Conference Poceedings
Singapore
August,
ACMASS
Annual Conference on Management and Social ScienceACMASS
Annual Conference on Management and Social Science ISBN 978-986-89298-3-8
ISBBME
1
Content
General Information for Conference Participants ... Conference Organization ... ACMASS International Committee Board ... ISBBME International Committee Board ...
Conference Schedule ... Social Science Keynote Speech...
Oral Sessions ... Business & Economics I...
ISBBME-1215 ... 17
ISBBME-1166 ... 30
ISBBME-1340 ... 38
ISBBME-1167 ... 53
ISBBME-1053 ... 61
ISBBME-1154 ... 62
Psychology / Education I ... ACMASS-5890 ... 85
ACMASS-5921 ... 92
ACMASS-5922 ... 98
ACMASS-5836 ... 99
ACMASS-5962 ... 100
ACMASS-5896 ... 110
Marketing, Banking, Business & Economics II ... ISBBME-1324 ... 117
ISBBME-898 ... 126
ISBBME-1253 ... 127
ISBBME-1392 ... 129
ISBBME-1288 ... 130
ISBBME-1287 ... 139
Society I ... ACMASS-5870 ... 151
ACMASS-5912 ... 159
ACMASS-5866 ... 160
ACMASS-5860 ... 161
ACMASS-5861 ... 162
ACMASS-5858 ... 163
ACMASS-5859 ... 164
Society II / Communication ... ACMASS-5905 ... 166
ACMASS-5933 ... 174
ACMASS-5806 ... 183
ACMASS-5864 ... 184
ACMASS-5868 ... 185
2
ACMASS-5904 ... 198
Management I ... ACMASS-5862 ... 207
ACMASS-5887 ... 208
ACMASS-5857 ... 209
ACMASS-5878 ... 210
ACMASS-5882 ... 221
Politics/Culture/Law ... ACMASS-5854 ... 228
ACMASS-5897 ... 229
ACMASS-5903 ... 234
ACMASS-5883 ... 240
ACMASS-5894 ... 241
ACMASS-5818 ... 247
Economics / Finance/ Business & Economics III ... ACMASS-5960 ... 250
ACMASS-5924 ... 256
ACMASS-5851 ... 267
ACMASS-5902 ... 268
ACMASS-5988 ... 273
ACMASS-5995 ... 277
ISBBME-1217 ... 298
Management II ... ACMASS-5873 ... 300
ACMASS-5918 ... 305
ACMASS-5876 ... 314
ACMASS-5987 ... 315
ACMASS-5856 ... 320
Education II ... ACMASS-5877 ... 322
ACMASS-5811 ... 329
ACMASS-5923 ... 334
ACMASS-5886 ... 335
ACMASS-5872 ... 336
ACMASS-5885 ... 343
ACMASS-5931 ... 351
Poster Sessions... Psychology / Society / Law / Marketing ... ACMASS-5909 ... 353
ACMASS-5879 ... 360
ACMASS-5950 ... 361
ACMASS-5895 ... 363
ISBBME-1323 ... 369
Management /Education ... ACMASS-5891 ... 382
3
ACMASS-5976 ... 384
4
General Information for Conference Participants
Information and Registration
The Registration and Information Desk will be situated on the 2nd floor in the Meeting Center at
Hotel Fort Canning Singapore during the following times:
Friday, August 15 (8:15-17:00)
Saturday, August 16 (8:15-17:00)
Parallel Sessions
Parallel Sessions will run onAugust 15 and 16. Oral Sessions are usually 90 minutes in length; each presenter has 15-20 minutes.
Presentations and Equipment
All presentation rooms are equipped with a screen, an LCD projector, and a laptop computer installed with Microsoft PowerPoint. You will be able to insert your USB flash drive into the computer and double check your file in PowerPoint. We recommend that you bring two copies the file in case of one fails. You may also link your own laptop to the provided projector, however please ensure you have the requisite connector.
A Polite Request to All Participants
5 Poster Sessions & Poster Requirements
Materials Provided by the Conference Organizer:
1. X-frame display & Base Fabric Canvases (60cm×160cm) 2. Adhesive Tapes or Clamps
Materials Prepared by the Presenters:
1. Home-made Poster(s)
2. Material: not limited, can be posted on the canvases 3. Size: 60cm*160cm
A 60cm*160cm Poster Illustrates the research findings.
1.Wider than 60cm (left)
6
Conference Organization
ACMASS International Committee Board
Armine Ishkanian London School of Economics
Melissa Thomas The Johns Hopkins University
Ng Chong Guan University of Malaysia
Sibnath Deb Pondicherry University
Adam D. Danel Virginia Polytechnic Institute and State University
Betsy (Sarah E.) Bledsoe-Mansori University of North Carolina at Chapel Hill
Rekha (Rao) Nicholson University of Bath
Ehsanul Haque University of Dhaka
T.S.Devaraja University of Mysore
Nafees Ahmad South Asian University
7
ISBBME International Committee Board
Warner P. Woodworth Brigham Young University
Aloysius Ajab Amin College of Social Science
Amitabh Gupta University of Delhi
Bo Liu University of Electronic Science & Technology
Eko Suyono Msc Ak Jenderal Soedirman University
Erwin Saraswati Brawijaya University
Eun Jin Hwang Indiana University of Pennsylvania
Evelyn Wamboye Pennsylvania State University
Henrique Schneider Department of Economics
Himanshu Tandon Vit University
Ishak Yussof Universiti Kebangsaan Malaysia
Jai Pal Singh CCS Haryana Agricultural University
Jianhong Fan University of Macau
Kavita Sharma University of Delhi
Miao Zhao Roger Williams University
Ocean Fan Lu Camosun College
Rusli Bin Ahmad Universiti Malaysia Sarawak
8
Special Thanks to Session Chairs
Chin-Chia Wu Feng Chia University
Gannu Praveen Kumar Sahasra Institute of Pharmaceutical Sciences Pamela M. H. Kwok PolyU Hong Kong Community College Régis Chenavaz Kedge Business School
Suho Bae Sungkyunkwan University
Alok Satapathy National Institute of Technology, Rourkela Dhemi Harlan Bandung Institute of Technology
Wan-Wen Day Chung-Cheng University
Rosli Mahmood Universiti Utara Malaysia
Dmitry Baluev Nizhniy Novgorod State University Hung-Yi Chen
Soochow University
Leo Huang National Kaohsiung University of Hospitality and Tourism Yu-Mi Kim Chungbuk National University
Pamela M.H. Kwok PolyU Hong Kong Community College Han Jo You University of Seoul
Nan-Ying Yu I-Shou University
9
Conference Schedule
Friday, August 15, 2014
Oral Sessions
Time Information 08:30-16:30 Registration
08:40-10:10 Lavender I---Business & Economics Lavender III---Psychology / Education I 10:10-10:25 Tea Break
10:25-10:30 Lavender I -Welcome Speech
10:35-12:00
Lavender I --- Keynote Speech Keynote Speaker: Carolyn M. Hurley
Paper title : Real Life Myth-busting: Identifying the truth about Deception
Lavender II---Marketing/Banking/Business & Economy II 12:00-13:30 Lunch Time
13:30-15:00 Lavender I ---Society I 15:00-15:30 Tea Break
15:30-17:00
Lavender I --- So iet II /Co u i atio Lavender II --- Ma age e t I
10
Friday, August 15, 2014
Poster Sessions
Time Information 08:30-16:30 Registration
10:00-11:00 Psychology / Society Marketing / Law
Saturday, August 16, 2014
Oral Sessions
Time Information08:30-16:30 Registration
08:45-10:15 Lavender I --- Economics/Finance/Business & Economy III 10:15-10:30 Tea Break
10:30-12:00 Lavender I --- Management II 12:00-13:30 Lunch Time
11
Saturday, August 16, 2014
Poster Sessions
Time Information
08:30-16:30 Registration
10:00-11:00 Management Education
Sunday, August 17, 2014
12
Conference Venue Information
Hotel Fort Canning Singapore
Address: Canning Walk, Singapore,Telephone number: +
Location:
13
14
Social Science Keynote Speech
Hotel Fort Canning Singapore
, Lavender I
/ / Friday :
-
:
Speaker : Carolyn M. Hurley
Topic: Real Life Myth
-
busting:
Identifying theTruth about Deception.
