Considerations 9

3.5. Conceptual Framework 10

3.5.1. Hypothesis Development 10

3.6. Conceptualization Of Variables 11

3.7. Population And Sample 13

3.8. Methods Of Data Collection 13

3.9. Methods Of Data Analysis 14

3.10. Limitations 14

Chapter Four - Data Presentation And Analysis 15

4.1. Demographic Analysis 15

4.2. Construct Validity 17

4.3. Spearman’s Correlation Coefficients For Multicollinearity Check 23

4.4. Test For Linearity And Equal Variance 25

4.5. Test For Co-Linearity 28

4.6. Nonparametric Test For Normality 29

4.7. Descriptive Statistics Test For Normality 30

Chapter Five – Conclusion And Implications 31

References 34

TABLE OF FIGURES

Figure 1: Proposed conceptual framework 10

Figure 2: Participants by Job Tittle 15

Figure 3: Participants by Education Level 16

Figure 4: Participants by Job Experience 16

Figure 5: Screen Plot of Components 21

Figure 6: Normal P-P plot of Standardized Residual 25

LIST OF TABLES

Table 1: Conceptualization of variables 11

Table 2: Descriptive Statistics 17

Table 3: Correlation Matrix 18

Table 4: KMO and Bartlett’s Test 19

Table 5: Communalities 19

Table 6: Total Variance Explained 20

Table 7: Component Matrix 22

Table 8: Rotated Component Matrix 22

Table 9: Reliability Statistics 23

Table 10: Item-Total Statistics 24

Table 11: Model Summary 25

Table 12: ANOVA Table 26

Table 13: Coefficients 27

Table 14: Coefficients Test for Co-linearity 28

Table 15: Hypothesis Test Summary 29

Table 16: One-Sample Chi-Square Test Summary 29

Table 17: Descriptive Statistics Test for Normality 30

CHAPTER ONE – INTRODUCTION

BACKGROUND STUDY

Firms have realized that in order to thrive in a constantly changing market environment, they must develop distinct dynamic characteristics that enhance their competitive advantages. As a result, they emphasize the use of human resources (HR), specifically employee performance (EP), as a source of strategic advantage (Wright & Snell, Financial performance and the long-term link with HR practices, work climate and job stress, 2005). Performance appraisal, according to Narcisse and Harcourt (2008), intrudes on "one of the most emotionally charged activities in business life," the evaluation of a man's contribution and ability (Narcisse & Harcourt, 2008). Furthermore, Boxall and Purcell (2011) assert that implementing a well-defined process for evaluating EP is critical to a firm's smooth operation (Boxall & Purcell, 2011).

Furthermore, the primary challenge for businesses is to evaluate EP and consider how it can become more efficient and “valid.” In other words, how can firms use performance evaluation practices to improve their ability to distinguish “good” employees who exhibit desirable performance from bad ones (Rynes, Barber, & Varma, 2000).

PROBLEM STATEMENT

Today, the phenomenon of growing competition between businesses and their need to effectively respond to rapidly changing operational conditions and human requirements has increased the need to comprehend the factors that influence employee performance (EP). The Cinnamon Hotels chain has thirteen hotels and restaurants in Sri Lanka and employs over 800 people, making it a leader in the Sri Lankan hotel industry. The productivity of the firm is primarily depending on the employee performance. Employee performances are depending upon various organizational factors but there has been no study conducted to evaluate the relationship of organizational factors and EP of Cinnamon Hotels in Sri Lanka. This study examines the interrelations between various organizational factors and their impact on EP in Cinnamon Hotels chain in Sri Lanka.

RESEARCH QUESTIONS

The research question is;

1. What are the organizational factors affecting employee performance of Cinnamon Hotels chain in Sri Lanka?

2. How the organizational factors are affecting on employee performance in Cinnamon Hotels in Sri Lanka?

RESEARCH OBJECTIVES

The research objectives are as follows;

1. To identify the organizational factors affecting employee performance of Cinnamon Hotels chain in Sri Lanka.

2. To study the impact of organizational factors on employee performance in Cinnamon Hotels in Sri Lanka.

SIGNIFICANCE OF THE STUDY

This study aims to identify the organizational aspects that influence employee performance in Sri Lanka's Cinnamon Hotels. In addition, the findings of this study will aid in the improvement of employee performance by identifying the organizational elements that can be controlled that influence employee performance. In addition, the findings will improve both the quality of services and the financial performance of Cinnamon Hotels.

CHAPTER TWO – LITERATURE RIVIEW

INTRODUCTION

In this study, the researcher studies the impact of ownership structure on the financial performance of commercial banks using two theories: agency theory and public choice theory. Additionally, an empirical review was conducted to support the study by referring to previous studies, articles, and publications.

THEORETICAL AND EMPIRICAL REVIEW

Mathis and Jackson (2011) as well as Armstrong (2012) argue that firm-related factors can come from either the firm's internal or external environment. They have determined that management support, training culture, and organizational climate are the factors that are related to the environment of the company, while communication, autonomy, and environment are the factors that are related to the work environment. Employee-related factors that have been identified include things like intrinsic motivation, proactivity, adaptability, skill flexibility, commitment, and skill level. Other factors include factors like skill level (Mathis & Jackson, 2011; Armstrong, 2006).

Even though many firm- and environment-related characteristics, such as leadership, organizational trust, human capital investments, etc., have been studied in the literature in terms of their impact on employee performance (EP), very little is known about the relationship between these factors and EP. This is despite the fact that the literature has examined these characteristics in terms of their impact on EP. Within the scope of their research, Bapna and colleagues (2013) investigate managerial support, training culture, organizational climate, and environmental dynamic (Bapna, Langer, Mehra, Gopal, & Gupta A, 2013).

According to the findings of a large number of studies, management support is an essential component for EP development (Pulakos, 2004; Amstrong, 2012). As Morrison and Phelps (1999) further demonstrate, improved job performance is likely when employees perceive management support for their efforts connected to their jobs. This is because employees are more motivated when they believe their efforts will be recognized by management (Morrison & Phelps, 1999). In addition, Parker et al. (2006) uncovered a positive correlation between management support, employee dedication, and employee initiative (employee-related factors) (Parker, Williams, & Turner, 2006).