Overview:
Across cultures most people believe that liars ‘avoid eye contact’ and ‘appear nervous’ even
though there is no scientific evidence supporting these beliefs. Relying on inaccurate beliefs may lead to poor deception detection ability in interpersonal relationships, by juries, business persons and security professionals. It doesn’t help that many of these beliefs are highly publicized in the popular media, increasing their erroneous use.
Dr. Hurley’s most recent work – published in the International Journal of Psychological Studies
– examined the source of these deception beliefs, in an effort to understand who is disseminating such inaccurate information. In her keynote speech, Dr. Hurley will discuss this research, as well
as the general necessity for social scientists to expel popular myths, and differentiate ‘fact’ from ‘fiction’ for practitioners employing such practices. The study of deceptive behavior is a prime
example, as it is a field ripe with contradictions, pseudoscience, and results highly dependent on features of the experimental design. Dr. Hurley will discuss her experience as both a researcher and consultant and the challenges of conducting research for the national security community.
Biology:
15
Oral Sessions
Business & Economics I
Lavender I
2014/08/15 Friday 08:40-10:10
Session Chair:
Prof. Chin
-
Chia Wu
ISBBME-1215
The Effects of High-Involvement Human Resource Management Systems on the Job Withdrawal: Focusing on Small & Medium- Sized Firm
Yong-Sun Chang︱Chosun University
Kang-Min Lee︱Chosun University
ISBBME-1166
A Simulated Annealing and a Largest-order-value Method for the Two-machine Flowshop Scheduling Problem to Minimize the Total Completion Time
Jan-Yee Kung︱Cheng Shiu University
Yu Cheng︱Feng Chia University
Shou-Che Wu︱Cheng Shiu University
Chun-His Wang︱National Taichung University of Science and Technology
Yan-Po Chau︱Cheng Shiu University
Chin-Chia Wu︱Feng Chia University
ISBBME-1340
Relationships between Working Hours and Productivity: The Case of Food Services and Information Communication Industries in Hong Kong
Chi Man NG︱The Open University of Hong Kong
Wan Ling Tsang︱The Coventry University
ISBBME-1167
A Genetic Algorithm and a Largest-order-value Method for the Two-machine Flowshop Scheduling Problem to Minimize the Total Tardiness
Wei-Chieh Liang︱Cheng Shiu University
Chih-Hou Wen︱Feng Chia University
Win-Chin Lin︱Feng Chia University
Cheng-Hsiung Shin︱Cheng Shiu University
Chin-Chia Wu︱Feng Chia University
16 ISBBME-1053
Pareto-Undominated and Socially-Maximal Equilibria in Non-Atomic Games
Haifeng Fu︱Xian Jiaotong-Liverpool university
Haomiao Yu︱Ryerson University
ISBBME-1154
Trend and Volatility Regime Switch Stock Index Hedging
EnDer Su︱National Kaohsiung First University of Science and Technology
17
ISBBME-1215
The Effects of High-Involvement Human Resource Management Systems on
the Job Withdrawal: Focusing on Small & Medium- Sized Firm
Yong-Sun Chang
Chosun University/Business Administration, South Korea changk6567@hanmail.net
Kang-Min Lee
Chosun University/Business Administration, South Korea changk6567@hanmail.net
Abstract
The purpose of this study is to find out the relationships between high involvement HRM, turnover culture, job withdrawal including turnover intention and job search behavior. High involvement HRM refers to interconnected human resource management practices designed to enhance employees’ skills and motivation. Turnover culture which means organization's negative pressure to leave organization will moderate relationship between High involvement HRM and job withdrawal. To identify these relationships, empirical data was collected and theoretically arranged. This study made the research model based on the theoretical arrangement to explain these relationships between constructs. Using the collected data from 267 employees at 27 small and medium-sized firms located in Southern region in Korea, this research tested and confirmed the construct validity and internal consistency by exploratory factor analysis, reliability by Cronbach's alpha, and regression analysis of HLM. This research came to the conclusions to as follows: First, high involvement HRM of aggregating employees' perception had the negative effect to individual level's job withdrawal including turnover intention and job search behavior. Second, turnover culture also negatively influenced onto the turnover intention and job search behavior. Third, turnover culture's moderating effect was not confirmed in this research. The findings suggest that high involvement HRM and turnover culture are meaningful variables to understand job withdrawal.
Keywords: High- Involvement HRM Systems, Turnover Culture. Job Withdrawal, Turnover Intention, Job Search Behavior
I. Introduction
Until now since 1980, a significant change in the field of human resources management studies are underway. For human resource management than a microscopic approach, a lot of macro approach has been introduced (Delery & Doty, 1996). Human resource management systems increasing employee motivation, commitment, knowledge, skills, and abilities will positively affect the performance (Bowen & Ostroff, 2004).