Another study found that organizational atmosphere influences employee attitudes and behaviors, and consequently, their levels of performance (Lepak, Liao, Chung, & Harden, 2006), while some studies report a link between organizational climate and adaptability (Chatman, Caldwell, O’Reilly, & Doerr, 2014) and another scholar claims that it also influences employees' proactivity level (Erkutlu, 2012). Lastly, Boxall et al. (2007) state that the culture of an organization has an effect on employee behavior, while Roos and Van Eeden (2008) state that the culture of an organization is related to the levels of motivation that employees have (Boxall, Purcell, & Wright, 2007; Roos & Van Eeden, 2008).

Some experts think that the expansion of one's knowledge and skill sets as a result of training contributes to an increase in EP (Dermol & Cater, 2013). In addition, training enhances employees' knowledge and abilities, allowing them to successfully face new everyday job-related obstacles, hence enhancing their job performance (Amstrong, 2012; Hale, 2002). In addition, training culture is associated with job autonomy (Song, Martens, McCharen, & Ausburn, 2011), and According to Winterton (2008), the training policies of organizations are inextricably linked to the enhancement of the job-related abilities and adaptability of their employees (employee-related factors) (Winterton, 2008).

Although numerous job-related elements, such as organizational fairness and job control (Kooij, et al., 2013), have been studied in the literature for their impact on employee performance (EP), this study focuses on job communication, job autonomy, and job environment. In this regard, the researcher has adopted these factors since the literature provides strong evidence that they are related to the other aspects included in the conceptual framework.

The autonomy of a job is the extent to which "the employment allows the person to make decisions regarding how to perform his obligations." In addition, they claim that job autonomy has a positive correlation with EP. Specifically, job autonomy refers to the amount of discretion and independence employees have in determining how to carry out their duties (Noe, Hollenbeck, Gerhart, & Wright, 2006). Therefore, persons with higher job autonomy have better flexibility at work since they may choose how to carry out their responsibilities more effectively, resulting in improved performance. (Morgenson, Delaney-Klinger, & Hemingway, 2005).

Additionally, several researchers have discovered that professional autonomy is favorably associated with commitment and proactivity (Parker, Williams, & Turner, Modeling the antecedents of proactive behavior at work, 2006). In addition, there is a relationship between job autonomy and employee performance that is regulated by intrinsic motivation (Dysvik & Kuvaas, 2011) (employee-related factors).

The work environment influences employee performance and productivity (Kopelman, Brief, & Guzzo, 1990). Moreover, the work environment influences employees' proactivity and productivity (Fawcett, Brau, Rhoads, Whitlark, & Fawcett, 2008). Similarly, Van Veldhoven (2005) confirms the relationships between job environment and EP (Van Veldhoven, 2005).

Similarly, job communication is related to commitment and motivation (Price, 1997), as well as commitment (employee-related factor) and employee performance (Chen, Silverthorne, & Hung, 2006). Moreover, job communication is a crucial aspect that can lead to increased levels of company performance (Bush & Frohman, 1991). In addition, communication on the job is a critical aspect that influences the overall performance of employees (Amstrong, 2012).

Employing individuals with a diversity of skills is a tremendous asset for a company, as it allows for the creation of many options to present or future work requirements. The skill flexibility of employees can be defined as the number of varied ways they can apply their skills in the workplace and the speed with which employees with diverse skills can be relocated to the appropriate positions (Wright & Snell, 1998).

Various practices, such as job rotation and cross-functional teams, may allow a company to increase the skill versatility of its personnel, according to some scholars. These methods generate skill combinations that are unique, exploitable for the company, and difficult for competitors to replicate. Therefore, state that skill flexibility has the strongest direct and most visible effect on EP, i.e. the higher the level of HR skill flexibility, the greater the likelihood that employees will demonstrate superior performance.(Bhattacharya, Gibson, & Doty, 2005). In addition to skill flexibility, skill level has a direct correlation with EP (Noe, Hollenbeck, Gerhart, & Wright, 2006; Boxall & Purcell, 2011). Moreover, employees' intrinsic motivation is also tied to EP, and for enterprises to improve their performance through greater EP, they must increase employee motivation (Delaney & Huselid, 1996; Boxall & Purcell, 2011).

According to the findings of researchers, proactivity "has not evolved as an integrated research stream in organizational behavior literature." This body of research is not governed by a single concept, theory, or measure (Crant, 2000). Also, proactivity is defined as "acting in anticipation of future (job-related) issues, needs, or changes," while some academics define it as influencing a situation by causing something to occur rather than reacting after the fact. Also, proactivity is defined as "acting in anticipation of future (job-related) issues, needs, or changes," while some academics define it as influencing a situation by pushing something to occur as opposed to reacting to it after it happened (Parker & Collins, 2010).

According to another researcher, proactive personnel are more productive than those with poor proactivity (Thompson, 2005). Individuals with high proactivity have been observed to take the initiative, communicate their thoughts, avert future problems in the workplace, improve their work performance, and positively influence their coworkers.(Parker & Collins, 2010).

Additionally, adaptability is a major factor determining EP. There may be a positive impact on employee performance if they can swiftly adapt to a new workplace (and/or new job requirements and needs) and unanticipated situations. In other words, people who have no problem adjusting to varied job needs and settings may be more productive than those who find it difficult to apply new knowledge, skills, and techniques to their tasks and who do not properly handle any job-related changes (Griffin, Neal, & Parker, 2007; Pulakos, Schmitt, Dorsey, Hedge, & Borman, 2002).

CHAPTER THREE – RESEARCH METHODOLOGY

Introduction

This chapter provides an in-depth discussion on the research design process as well as the methodological choices that were made for the study. For the most part, the philosophical attitude and the research problem have served as the primary guides for the choosing of research methods.. Furthermore, it explains why positivism and deductive reasoning are deemed appropriate for research. Additionally, the chapter established procedures for data collection, analysis, and reporting. It followed distinct procedures for the quantitative and qualitative approaches, as each serves a distinct purpose. In addition, the strategies that were implemented to strengthen the validity and reliability of the study are dissected in extensive depth. In the final section of the chapter, an examination of the methodology of the research is provided. Topics covered include the scheduling, weighting, and integration decisions pertaining to the study, in addition to an emphasis on ethical aspects.