18
In this study, the concept of a high-involvement HRM research was conducted by applying it to small & medium firm. Almost, HRM in small & medium firm can't be systematically developed rather than large firm. Therefore, research targeting small & medium firm will have greater meaning than large firm. The development of HRM in those is an important issue. In the 2000s than in the 1990s, small and medium-sized firms had a larger role in Korean employment. Those created new jobs to 86.4% in 2006 compared to 77.1% in 1995 (Chung, Phang, & Kim, 2008). In this context, taking into account the less developed compared to large firms, HRM development of small & medium-sized firms has the potential significance of the research. However, HRM of small & medium-sized firms compared to large firms confronts with quite harsh conditions. Those have difficulty in high turnover, insufficient education and training, low wages, uncertain vision, lack of competent worker (Lee, 2011).
Research about HRM needs analysis of cross-level influences. Takeuchi, Chen, & Lepak (2009) assert that HR systems affect firm performance by creating an organizational environment that elicits employee behaviors and capabilities that contribute to firm competitive advantage. Nishii, Lepak, & Schneider (2008) say that HR practices are associated with organizational performance through their influence on employee attitudes and behavior. Employees' perceptions of HR practices are important.
If large investments in HRM tend to be made in the long term development of employee skill, management focuses on motivating employees to work hard, and management places the importance of employee welfare, Firm's employees perceive their organization valuable.
The use of high-involvement HRM practices, including comprehensive employee recruitment and selection procedures, incentive compensation and performance management systems, and extensive employee involvement and training, can improve enhance retention of quality employees (Huselid, 1995). Therefore, HRM practices can help to create a source of sustained competitive advantage.
This research proposes a model explicating the effects of employees’ collective perceptions of high performance. We provides theoretical and empirical rationales for aggregating employees’ perceptions to HR practice of small and medium-sized firms. And, we present results from moderating effects of turnover culture in which relationships between high involvement HRM of small and medium-sized firms at the organizational level and job withdrawal at the individual levels are verified in an empirical model.
This research seeks to enhance our understanding of cross-level relationships through which an important HRM concept influences individual job withdrawal in organizations.
. Theoretical Backgrounds and Hypotheses 2.1. High- Involvement Human Resource Management Systems
19
High-involvement HRM operate by (a) increasing employees' knowledge, skills, abilities, (b) empowering employees to act, (c) motivating them to do so.
The internal fit perspective suggests that the adoption of an internally consistent system of high involvement HRM Practices will be reflected in better firm performance. High involvement HRM systems refer to a group of separate but interconnected human resource (HR) practices designed to enhance employees' skills and effort (Datta, Guthrie, & Wright, 2005; Huselid, 1995).
Researchers are more apt to suggest that there is an identifiable set of best practices for managing employees that have universal positive effects on organizational performance (Becker & Gerhart, 1996). HRM practices influence employee skills through the acquisition and development of a firm's human capital. Recruiting procedures will have a substantial influence over the quality and type of skills new employees possess. Providing formal and informal training experiences can further influence employees' development. Firm efforts to motivate employee behavior include the use performance appraisals that assess individual or work group performance, linking these appraisals tightly with incentive compensation systems, the use of internal promotion systems (Huselid, 1995).
In order to improve organizational performance, various human resource management practices should be integrated. Organization in order to create a competitive advantage to individual policies or practices that are separated from each other should be the limit (Becker & Gerhart, 1996). Therefore, the effects of human resources management systems have been recently analyzed as a bundle of practices than an individual human resource management practices (Arthur, 1994; Huselid, 1995; MacDuffie, 1995; Youndt et al., 1996). Internally consistent HRM systems among individual practices enhance the company's performance due to complementarity (Becker & Gerhart, 1996; Huselid, 1995). A bundle of individual human resource management practices observed in the form of a systems perspective are being supported (Macduffie, 1995).
High-involvement HRM systems has been measured in a variety of forms. Perry-Smith & Blum (2000) emphasized child care, maternity leave, flexible working practices. Guthrie (2001) used internal promotions, performance based promotions, skill-based pay, group-based (gain-sharing, profit-sharing) pay, employee stock ownership, employee participatory programs, information sharing, attitude surveys, teams, cross-training or cross-utilization, and training focused on future skill requirements.
20
programs, training in group problem solving, and socializing activities and by higher percentages of maintenance.
2.2 Job Withdrawal Behavior
Job withdrawal is a set of behaviors that dissatisfied individuals enact to avoid the work situation. If the job conditions cannot be changed, dissatisfied worker may be able to solve the problem by leaving the job. If the source of the dissatisfaction relates to organization wide policies like HRM, organizational turnover is likely (Noe, Hollenbeck, Gerhart, & Wright, 2012). Three forms of withdrawal cognitions will be examined, namely, attraction of present job, job search behavior and turnover intentions. Turnover intention is an attitude about the organization. An individual develops intentions to leave a specific organization. Job dissatisfaction brings about the job search, and job search is connected to the turnover behavior (Van Hooft et al., 2004).
The perceived desirability and ease of movement cause turnover (Carsten & Spector, 1987; Price & Muller, 1981; Steers & Mowday, 1981). The perceived desirability of movement is usually taken to mean job satisfaction (Mitchell & Lee, 2001). Desirable job satisfaction and organizational commitment influences one's intention to quit (Steers & Mowday, 1981). The perceived ease of movement is reflected by job alternatives, and the perceived desirability of movement is usually taken to mean job satisfaction. The traditional viewpoint is that people become dissatisfied with their jobs, search for alternatives, compare those options with their current jobs and leave if any of the alternatives are judged to be better than their current situation (Mitchell et al., 2001). Accordingly, job dissatisfaction may lead to turnover. Job satisfaction and organizational commitment influence withdrawal cognitions.
The contextual variables of pay, integration, instrumental communication, formal communication, and centralization were the primary determinants of job satisfaction (Lee & Mowday, 1987). Organizational characteristics and experiences, conceptualized as an individual's experienced organizational reality influence on affective responses. Price and Mueller (198l) included components of an internal labor market, that is, promotional opportunity, training and career development, in their investigation of causes of turnover.
2.3. Effects of High-Involvement HRM Systems on the Job Withdrawal
High-involvement HR practices allow a firm to build firm-specific human capital, which in turn influences organizational performance in two ways: directly, via its effect on employee performance, and indirectly, via employee attachment to the firm (Batt, 2002). These increase employees' knowledge, skills, ability. The results are greater job satisfaction, lower employee turnover (Combs, Liu, Hall, & Ketchen, 2006). This research analyses that adopting a cross-level theoretical perspective, which considers aspects of high-involvement HRM, is needed to fully understand how HRM systems relate to employee attitudes and behaviors.
21
HRM affects on employee attitude and behavior. Greater autonomy is associated with higher satisfaction and lower intentions to quit at the individual level of analysis (Hom & Griffeth, 1986). Takeuchi, Chen, & Lepak (2009) explained how HRM relate to employee attitudes. They found that higher level climate acts as an important mediator of the cross-level relationships between HRM and individual job attitudes. This research provided a more explicit integration of micro and macro views of the management of human resources in work organizations. Multilevel analysis of data from hotels in China revealed that high-performance human resource practices were related to service oriented (Sun, Aryee, & Law, 2007).
Human resource incentives such as training, employment security, high relative pay, and practices that build trust are likely to induce employee attachment and commitment (Sun, Aryee, & Law, 2007). Huselid (1995) found considerable support for the hypothesis that investments in HRM practices are associated with lower employee turnover. Since higher levels of high performance work practices lead to lower turnover, and presumably greater employment security, there appears to be considerable justification for encouraging firms to make such investments. High-involvement human resource practices were related to the organizational performance indicators of turnover (Arthur, 1994; Bae & Lawler, 2000; Huselid, 1995; Guthrie, 2000). Therefore, we expect the following:
Hypothesis 1: High- Involvement HRM systems will diminish employee turnover
Hypothesis 2: High- Involvement HRM systems will diminish employee job search behavior
2.4 Turnover Culture as the Contextual and Moderating Variable
Culture means shared conceptions that act in a normative fashion to guide behavior. These social glue binds the organization (Smircich, 1983). Schein (1985, 1992) divides organizational culture into three levels. Organizational culture consists of some combination of artifacts (also called practices, expressive symbols, or forms), values and beliefs, and underlying assumptions that organizational members share about appropriate behavior.
Cultures can generate commitment to corporate values or management philosophy. Culture serves as organizational control mechanisms, informally approving or prohibiting some patterns of behavior (Martin & Siehl, 1983). Culture is holistic, historically determined, and socially constructed, and it involves beliefs and behavior, exists at a variety of levels, and manifests itself in a wide range of features of organizational life (Hofstede, Neuijen, Ohayv, & Sanders, 1990).
The concept of a turnover culture derives from the organizational culture literature. The focus for the current research is to explore the concept of an organizational culture which avoids employee turnover. A turnover culture can be understood to be a set of shared understandings about the legitimacy of leaving an organization. Prior research is theoretically undeveloped and has not explained the moderating effects of organizational culture that explain the relationship between HR practices and employees attitudes.
22
The cross-level effects this study examines consider the extent to which individual-level job withdrawal vary between high involvement HRM and turnover culture. The influence that high involvement HRM practices affect on individual job withdrawal attitudes may vary according to turnover culture. Therefore, we expect the following:
Hypothesis 3: Turnover culture will diminish employee turnover intention. Hypothesis 4: Turnover culture will diminish employee job search behavior.
Hypothesis 5: Turnover culture will moderate relationships between high involvement HRM and turnover intention.
Hypothesis 6: Turnover culture will moderate relationships between high involvement HRM and job search behavior.
. Research Method 3.1. Research Model
Research model shows that high-involvement HRM systems and turnover culture impact employees' attitudes and behaviors. Specifically, turnover culture moderates between high-involvement HRM systems and employees' attitudes and behaviors.
3.2. Sample and Procedure
This study targeted the small and medium-sized firms and sought to collect data from multiple managers and employees within each organization. All employees were full-time. The questionnaires from 27 companies were collected. We collected 267 questionnaire responses of 27 companies. 9.89 responses per organization were used for the analysis. The employees, on average, were 33.01 years old. Majority were male (68.5%), and mere clerk employees were (41.6%), middle management or supervisors (58.4%). 27 firms are almost manufacturing companies. As for the hypotheses, hierarchical linear modeling was conducted using HLM 6.08. High-involvement HRM practices and turnover culture were assessed at the organizational level, withdrawal behavior variables were assessed at the individual level.
3.3 Measures
23
easiness of dismissal (11), employment stability (12), decision-making participation (13), proposal opportunity (14) open conversation (15), bonus for excess earnings (16), regulations of job descriptions (17), and concreteness of job descriptions (18).
Turnover culture was measured using 12 items. Rumery (2003) developed turnover culture measures. Measures of workgroup turnover has three facets: Attitudes toward turnover from the workgroup, for example I would feel comfortable talking with my coworkers about quitting my job , To assess overall turnover culture, 12 items was used. Higher score in turnover culture, organizational members may be pressured against turnover.
Job withdrawal measures turnover intention, job search behavior. The turnover intention was measured with 3 questions on a five-point scale. These 3 questions are the questions used in the study conducted by Hom et al. (1979), and these questions ask the intent to leave the current workplace within 1 year, level of intent to leave the current workplace within 1 year and the possibility to leave the current workplace within 1 year. The job search behavior was measured with 10 questions used by Kopelman et al. (1992). The job search was measured with 10 questions such as a question asking whether or not to read a book relating to the turnover (changing job) on a 5-point scale.
Control variables include the number of employees (organizational level), age, gender and academic backgrounds (individual level).
. Results
According to the results of exploratory factor analysis, the factors are divided into high-involvement human resource management, turnover culture, turnover intention, job search behavior. The explanation ratio was 60.630. The factor loading was based on over 0.5. The questions regarding turnover intention and job search were separately combined into each factor. On the other hand, the questions No 9, 10, 11 and 12 among the questions regarding the high-involvement human resource management were not combined into a factor. Some items of turnover culture were not combined.
The measurement result of reliability in Cronbach’s alpha value showed that high-involvement HRM systems was 0.943, turnover culture was 0.825, job search was 0.920, and the turnover intention was 0.949.