Research Design

Philosophy

The researcher intends to err on the side of positivism in this research. The hypothetico-deductive method is used in positivism to verify a priori hypotheses, which are frequently stated quantitatively, and from which functional relationships between causal and explanatory factors and outcomes can be deduced. a priori hypotheses are frequently stated quantitatively. a priori hypotheses are frequently verified (Saunders, et al., 2019).

Approaches

This research will employ deductive reasoning. When a conclusion is derived logically from a set of theory-derived premises, the conclusion is true if and only if all of the premises are true (Ketokivi & Mantere, 2010). The researcher begins with established theories and concepts on factors affecting employee performance and then generalizes from broad to specific. The data collection process is used to test propositions or hypotheses about existing phenomena and theories developed in previous research.

Strategy

The current research will employ a survey. (Saunders et al., 2019) noted that the The survey strategy gives you the ability to collect data, which you can then quantitatively analyze by making use of descriptive and inferential statistics. In addition, survey data can be used to create models of the interactions between variables and provide probable explanations for specific correlations that have been found between different variables. When probability sampling is used, it is possible to create findings that are statistically representative of the entire community at a lower cost than collecting data from the entire population. This control over the research procedure should be provided to you by using a survey technique, which should also provide you with more control over the research procedure itself.

Methodological Choice

The first decision that the researcher needs to make about methodology is whether to carry out a quantitative, qualitative, or mixed techniques study. For the research design to be coherent, each of these options will almost definitely call for a different constellation of constituents (Saunders, et al., 2019). In addition, Saunders et al. (2019) noted that one way to differentiate quantitative research from qualitative research is to differentiate between numeric data (numbers) and non-numeric data. This distinction may be made by looking at the relationship between the two types of data (words, images, audio recordings, video clips and other similar material).The researcher analyzes ratio data (numerical figures) in this study; thus, the research is classified as quantitative. Additionally, the researcher employed a positivist and deductive philosophy and strategy consistent with quantitative research design techniques (Bryman, 1988; Saunders, et al., 2019; Walsh, et al., 2015).

Time Horizon

Time horizons are classified into two types: cross sectional and longitudinal. Cross-sectional time horizons will be used in the current research thus, depending on the time period covered by the research, the ‘snapshot’ perspective is referred to as cross-sectional, whereas the ‘diary’ perspective is referred to as longitudinal (Saunders, et al., 2019).

Technique and Procedures

In terms of techniques and procedures, the researcher will rely on secondary data because it is more efficient and less expensive. Surveys and reviews of relevant documents are used to collect the data. The secondary data from surveys will have been collected utilizing one of the following three separate survey strategies: census, continuous or regular survey, or ad hoc survey (Saunders, et al., 2019). In this study, the researcher refers to survey data, which are actually census data that were published by the Sri Lankan Central Bank.

Secondary data are described in this text as data that, in contrast to spoken words, persist physically (including digitally) as proof. This enables data to be transposed over time and place and reanalyzed for a purpose other than the one for which they were acquired (Lee, 2012). For the purpose of collecting documented data, the researcher looks at the financial reports of individual banks as well as the reports of financial rating firms.

Ethical Considerations

The research is governed by the ethical considerations. Therefore, the data was collected covertly to avoid participants being in a risk of embarrassment, pain, harm or any other material disadvantage.

Conceptual Framework

Figure 1 illustrates the links between the three essential components (firm/environment-related factors, job-related factors, and employee-related factors) and EP. These linkages are shown to have an impact on employee performance.

Figure 1: Proposed conceptual framework

Source: Developed by the Author (2022)

Hypothesis Development

The following hypotheses are presented based on the foregoing;

H₁ - Management support has a positive relationship with EP.

H₂ - Training culture has a positive relationship with EP.

H₃ - Organizational climate has a positive relationship with EP.

H₄ - proactivity has a positive relationship with EP.

H₅ - Adaptability has a positive relationship with EP.

H₆ - Intrinsic motivation has a positive relationship with EP.

H₇ - Skill flexibility has a positive relationship with EP.

H₈ - Job environment has a positive relationship with EP.

H₉ - Job communication has a positive relationship with EP.

Conceptualization of Variables

Table 1: Conceptualization of variables

Variable	Definition		Reference
Firm’s environment-related factors
Management support	The degree to which an individual is supported by management in his or her efforts to improve work performance.
Training culture	The extent to which a business recognizes the value of employee training as a factor that contributes favorably to employee performance.
Organizational climate	Specifically, how an employee evaluates the quality of their working connections with both their managers and their fellow employees.
Job related factors
Job environment
Managerial Affirmation	The degree to which an employee perceives that he is an asset to the organization as a result of the acts of his direct supervisor.
Intrinsic Affirmation	The amount to which an employee feels that he or she can make a good and distinctive contribution to the organization through the work that they do can be attributed to the job design.
Personal Belonging	The degree to which employees' social requirements are met by the work environment in their place of employment.
Co-worker Belonging	The degree to which a worker is socially connected with his or her fellow employees.
Personal Competence	The degree to which a worker believes that his or her abilities contribute to great performance on the workplace.
Job communication
Job communication	The amount of feedback that a supervisor delivers to an employee regarding the employee's performance.
Job-related communication	The degree to which a supervisor keeps employees informed about the numerous changes that are taking place in the working environment and in the workplace.
Responsiveness	The willingness of the supervisor to both listen to and answer to the requests and enquiries made by the employees.
Employee-related factors
Proactivity
Taking charge	The efforts made by workers to voluntarily and helpfully improve working operations.
Voice	Even in the face of opposition from others, being inventive in one's suggestions for change and one's recommendations for alterations to established methods.
Innovation	conceiving of and putting into practice innovative ideas or tactics in one's place of employment.
Problem Prevention	actions taken independently and proactively with the goal of preventing a recurrence of problems in the workplace.
Environmental Scanning	Conduct a thorough investigation of the ecosystem surrounding the company in order to discover strategies to guarantee compatibility with the ecosystem.
Issue selling Credibility	The degree to which a person has a history of effective problem sales in their past positions.
Issue selling Willingness	The amount of time, energy, and effort that an employee is willing to commit to make sure that important organizational decision makers are aware of the concerns.
Feedback inquiry	Having a supervisor provide one with direct feedback regarding the quality of work performance that was requested.
Feedback Monitoring	Providing comments based on the information gleaned from actively monitoring the environment of the workplace.
Job change Negotiation	Efforts made to adjust one's profession so that it is a better fit for the individual's set of talents and experience.
Career initiative	Rather of merely reacting passively to the circumstances of one's current job, an individual takes active steps to develop his or her career.
Adaptability
Handling emergencies or crisis situations	In a scenario in which there is a risk to one's life, a dangerous circumstance, or an emergency, acting with the right and proper urgency.
Handling work Stress	Keeping one's cool and composure in the face of trying circumstances or a heavy workload, as well as acting as a sedating and calming influence to whom others seek for direction, are both essential skills.
Solving problems creatively	the process of coming up with original answers to a work-related problem that is unusual, difficult, and unclear.
Dealing with uncertain and unpredictable work situations	Deals with work-related events and situations in an effective and efficient manner, applying the proper solution to deal with the unplanned or unanticipated aspect of the job.
Learning work tasks, technologies, and procedures	The ability to swiftly and effectively adapt to new work processes and procedures, as well as the learning of new methods for accomplishing activities that had not been previously learned.
Interpersonal adaptability	Being open to hearing and thinking about the perspectives and ideas of others, as well as being willing to adjust one's own thinking when appropriate.
Cultural adaptability	Willingly making adjustments to one's appearance or behavior while on the work in order to comply with or demonstrate respect for the norms, values, and customs of others.
Physically oriented adaptability	adjusting one's work habits to accommodate challenging environments, such as those characterized by high temperatures, high humidity, or excessive filthiness.
Intrinsic motivation	The degree to which an employee fulfills their responsibilities in order to acquire the greatest amount of personal happiness possible.
Skill level	The degree to which an organization believes that more frequent skill evaluations have a beneficial effect on the performance of its workforce.
Employee performance	The degree to which the level of productivity achieved by an employee satisfies the requirements set out by the company for performance.

Source: Developed by the Author (2022)

Population and Sample

Twelve Cinnamon hotels can be found in Sri Lanka: Cinnamon Bentota Beach, Cinnamon Bey Beruwala, Cinnamon Citadel Kandy, Cinnamon Grand Colombo, Cinnamon Lakeside Colombo, Cinnamon Lodge Habarana, Cinnamon Red Colombo, Cinnamon Wild Yala, Habarana Village by Cinnamon, Hikka Tranz by Cinnamon, and Trinco Blu by Cinnamon. A total of 29 randomly chosen HR executives and managers agreed to take part in the study after being persuaded by the researchers. In spite of this, only 8 managers (a response rate of 27.58 percent) and 240 employees filled out and submitted the surveys, with 8 managers and 196 people completing them adequately (valid sample).

Methods of Data Collection

Two different types of structured questionnaires were developed and used in the process of data collecting. These questionnaires were designed with the primary intention of measuring the three primary constructs (a total of thirteen subfactors) that were included in the proposed study model. The HR managers and executives, in addition to the employees, received their copies, in that order (in every hotel in Cinnamon Hotels chain).

The employee questionnaire was broken up into two sections: the first addressed the respondent's general characteristics and job position, and the second was comprised of questions that measured the elements that affected EP.

In a similar vein, questions pertaining to the company were included in the survey that was sent out to HR managers. The response of managers was given to this final aspect since managers have the greatest information about the environment in which a company operates (Sutcliffe and Huber, 1998). A total of 33 items were rated using a Likert scale that ranged from 1 (absolutely disagree) to 5 (completely agree), with 1 being the most disagreement and 5 representing the most agreement (totally agree). The constructions of the questionnaire, their operational definitions, the number of items used to assess each construct, and the related literature are all listed in Table I.

Methods of Data Analysis

The questionnaire contains both categorical and numerical data. All data for quantitative analysis were be recorded using numerical codes enabling to enter the data quickly and with fewer errors. Nevertheless, entered data were validated and filtered to identify missing values. Three stages of analysis were conducted on the data. Exploratory Data Analysis (EDA) was used in the initial phase for graphical data presentation and comprehension (Tukey, 1977). According to Kosslyn (2006), data exploration commenced by examining individual variables and their constituents (Kosslyn, 2006). The descriptive statistics then allow the researcher to numerically describe (and compare) the data values of a variable. Statistics describing a variable concentrate on two aspects of the distribution of data values: the central tendency and the dispersion (Saunders, Lewis, & Thornhill, 2019). Finally, the researcher examined differences and trends in relationships using both parametric and nonparametric statistical methods. In contrast, parametric statistics are used with numerical (interval and ratio) data to test the statical independence of variables and normality in order to avoid producing false results (Blumberg, Cooper, & Schindler, 2014). In addition, statistical significance is evaluated utilizing both non-parametric and parametric statistics to determine the independence or statistically significant association of the variables and to investigate the trends.

Limitations

It is essential to determine the constraints imposed by the study methodology that was selected. It establishes the validity of the scientific effort and lends a level of credibility to the conclusions that have been published, both of which are helpful in gaining an understanding of the findings of the research (Umanalio, M.C.B; Hamid, I; Hamiru, H; Assagaf, S.S.F; Bula, M; Nawawi, M; Pulhehe, S; Yusuf, S; Bon, A T;, 2019).The study’s primary limitation is that this study has not considered non-organizational factors that could affect the EP such as personal and domestic factors and inflation. Also, the researcher has not privileged to access sensitive corporate information which directly affect on ascertaining EP.