This study aggregated the measured values at individual level and analyzed at organizational level. In such case, the reliability of aggregation variable should be measured. The reliability of aggregation variable was verified through ICC (1) and ICC (2). ICC (1) indicates the ratio of the variance between groups to the variance within group. According to James (1982), the value of ICC (1) is in between 0.0 and 0.5 and the median value is 0.12 in the study on the organizational atmosphere. Also, the value of η2 indicating the proportion of between-group variance among the whole variance was calculated in this study. The aggregation of individual values according to the result of verification of ANOVA, ICC(1), ICC(2), η2
24
25
Hypotheses 1 and 2 suggest that high-involvement HRM of organizational level is negatively related to turnover intention and job search behavior of individual level. As shown by Table 4 (model 2) and Table 5 (model 5), high-involvement HRM of organizational level significantly related to turnover intention of individual level (ϓ= -0.63, p<.01) and to job search behavior of individual level (ϓ= -0.32, p<.01), thereby supporting Hypotheses 1 and 2. As high-involvement HRM practices become dispersed, fewer individuals intend to leave and search their jobs.
Hypotheses 3 and 4 suggest that turnover culture will diminish turnover intention and job search behavior of individual level. According to Table 4 (model 2) and Table 5 (model 5), turnover culture significantly related to turnover intention of individual level (ϓ= -0.30, p<.01) and to job search behavior of individual level (ϓ= -0.24, p<.1). That supported Hypotheses 3 and 4.
26
. Discussion and Conclusion
This study was to empirically analyze the underlying mechanisms through which high-involvement HRM systems relate to job withdrawal including employees' turnover intention and job search behavior. By drawing on theory and research from high-involvement HRM systems and turnover culture literatures, the results supported the cross-level linkages from high-involvement HRM systems and turnover culture (organizational level) to employees' job withdrawal (individual level). This research sketches the relationships between the contingent role of a new organizational context variable, turnover culture and job withdrawal as well as high-involvement HRM practices and that.
The findings of this study revealed high-involvement HRM systems to be related to job withdrawal indicators of turnover intention and job search behavior. HRM practices increase employee motivation through investment of a firm's human capital. This research analyses that adopting a cross-level theoretical perspective, which considers aspects of high-involvement HRM, is needed to fully understand how HRM systems relate to employee attitudes and behaviors. HRM of organizational level affects on individual employee attitude and behavior. In sum, this study contributes to universal approach of strategic HRM literature by illustrating the cross-level linkages among high-involvement HRM, turnover culture, and individual employees' behavior and attitude. Employees' perception about HR practices is likely to play an important role in influencing the ultimate effect of HR practices.
27
asked to report on a firm's HRM systems and strategies. Such a methodology does not permit employees' attribution about HRM practices (Nishii et al., 2008). Therefore, this study measures high-involvement HRM systems through aggregating employees' perception about HR practices. Also, this study introduces turnover culture as the moderating variables. The relationship between high-involvement HRM systems and job withdrawal may be bigger under conditions of high turnover culture. However, contrary to expectations (Hypothesis 5 and 6), no support was found for the moderating effect of turnover culture.
In sum, this study contributes to high-involvement HRM literature by illustrating the cross-level linkages. Collective employee perceptions of HRM impact on their job withdrawal in small and medium firms. HRM practices that increase employees' involvement diminish job withdrawal including turnover intention and job search behavior. That may be related with organizational performance.
In this study, because sample size is small, the reliability of the results may not be sufficient. We hope this research will stimulate much needed additional work on the multi level mechanisms through which high-involvement HRM concepts impact important outcomes of organizations. We also encourage researchers to consider other theoretical perspectives and mechanisms linking high-involvement HRM systems to outcomes, such as individual creativity and knowledge sharing. Second, we can learn more about the organizational-level factors that act as moderators of the relationship between high-involvement HRM and individual outcomes.
References
Arthur, J. B. (1994). Effects of Human Resource Systems on Manufacturing Performance and Turnover, Academy of Management Journal, 37(3), 670-687.
Bae, J., & Lawler, J. J. (2000). Organizational and HRM Strategies in Korea: Impact on Firm Performance in an Emerging Economy, Academy of Management Journal, 43, 502–517. Batt, R. (2002). Managing Customer Services: Human Resource Practices, Quit Rates, and Sales
Growth, Academy of Management Journal, 45(3), 587-597.
Becker, B., & Gerhart, B. (1996). The Impact of Human Resource Management on Organizational Performance: Progress and Prospects, Academy of Management Journal, 39(4), 836-866.
Bowen, D.E., & Ostroff, C. (2004). Understanding HRM-Firm Performance Linkages: The Role of the "Strength " of the HRM System. Academy of Management Review, 29, 203-221. Carsten, J.M., & Spector, P.E. (1987), Unemployment, Job Satisfaction and Employee Turnover:
A Meta-Analytic Test of the Muchinsky Model, Journal of Applied Psychology, 72, 374-381.
Chung, Y-K., Phang, H-N, & Kim, J-W. (2008). The Role of Job Creation during the Period from 1990 to 2006. Journal of Human Resource Management Research, 15(4), 229-243. Combs, J., Liu Y., Hall A., & Ketchen, D. (2006). How much do High-Performance Work
Practices Matter? A Meta-Analysis of their Effects on Organizational Performance,
Personnel Psychology, 59, 501-528.
Datta, D. K., Guthrie, J. P., & Wright, P. M. (2005). Human Resource Management and Labor Productivity: Does Industry Matter, Academy of Management Journal, 48, 135–145.
28
Delery, J. E., & Dotty, D. H. (1996), Modes of Theorizing in Strategic Human Resource Management: Tests of Universalistic, Contingency, and Configurational Performance Predictions, Academy of Management Journal, 39(4), 802-835.
Guthrie, J. P. (2001). High-Involvement Work Practices, Turnover, and Productivity: Evidence from New Zealand, Academy of Management Journal, 44(1), 180–190.
Hofstede, G., Neuijen, B., Ohayv, D. D., & Sanders, G. (1990). Measuring Organizational Cultures: A Qualitative Study across Twenty Cases, Administrative Science Quarterly, 35, 286-316.
Hom, P.W., & Griffeth, R.W. (1995). Employee Turnover, Cincinnati: South/Western.
Hom, P.W., Katerberg, R., & Hulin, C.L. (1979). Comparative Examination of Three Approaches to the Prediction of Turnover, Journal of Applied Psychology, 64, 280-290. Huselid, M. (1995), The Impact of Human Resource Management Practices on Turnover,
Productivity, and Corporate Financial Performance, Academy of Management Journal, 38(3), 635-672.
James, L. R. (1982). Aggregation Bias in Estimates of Perceptual Agreement, Journal of Applied Psychology. 67(2), 219-229.