CHAPTER FOUR - DATA PRESENTATION AND ANALYSIS

Demographic analysis

Figure 2: Participants by Job Tittle

Source: Developed by the Author (2022)

Out of 300 participants, 88% were front-line employees and 14% were administrative employees where 6% of them were middle level managers and senior managers. Only 4% were graduates or undergraduates and the majority 68% were possessing none of the qualification and 24% were holding certificate level qualifications. 84% of the participants were new commers and rest 16% were having past job experience prior joining the Cinnamon Hotels.

Figure 3: Participants by Education Level

Source: Developed by the Author (2022)

The majority 68% of the participants were possessing certificate level education qualification while 24% of participants were not having any higher education qualification. Only 4% were possessing post graduate or graduate qualifications.

Figure 4: Participants by Job Experience

Source: Developed by the Author (2022)

16% of the participants were having previous job experience but the majority 84% of the participants were newcomers.

Construct Validity

A confirmatory factor analysis was carried out in order to determine whether or not the suggested research model accurately represents the relevant constructs. According to Hair et al. (1998), the Kaiser–Meyer–Oklin (KMO) measure of sampling adequacy as well as Bartlett's test of sphericity are recommended for measuring construct validity. On the other hand, Straub et al. (2004) point out that Cronbach's reliability test can be used to assess the internal consistency of measurements. A second application of the total variance explained (TVE) score is to determine the proportion of the overall variance that can be attributed to each and every element. In the end, the structural equation modeling (SEM) methodology was used to evaluate how well the total model fit the data.

Table 2: Descriptive Statistics

Descriptive Statistics
	Mean	Std. Deviation	Analysis N
Management_Support	2.43	.817	300
Training_Culture	2.44	1.063	300
Organizational_Climate	2.40	.788	300
Productivity	2.36	.630	300
Adaptability	2.09	1.041	300
Intrinsic_motivation	2.12	1.042	300
Skill_Flexibnility	2.31	1.134	300
Job_Environment	2.10	1.010	300
Job_Communication	2.42	.730	300
Employee_Performances	2.42	.748	300

Source: Developed by the Author using SPSS (2022)

The first of these was the table of descriptive statistics, which was checked for clarity, then the intended variables were input, and finally the number of cases was confirmed to be accurate. When performing factor analysis in the Correlation Matrix, correlations with a high value are most advantageous. Therefore, this table serves as a filter for the data. As a rule of thumb, correlations greater than 0.30 are recommended (the more of them and the higher they are, the better). We determined that there were sufficient moderate to high coefficients (>.30) in the correlation matrix to continue with the analysis.

Table 3: Correlation Matrix

Correlation Matrix
		Management_Support	Training_Culture	Organizational_Climate	Productivity	Adaptability	Intrinsic_motivation	Skill_Flexibnility	Job_Environment	Job_Communication	Employee_Performances
Correlation	Management_Support	1.000	.544	.272	.342	.298	.231	.133	.332	.146	.175
	Training_Culture	.544	1.000	.264	.346	.283	.150	.037	.367	.089	-.019
	Organizational_Climate	.272	.264	1.000	.354	.402	.379	.131	.374	.009	.174
	Productivity	.342	.346	.354	1.000	.580	.583	.074	.569	.078	.133
	Adaptability	.298	.283	.402	.580	1.000	.589	.170	.479	.084	.039
	Intrinsic_motivation	.231	.150	.379	.583	.589	1.000	.094	.507	.080	.151
	Skill_Flexibnility	.133	.037	.131	.074	.170	.094	1.000	.116	.577	.472
	Job_Environment	.332	.367	.374	.569	.479	.507	.116	1.000	.106	.023
	Job_Communication	.146	.089	.009	.078	.084	.080	.577	.106	1.000	.406
	Employee_Performances	.175	-.019	.174	.133	.039	.151	.472	.023	.406	1.000
Sig. (1-tailed)	Management_Support		.000	.000	.000	.000	.000	.011	.000	.006	.001
	Training_Culture	.000		.000	.000	.000	.005	.261	.000	.063	.373
	Organizational_Climate	.000	.000		.000	.000	.000	.011	.000	.435	.001
	Productivity	.000	.000	.000		.000	.000	.100	.000	.089	.011
	Adaptability	.000	.000	.000	.000		.000	.002	.000	.074	.252
	Intrinsic_motivation	.000	.005	.000	.000	.000		.052	.000	.084	.004
	Skill_Flexibnility	.011	.261	.011	.100	.002	.052		.022	.000	.000
	Job_Environment	.000	.000	.000	.000	.000	.000	.022		.034	.343
	Job_Communication	.006	.063	.435	.089	.074	.084	.000	.034		.000
	Employee_Performances	.001	.373	.001	.011	.252	.004	.000	.343	.000

Source: Developed by the Author using SPSS (2022)

Table 4: KMO and Bartlett’s Test

KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.770
Bartlett’s Test of Sphericity	Approx. Chi-Square	953.268
	df	45
	Sig.	.000

Source: Developed by the Author using SPSS (2022)

KMO reported a value of 0.770 for the Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Any result larger than 0.6 indicates that the data are appropriate for factor analysis. Bartlett’s Test of Sphericity yielded a chi-square of 953.268 at df = 45 and a significance threshold of 0.000, with a p-value of 0.000. This is also a great outcome, and we are more certain that our final factor analysis will produce relevant data.

Table 5: Communalities

Communalities
	Initial	Extraction
Management_Support	1.000	.725
Training_Culture	1.000	.801
Organizational_Climate	1.000	.383
Productivity	1.000	.671
Adaptability	1.000	.656
Intrinsic_motivation	1.000	.732
Skill_Flexibnility	1.000	.722
Job_Environment	1.000	.589
Job_Communication	1.000	.670
Employee_Performances	1.000	.590
Extraction Method: Principal Component Analysis.

Source: Developed by the Author using SPSS (2022)

The fraction of the original variable’s variability that is accounted for by the high-loading elements is explained by communalities. To bring this definition to life, consider the value 0.725 in the first row of the Communalities table’s Extraction column. 72.5 percent (0.725 100) of the variance in “Management Support” can be explained by the high-loading (eigenvalues >1) components (factors). Similarly, additional criteria were understood.