Kim, S-P. (2006). Commitment Human Resources Management and Organizational Performance in the Small and Medium Sized Firms, Journal of Human Resource Management Research, 13(4), 15-36.
Kopelman, R.E., Rovenpor, J.L. & Millsap, R.E. (1992). Rationale and Construct Validity Evidence for the Job Search Behavior Index: Because Intentions (and New Year's Resolutions) often Come to Naught, Journal of Vocational Behavior, 40, 269-287.
Lee, J-W. (2011). The Current State of Human Resource Management of Small and Medium-Sized Enterprises in Daegu, Management and Information Systems Review, 30(2), 27-56.
Lee, T. W., & Mowday, R. T. (1987). Voluntarily Leaving an Organization: An Empirical Investigation of Steers and Mowday's Model of Turnover, Academy of Management
Journal, 30, 721–743.
MacDuffie, J. P. (1995), Human Resource Bundles and Manufacturing Performance: Organizational Logic and Flexible Production Systems in the World Auto Industry,
Industrial and Labor Relations Review, 48(2), 197-221.
Martin, J., & Siehl, C. (1983). Organizational Culture and Counterculture: An Uneasy Symbiosis,
Organizational Dynamics, 12, 52-64.
Mitchell, T.R., & Lee, T.W. (2001). The Unfolding Model of Voluntary Turnover and Job Embeddedness: Foundations for A Comprehensive Theory of Attachment, Research in Organizational Behavior, 23, 189-246.
Mitchell, T.R., Holtom, B.C., Lee, T.W., & Erez, M. (2001). Why People Stay: Using Job Embeddedness to Predict Voluntary Turnover, Academy of Management Journal, 44(6), 1102-1121.
Nishii, L.H., Lepak, D.P., & Schneider, B. (2008). Employee Attributions of the Why " of HR Practices: Their Effects on Employee Attitudes and Behaviors, and Customer Satisfaction,
Personnel Psychology, 61, 503-545
Noe, R. A., Hollenbeck, J. R., Gerhart, B., & Wright, P. M. (2012). Human Resource Management: Gaining a Competitive Advantage. McGraw-Hill Irwin
29
Price, J.L., & Muller, C.W. (1981). A Causal Model of Turnover for Nurses, Academy of Management Journal, 24, 711-722.
Rumery, S. M. (2003). The Influence of Work Group Culture on Employee Turnover. Ph.D. (The University of Connecticut).
Schein, E. H. (1985). Organizational Culture and Leadership. San Francisco, CA: Jossey-Bass Publishers.
Schein, E. H. (1992). Organizational Culture and Leadership (2nd ed.). San Francisco, CA: Jossey-Bass Publishers.
Smircich, L. (1983). Concepts of Culture and Organizational Analysis, Administrative Science Quarterly, 28, 339-358.
Steers, R.M., & Mowday, R.T. (1981). Employee Turnover and Post decision Accommodation Process, In L. L. Cummings and B. M. Staw (eds.), Research in Organizational Behavior, 3, 235-281. Greenwich, CT: JAI Press.
Sun, L. Y., Aryee, S., & Law, K. S. (2007). High Performance Human Resource Practices, Citizenship Behavior, and Organizational Performance: A Relational Perspective. Academy of Management Journal, 50, 558-577.
Takeuchi, R.., Chen, G., & Lepak, D. P. (2009). Through the Looking Glass of a Social System: Cross-Level Effects of High Performance Work Systems on Employees' Attitudes,
Personnel Psychology, 62, 1-29.
Van Hooft, E.A., Born, M.PH., Taris, T.W., Van Der Flier, H. & Blonk, R.W.B. (2004). Predictors of Job Search Behavior among Employed and Unemployed People, Personnel Psychology, 57, 25-59.
30
ISBBME-1166
A Simulated Annealing and a Largest-order-value Method for the
Two-machine Flowshop Scheduling Problem to Minimize the Total Completion
Time
Jan-Yee Kung
Department of Business Administration, Cheng Shiu University, Taiwan
jennyk@mail.csu.edu.tw
Yu Cheng
Department of Statistics, Feng Chia University, Taiwan goddirk41@yahoo.com.tw
Chin-Chia Wu*
Department of Statistics, Feng Chia University, Taiwan cchwu@fcu.edu.tw
Shou-Che Wu
Department of Modern Living, Cheng Shiu University, Taiwan k0348@gcloud.csu.edu.tw
Chun-His Wang
Department of Accounting, National Taichung University of Science and Technology, Taiwan wachsh@nutc.edu.tw
Yan-Po Chau
Department of Business management, Cheng Shiu University, Taiwan
rainbow@csu.edu.tw
Abstract
The precedence constraint exists in real-life scheduling problems. Application can be seen in the
medical treatments such as operating room scheduling. For example, in the scheduling of patients from multiple waiting lines or different physicians, patients in the same waiting line for scarce resources such as organs, or with the same physician often need to be treated in the first
-come-first serve sequence to avoid ethical or legal issues, and precedence constraints can restrict
their treatment sequence. In light of this observation, we consider a two-machine flowshop
scheduling problem to minimize the total completion time. For solving this problem, we apply two heuristics to find a near optimal solution and evaluate the yielded solution compared with an optimal solution obtained by full enumeration method. We conduct the experiments to test performances of all the proposed algorithms for the small and big numbers of jobs, respectively.
31
1. Introduction
Pinedo (2008) pointed out that in many manufacturing and assembly facilities each job has to undergo a series of operations. Often these operations have to be done on all jobs in the same order implying that the jobs have to follow the same routine. The machines are then assumed to be set up in series and the environment is referred to as a flow shop. Yeung et al. (2004) pointed out that flowshop scheduling problems exist naturally in many real-world situations since there
are many practical as well as important applications for a job to be processed series with more than one stage in industry. Panwalker and Iskander (1977) pointed out that flow time and tardiness are the most prominent measures. Essentially, minimizing the flow time keeps the work-in-process inventory at a low level while minimizing the tardiness reduces the penalties
incurred for late jobs. The former represents internal efficiency while the latter represents external efficiency.
Ignall and Schrage (1965) developed some lower bounds and apply a branch-and-bound method
for the problem. Gonzalez and Sahni (1978) showed that this problem is NP-hard. They used a
branch-and-bound algorithm to derive the exact solution. Garey et al. (1979) proved that the total
completion time minimization problem is also NP hard in the two-machine flowshop. Cadambi
and Sathe (1993) developed a theorem concerning the ordering criteria on a pair of non-adjacent
jobs and then apply this dominance in a branch-and-bound method for the problem. Wang et al.