Table 6: Total Variance Explained

Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	3.522	35.218	35.218	3.522	35.218	35.218	2.926	29.258	29.258
2	1.866	18.656	53.874	1.866	18.656	53.874	1.993	19.933	49.191
3	1.152	11.521	65.395	1.152	11.521	65.395	1.620	16.203	65.395
4	.783	7.835	73.229
5	.636	6.363	79.592
6	.526	5.257	84.849
7	.433	4.327	89.176
8	.417	4.172	93.348
9	.355	3.552	96.900
10	.310	3.100	100.000
Extraction Method: Principal Component Analysis.

Source: Developed by the Author using SPSS (2022)

In the table headed Total Variance Explained, the eigenvalues for the ten additional components are displayed. Examine the Initial Eigenvalues column and note the value of 3.522 for Component 1.This eigenvalue (3.522) is equivalent to 35.22 % (3.522/10 × 100) of the total variance when all 10 variables are considered. Component 2’s eigenvalue is 1.866, indicating it accounts for 18.66% of the total variation across all variables. This percentage is unrelated to the variance of the first component; hence, the first three components (35.22+18.66+11.52) account for 65.4% of the variance of all variables (see the Cumulative percent column).

Figure 5: Screen Plot of Components

Source: Developed by the Author using SPSS (2022)

The eigenvalues are plotted on the y-axis and the 10 components are plotted on the x-axis. When evaluating the factor analysis, the scree plot is a generally regarded tool for identifying the proper number of components (factors). As seen in the explanation of total variance, Components 1, 2, and 3 account for 65.4% of the variance in all variables. The scree plot gives supporting evidence for a two-component solution to our factor analysis problem.

The table in the Component Matrix displays the factor-loading values for components with eigenvalues greater than 1.0. This matrix displays loading levels preceding rotation. This table’s values are interpreted similarly to any correlation coefficient. This means that zero indicates no loading, whereas negative numbers suggest that when the score of the variable grows, the score of the component falls. The coefficients of productivity (0.780), adaptability (0.752), and intrinsic motivation (0.720) indicate that these three variables have a substantial impact on component 1. Similar to job communication (0.766) and employee performance (0.707), component 2 receives high marks.

Table 7: Component Matrix

Component Matrixa
	Component
	1	2	3
Management_Support	.594	.010	.610
Training_Culture	.546	-.166	.690
Organizational_Climate	.606	-.087	-.091
Productivity	.780	-.196	-.156
Adaptability	.752	-.171	-.249
Intrinsic_motivation	.720	-.147	-.437
Skill_Flexibnility	.323	.785	-.049
Job_Environment	.740	-.196	-.051
Job_Communication	.279	.766	.072
Employee_Performances	.287	.707	-.090
Extraction Method: Principal Component Analysis.
a. 3 components extracted.

Source: Developed by the Author using SPSS (2022)

Table 8: Rotated Component Matrix

Rotated Component Matrixa
	Component
	1	2	3
Management_Support	.230	.151	.806
Training_Culture	.200	-.034	.872
Organizational_Climate	.581	.090	.194
Productivity	.787	.036	.223
Adaptability	.798	.056	.125
Intrinsic_motivation	.850	.077	-.058
Skill_Flexibnility	.088	.845	.027
Job_Environment	.706	.021	.299
Job_Communication	.001	.810	.117
Employee_Performances	.096	.762	-.018
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 4 iterations.

Source: Developed by the Author using SPSS (2022)

Rotated Component Matrix was generated when the researcher requested Varimax rotation as one of the final steps in configuring the analysis. Rotation using the Varimax method is one of the most used orthogonal procedures. Orthogonal implies that the components are assumed to be uncorrelated. Whether or not this assumption is reasonable is a separate question, yet it is frequently made by people employing the factor-analytic method. The other primary sort of rotation (oblique) is typically employed when you have prior knowledge that the components are possibly connected. The table demonstrates that the unrotated Component Matrix could be improved with respect to aspects such as productivity, flexibility, intrinsic motivation, and work environment.

Spearman’s Correlation Coefficients for Multicollinearity Check

Discriminatory validity was tested with Pearson Correlation values from correlations table. Pearson correlation value should be more than 0.3 for the acceptance of discriminatory validity. According to the table, none of Pearson correlation values exceeds 0.3 therefore, variables in the personal factors have no Test for reliability

Table 9: Reliability Statistics

Reliability Statistics
Cronbach’s Alpha	Cronbach’s Alpha Based on Standardized Items	N of Items
.771	.779	10

Source: Developed by the Author using SPSS (2022)

Table 10: Item-Total Statistics

Item-Total Statistics
	Scale Mean if Item Deleted	Scale Variance if Item Deleted	Corrected Item-Total Correlation	Squared Multiple Correlation	Cronbach’s Alpha if Item Deleted
Management_Support	20.65	22.971	.476	.361	.747
Training_Culture	20.65	22.443	.377	.379	.761
Organizational_Climate	20.69	23.273	.457	.270	.750
Productivity	20.73	23.249	.612	.517	.739
Adaptability	21.00	20.860	.571	.490	.731
Intrinsic_motivation	20.97	21.243	.524	.490	.739
Skill_Flexibnility	20.77	22.765	.307	.430	.774
Job_Environment	20.98	21.160	.558	.440	.734
Job_Communication	20.66	24.640	.303	.377	.767
Employee_Performances	20.66	24.720	.281	.334	.769

Source: Developed by the Author using SPSS (2022)

There are numerous ways for calculating internal consistency, with Cronbach’s alpha being one of the most popular. This statistic is typically employed to examine the consistency of responses to a subset of questions (scale items) that are combined to measure a certain idea. It is a coefficient alpha with a value between 0 and 1. At or above a value of 0.7, the questions on the scale are internally consistent in their measurement (Saunders, Lewis, & Thornhill, 2019). Accordingly, the study proves to have a higher internal consistency with Cronbach’s alpha value of 0.779. Also, the Cronbach’s alpha value cannot be improved further by deleting items as per the item-total statistics table.

Test for linearity and equal variance

Figure 6: Normal P-P plot of Standardized Residual

Source: Developed by the Author using SPSS (2022)

The linear regression process necessitates that the residuals, also known as error terms, have a normally distributed distribution, and the probability plot gives proof that this is the case.

Table 11: Model Summary

Model Summaryb
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.578a	.334	.313	.620
a. Predictors: (Constant), Job_Communication, Organizational_Climate, Training_Culture, Intrinsic_motivation, Management_Support, Skill_Flexibnility, Job_Environment, Adaptability, Productivity
b. Dependent Variable: Employee_Performances

Source: Developed by the Author using SPSS (2022)

In the R column of the model summary table, the value 0.578 indicates a strong multiple correlation coefficient. It is the correlation coefficient between all independent factors and the dependent variable. The Model Summary reveals that the independent factors determine the amount of change in the dependent variable. The R Square value of 0.334 shows that 33.4 percent (0.333 100) of the variance in the dependent variable can be accounted for by the two independent variables. If independent factors are known, it is acceptable to state that we have a "moderate" predictor of the dependent variable.

Table 12: ANOVA Table

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	55.785	9	6.198	16.128	.000b
	Residual	111.452	290	.384
	Total	167.237	299
a. Dependent Variable: Employee_Performances
b. Predictors: (Constant), Job_Communication, Organizational_Climate, Training_Culture, Intrinsic_motivation, Management_Support, Skill_Flexibnility, Job_Environment, Adaptability, Productivity

Source: Developed by the Author using SPSS (2022)

The ANOVA table demonstrates that the mathematical model (the regression equation) adequately explains variation in the dependant variable. The value of 0.000 (which is less than 0.05) indicates that it is unlikely that the variation explained by the model is due to random variation. It can be concluded that variations in the dependent variable are caused by variations in the independent variables. Changes in independent variables led to significant changes in the dependent variable in this study.

Table 13: Coefficients

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	.820	.199		4.130	.000
	Management_Support	.146	.054	.160	2.694	.007
	Training_Culture	-.095	.042	-.136	-2.247	.025
	Organizational_Climate	.145	.053	.153	2.755	.006
	Productivity	.190	.081	.160	2.339	.020
	Adaptability	-.146	.047	-.204	-3.082	.002
	Intrinsic_motivation	.098	.048	.136	2.042	.042
	Skill_Flexibnility	.229	.040	.347	5.767	.000
	Job_Environment	-.119	.047	-.161	-2.544	.011
	Job_Communication	.209	.061	.204	3.425	.001
a. Dependent Variable: Employee_Performances

Source: Developed by the Author using SPSS (2022)

The table indicates that the probability of more extreme results occurring by chance when making predictions was less than 0.001 for only skill flexibility and job communication with significance values 0.000 and 0.001. Also, for management support (p=0.007), training culture (p=0.025), organizational climate (p=0.006), productivity (p=0.02), adaptability (p=0.002), intrinsic motivation (p=0.042) and job environment (p=0.011), probability of occurring extreme results were less than 0.05 of significance level. Therefore, all the hypothesis were significantly supported by the study and the null hypothesis were rejected while skill flexibility and job communication are having highly significant relationship with the EP.

Test for Co-linearity

Table 14: Coefficients Test for Co-linearity

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
		B	Std. Error	Beta			Tolerance	VIF
1	(Constant)	.820	.199		4.130	.000
	Management_Support	.146	.054	.160	2.694	.007	.655	1.527
	Training_Culture	-.095	.042	-.136	-2.247	.025	.631	1.584
	Organizational_Climate	.145	.053	.153	2.755	.006	.749	1.335
	Productivity	.190	.081	.160	2.339	.020	.492	2.034
	Adaptability	-.146	.047	-.204	-3.082	.002	.526	1.900
	Intrinsic_motivation	.098	.048	.136	2.042	.042	.517	1.933
	Skill_Flexibnility	.229	.040	.347	5.767	.000	.635	1.574
	Job_Environment	-.119	.047	-.161	-2.544	.011	.573	1.746
	Job_Communication	.209	.061	.204	3.425	.001	.648	1.543
a. Dependent Variable: Employee_Performances

Source: Developed by the Author using SPSS (2022)

For Independent variables to be independent, Tolerance value should not exceed +1. According to the analysis, the independency is protected between independent variables.

Nonparametric test for normality

Table 15: Hypothesis Test Summary

Hypothesis Test Summary
	Null Hypothesis	Test	Sig.	Decision
1	The categories of Management_Support occur with equal probabilities.	One-Sample Chi-Square Test	.000	Reject the null hypothesis.
2	The categories of Training_Culture occur with equal probabilities.	One-Sample Chi-Square Test	.000	Reject the null hypothesis.
3	The categories of Organizational_Climate occur with equal probabilities.	One-Sample Chi-Square Test	.000	Reject the null hypothesis.
4	The categories of Productivity occur with equal probabilities.	One-Sample Chi-Square Test	.000	Reject the null hypothesis.
5	The categories of Adaptability occur with equal probabilities.	One-Sample Chi-Square Test	.000	Reject the null hypothesis.
6	The categories of Intrinsic_motivation occur with equal probabilities.	One-Sample Chi-Square Test	.000	Reject the null hypothesis.
7	The categories of Skill_Flexibnility occur with equal probabilities.	One-Sample Chi-Square Test	.000	Reject the null hypothesis.
8	The categories of Job_Environment occur with equal probabilities.	One-Sample Chi-Square Test	.000	Reject the null hypothesis.
9	The categories of Job_Communication occur with equal probabilities.	One-Sample Chi-Square Test	.000	Reject the null hypothesis.
10	The categories of Employee_Performances occur with equal probabilities.	One-Sample Chi-Square Test	.000	Reject the null hypothesis.
Asymptotic significances are displayed. The significance level is .050. Source: Developed by the Author using SPSS (2022)

Table 16: One-Sample Chi-Square Test Summary

One-Sample Chi-Square Test Summary
Total N	300
Test Statistic	253.733a
Degree Of Freedom	4
Asymptotic Sig.(2-sided test)	.000
a. There are 0 cells (0%) with expected values less than 5. The minimum expected value is 60.