(1996) proposed three heuristic algorithms for this problem. When adding a learning effect in the problem, Lee and Wu (2004) utilize the branch-and-bound technique to search for the optimal
solution. Das and Canel (2005) pointed out that the development of an exact method affords the developer a great deal of insight into the structure of the problem.
However, the precedence constraint has not been considered in the two-machine flowshp setting.
Moreover, application can be seen in surgery scheduling, there are two types of patients, elective and nonelective. For the elective patients, surgeries can be planned in advance, whereas for nonelective patients, surgeries are unexpected and need to be performed urgently (see Chandra et al. 2014). In light of the above observations, we discuss a set of n jobs to be processed no preemptively on a two-machine flowshop that can be processed at most one job at a time.
The remainder of this paper is organized as follows: In Section 2 we introduce some notation and formulate the problem. In Section 3 we develop two heuristics by combining three local search methods to find near-optimal solutions for the problem. In Section 4 we present extensive
computational results to determine the performance of all the proposed methods. We conclude the paper and suggest topics for future research in the last section.
2. Problem Definition
In this Section, the problem formulation is formally defined as follows. Consider a job set N={ J1, …, Jn } to be processed on machines M1 and M2. The jobs processing times of Ji on M1
and M2 are all constant and fixed numbers, say p1i and p2i, respectively. All jobs have the same
processing order through the flowshop machines and are available at zero. Assume that there are no set-up times and jobs are processed without interruption or preemption. Each job can be
processed on one machine only. Specifically, we consider there are no precedence constraints among operations of different jobs but Ju and Jv has a precedent constraint only. That is, Ju should
be processed before Jv, and it is also assumed that the period between Ju and Jv can be processed
32
For a given sequence S, the completion time of a job scheduled in the ith position on machines M1 and M2 are defined C1[k](S) and C2[k](S), where square brackets [ ] are used to signify the
position of jobs in a sequence. The objective function of this paper is to minimize the total completion time or mean flow time of all n jobs, a widely considered performance measure in literature.
3. Heuristics Methods
In this study, we first apply a largest-order-value combined with a pairwise improvement scheme
that is one of the differential evolution (DE) algorithm, and then use a simulated algorithm (SA) to solve this problem. A brief of the steps of these algorithms are introduced as follows:
Storn and Price (1997) proposed a differential evolution (DE) approach for minimizing possibly nonlinear and non-differentiable continuous space functions. By means of an extensive test bed it
is demonstrated that the method converges faster and with more certainty than many other acclaimed global optimization methods. The major advantage is that this method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation. In order to apply the DE method to solve discrete optimization problem, Bean (1994) adopted the largest-order-value method to decode a set of N initial populations, which are chosen completely
at random, to be used in the discrete optimization problems. The most important implementation details (LOV) of the algorithm are summarized as follows:
Step 1: N initial sequences were randomly generated and each is decoded by the largest-order
-value method, Say, X1, X2, …, XN. We set N to 30 in this study.
Step 2: For each Xi, we use the pairwise interchange to improve Xi.
Step 3: Compare the temporary solution with the current best sequence and keep the best one that has the smaller objective function until N is reached.
Step 4: The process is ended at 30 iterations based on our pretests.
On the other hand, the simulated annealing (SA) approach is also adopted in this study since it has attained wide successful in solving many optimization problems after Kirkpatrick et al. (1983). Moreover, Koulamas et al. (1994) provided a survey of the SA approach in a variety of application areas. This approach has the advantage of avoiding getting trapped in a local optimum. This is due to hill climbing moves, which are governed by a control parameter. The most important implementation details of the SA algorithm are summarized as follows:
Step 1: We arrange the sequence of n jobs by smallest processing times of p1i p2i as an initial
solution. In case job v is scheduled after job u, we move the job v forward to insert before job u when job v is scheduled after job u.
Step 2: The pairwise interchange (PI) neighborhood generation method was used in the SA process.
Step 3: When a new sequence is regenerated, it is accepted if its value in the objective function is smaller than that of the original sequence; otherwise, it is accepted with some probability which decreases as the process evolves. The probability of acceptance is generated from an exponential distribution, P accept
exp
TC
, where is the control parameter andTC
is the change in the objective function. In addition, the method of changing at the kth iteration is obtained from Ben-Arieh and Maimon (1992) and is given by
33
where is an experimental constant. If the total completion time increases as a result of a random pairwise interchange, the new sequence is accepted when P(a ccept)r, where r is a uniform random number between 0 and 1. The value of is set to 2 in our pretests.
Step 4: Based on the tests, the process is ended at 300*n, where n is the number of jobs.
In order to get a good quality solution, we add some local search after the DE or SA process. There are including pairwise interchange, backward-shifted reinsertion, and forward-shifted
reinsertion (Della Croce et al. 1996). They are recorded as LOV, LOV+PI, LOV+BACK, and LOV+FOR for the LOV; and SA, SA+PI, SA+BACK, and SA+FOR for the SA without improvement or with improving by pairwise interchange, backward-shifted reinsertion, and
forward-shifted reinsertion, respectively.
4. Results
In order to take measurements of the performances of proposed LOV and SA algorithm, we carry on two experimental parts including a small job-size and big job-size numbers. All algorithms
are coded in FORTRAN using Compaq Visual Fortran version 6.6 and performed the experiments on a personal computer powered by an Intel Pentium(R) Dual-Core CPU E6300 @
2.80GHz with 2GB RAM operating under Windows XP.
In the experiment setting, we test the number of jobs at n=8, 10 and 12. The processing times of jobs on machine M1 and M2 were generated from a uniform distribution over the integers
U(1,100) and U(1,100), U(1,50) and U(100), and U(1,100) and U(1,50). They are recorded as Types I, II, and II. Total 9 cases were examined and 100 replications were tested for each case in this part.
In the first part, we test the number of jobs at n=8, 10 and 12. We first solved each instance using full enumeration to obtain an optimal solution. We then compared the solutions obtained by the DE or SA algorithm by using the 8 heuristics LOV, LOV+PI, LOV+BACK, and LOV+FOR, and the SA, SA+PI, SA+BACK, and SA+FOR algorithms to obtain near-optimal solutions.in terms
of the percentage error, defined as MPE= i opt 100
opt
H H
H
%,
where Hi is the objective value obtained by algorithm Hi and Hopt is the optimal objective value
obtained by full enumeration. For each case, we recorded the average (denoted as avg) and maximum (denoted as max) percentage error. Tables 1-2 summarize the results.