Source: Developed by the Author using SPSS (2022)

In this situation, it is testing whether it can reject all null hypotheses with confidence. It performed a one-sample chi-square test to determine whether the observed values differed from the predicted values, and since the p-value was less than.001, we can reject the null hypothesis.

Descriptive Statistics test for normality

Table 17: Descriptive Statistics Test for Normality

Descriptive Statistics
	N	Range	Minimum	Maximum	Mean	Std. Deviation	Variance	Skewness		Kurtosis
	Statistic	Statistic	Statistic	Statistic	Statistic	Statistic	Statistic	Statistic	Std. Error	Statistic	Std. Error
Management_Support	300	4	1	5	2.43	.817	.668	.494	.141	-.170	.281
Training_Culture	300	4	1	5	2.44	1.063	1.130	.402	.141	-.499	.281
Organizational_Climate	300	4	1	5	2.40	.788	.621	1.520	.141	1.629	.281
Productivity	300	3	1	4	2.36	.630	.397	1.481	.141	1.121	.281
Adaptability	300	4	1	5	2.09	1.041	1.083	.632	.141	-.556	.281
Intrinsic_motivation	300	4	1	5	2.12	1.042	1.087	.674	.141	-.278	.281
Skill_Flexibnility	300	3	1	4	2.31	1.134	1.285	.367	.141	-1.261	.281
Job_Environment	300	4	1	5	2.10	1.010	1.020	.543	.141	-.646	.281
Job_Communication	300	4	1	5	2.42	.730	.533	1.386	.141	2.037	.281
Employee_Performances	300	4	1	5	2.42	.748	.559	1.351	.141	1.282	.281
Valid N (listwise)	300

Source: Developed by the Author using SPSS (2022)

According to Umashekaran and Soundars, Skewness should be between -1 to +1 for the data and Kurtosis should be between -2 to +2 to accept that data are normally distributed. The analysis states that organizational climate (skewness=1.520), Productivity (skewness=1.481), Job communication (skewness=1.386 kurtosis=2.037) and, Employee performance (skewness=1.351) are not normally distributed.

CHAPTER FIVE – CONCLUSION AND IMPLICATIONS

The research was conducted to identify the organizational factors that affecting the EP of Cinnamon hotels in Sri Lanka and to study the relationship of those factors with the EP of the hotel chain to support further improvement of productivity and financial results of the employees of the Cinnamon Hotels.

There were ten factors identified as influencing factors for the EP such as Management Support, Training Culture, Organizational Climate, Productivity, Adaptability, Intrinsic motivation, Skill Flexibility, Job Environment, Job Communication and, Employee Performances.

The research philosophy was positivism and the approach was deductive. The research strategy was survey and methodological choice was quantitative while cross sectional time horizon was adopted.

Out of 300 participants, 88% were front-line employees and 14% were administrative employees where 6% of them were middle level managers and senior managers. Only 4% were graduates or undergraduates and the majority 68% were possessing none of the qualification and 24% were holding certificate level qualifications. 84% of the participants were new commers and rest 16% were having past job experience prior joining the Cinnamon Hotels.

The sample data were normally distributed except for organizational climate, productivity, job communication and, employee performance according to the skewness and kurtosis values. However, the analysis proved the linearity of data including the residuals are indeed normally distributed during the regression analysis and the study further proved that the data were having higher internal consistency with Cronbach’s alpha value of 0.779. Correlation Metrix determined that there were sufficient moderate to strong coefficients (>.30) to continue the analysis. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) yielded a value of 0.770, which is greater than 0.6; thus, the study's sample adequacy was established. Bartlett's Test of Sphericity yielded a chi-square value of 953.268 at df = 45 and a significance level of 0.000, indicating that the study should be continued.

Communalities explains the proportion of variability in the original variable that is accounted for by the high-loading factors. The heist value recorded for training culture with 80.1%. Also, TVE provides that the first three components taken together (35.22+18.66+11.52) can be said to account for 65.4% of the variance for all variables and the result was supported by the screen plot analysis. The factor analysis provided that productivity (0.780), adaptability (0.752) and Intrinsic motivation (0.720) load strongly on component 1 where job communication (0.766) and employee performance (0.707) result in high scores on component 2.

The research model shows a good multiple correlation coefficient (R=0.578) 33.4% of the variance in dependent variable (R²=0.334) can be explained by both the independent variables. Further, the study supported that that the mathematical model can accurately explain variation in the dependent variable (Sig=0.000).

The table indicates that the probability of more extreme results occurring by chance when making predictions was less than 0.001 for only skill flexibility and job communication for other variables with significance level less than 0.05. Accordingly, skill flexibility (p=0.000 and β=0.347) and job communications (p=0.001 and β=0.204) were supported to have a highly significant positive relationship with employee performance (H₇ and H₉). Also, the rest of hypothesis (H₁, H₂, H₃, H₄, H₅, H₆ and H₈) were supported by the study with significant positive relationship with employee performance by Management support (p=0.007), Training culture (p=0.25), Organizational climate (p=0.006), Productivity (p=0.02), Adaptability (p=0.002), Intrinsic motivation (p=0.042) and, Job environment (p=0.011) therefore, those factors were identified as the organizational factors that affect the employee performance of the Cinnamon Hotels chain in Sri Lanka. The finding was further supported by the one-sample chi-square test with (sig=0.000) where all the null hypothesis were rejected.

However, the magnitude of the effect was comparatively identified with β values of the regression analysis. Accordingly, the affecting factors are listed in descending order based on their β values in the regression analysis.

1. Skill flexibility

2. Job communication

3. Management support

4. Productivity

5. Organizational climate

6. Job environment

7. Training culture

8. Adaptability

However, there may be many non-organizational factors that can have an affect on EP such as political, economical, social, technological, environmental and legal (PESTEL) and also domestic factors such as domestic grievances, psychological factors etc. The mediation of such factors has not studies in this research and future reteaches can study on such areas identify the effect of them over EP.

However, the findings of the study lay a pavement for Cinnamon Hotels management to identify the critical factors which governs the EP of their employees and alter their organizational strategy to address them. Specially, the training and development should orientate into addressing such areas at all levels of the management.

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