As shown in Tables 1-2, on average, the mean error percentages of LOV and SA are only 1.348%
34
LOV+FOR are 0.715%, 0.744%, and 0.705%.
For the cases with a large number of jobs, i.e., n = 40, 80 and 120, we first solved each instance by using the 8 heuristics LOV, LOV+PI, LOV+BACK, and LOV+FOR, and the SA, SA+PI, SA+BACK, and SA+FOR algorithms to obtain near-optimal solutions. We measure the
performance of a heuristic algorithm in terms of the relative percentage deviation (RPD), defined as
min min
100%
i
V V RPD
V
,
where Vi is the objective value obtained by a heuristic algorithm H and Vmin is the best solution
among LOV, LOV+PI, LOV+BACK, and LOV+FOR, and the SA, SA+PI, SA+BACK, and SA+FOR algorithms. For each case, we recorded the average (denoted as mean) percentage deviation. Tables 3-4 summarize the results.
As shown in Tables 3-4 and Figures 4-6, on average, the average percentage deviation (RPD ) of
LOV and SA are 19.966% and 0.144%. The results indicates that SA outperforms than LOV at the larger jobs. The worst case of LOV is up to 34.389, but the worst case of SA is only 0.784%. When adding the improvement schemes into LOV and SA. It can be seen that the RPD of LOV+PI, LOV+BACK, and LOV+FOR increase as the number of jobs becomes bigger no matter what data type. On the other hand, the RPDs of SA+PI, SA+BACK, and SA+FOR decline. It can also be seen that the performances of LOVs at types II and III are better than those at type I, while the performances of SAs at type III are better than those at types I and II. The results also indicate that SAs outperform better than those of LOVs. On an average, the RPDs of SA+PI, SA+BACK, and SA+FOR are 0.031%, 0.028%, and 0.103%, while the RPDs of LOV+PI, LOV+BACK, and LOV+FOR are 17.062%, 18.220%, and 18.023%. For consuming the CPU times of LOVs and SAs, both LOVs and SAs take in a second to solve out an instance for the smaller jobs, while SAs take only about 2 seconds and LOVs takes about 340 seconds to solve out an instance for the larger jobs.
Summing up the above observation, it is recommend that SA+BACK can be used for this problem in term of the short CPU time and good solution quality.
5. Conclusions and Suggestions
In this paper we study the two-machine flowshop scheduling problem with a job constraint to
minimize the total completion time. In view as the intractability of the problem under consideration, we construct 8 heuristics based on three commonly used local search methods to approximately solve the problem. The computational results show that the SA algorithm seeded with the solution obtained by combining the pairwise interchange method performs best in terms of solution quality and solution time. Future research may consider applying other metaheuristics to solve the problem involving other scheduling objectives such as the total tardiness.
Acknowledgements
35
6. References
Bean, J.C. (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA Journal of Computing 6: 154-160.
Chandra, C., Liu, Z., He, J., Ruohonen, T. (2014) A binary branch and bound algorithm to minimize maximum scheduling cost. Omega 42: 9-15.
Cadambi, B.V., Sathe, Y.S., 1993. Two-machine flowshop scheduling to minimise mean flow time. Opsearch 30: 35-41.
Das, S., Canel, C., 2005. An algorithm for scheduling batches of parts in a multi-cell flexible manufacturing system. International Journal of Production Economics 97: 247-262.
Della Croce, F., Narayan, V., Tadei R., (1996). The two-machine total completion time flow shop problem. European Journal of Operational Research 90: 227-237
Gonzalez, T., Sahni, S., 1978. Flowshop and jobshop schedules: complexity and approximation. Operations Research 26: 36-52.
Ignall, E., Schrage, L.E., 1965. Application of the branch and bound technique to some flowshop scheduling problems. Operations Research 13: 400-412.
Storn, R., Price, K. (1997) Differential evolution- a simple and efficient heuristic for global
optimization over continuous spaces. Journal of Global Optimization 11(4): 341-359. Wang, C., Chu, C., Proth, J.M., 1996. Efficient heuristic and optimal approaches for n/2/F/
Cischeduling problems. International Journal of Production Economics 44: 225-237.
Garey, M.R., Johnson, D.S., and Sethi, P.R. (1979). The complexity of flowshop and jobshop scheduling. Mathematical Operational Research 1: 117-129.
Kirkpatrick, S., Gellat, .CD., and Vecchi, M.P. (1983). Optimization by simulated annealing algorithm. Science 220: 671-680.
Koulamas, C., Antony, S.R., and Jean, R. (1994). A survey of simulated annealing applications to operations research problems. Omega 22: 41-56.
Lee, W.C. and Wu, C.C. (2004). Minimizing total completion time in a two-machine flowshop with a learning effect. International Journal of Production Economics 88: 85-93.
Panwalker, S.S. and Iskander, W. (1977). A survey of scheduling rules. Operations Research 25: 45-61.
Yeung, W.K., Oğuz, C., Cheng, T.C.E. (2004). Two-stage flowshop earliness and tardiness machine scheduling involving a common due window. International Journal of Production Economics 90: 421-434.
38
ISBBME-1340
Relationships between Working Hours and Productivity: The Case of Food
Services and Information Communication Industries in Hong Kong
Ng Chi Man*
School of Arts and Social Sciences, The Open University of Hong Kong, Hong Kong
cmng@ouhk.edu.hk
Tsang Wan Ling
Faculty of Business, Environment and Society, The Coventry University, United Kingdom
cmng@ouhk.edu.hk
Abstract
This paper aims to study the relationships between working hours and productivity in food services and information communication industries in Hong Kong. Though the discussion on productivities determinants and the analysis of opinions provided by management and the non
-management staff members in these two industries, the study concludes with a recommendation on a productivity enhancement scheme. In total, 312 employees responded to the questionnaire, the demographic characteristics of two industries’ employees were very similar, but the working information was different due to different workplace requirements. Statistical results revealed that the correlation between duration of working hours and productivity are not statistically significant in these two industries. However, the ideas from the management and the non
-management level were different on the correlation between critical factors in these two industries. Moreover, statistical results also indicates that the correlation between working environment and personal health with stress and job satisfaction is positive and statistically significant in the food services industry, while the correlation between the job content and information technology skills is also positive and statistically significant in information communication industry. Management should focus on these critical factors for the improvement and enhancement the productivity of employees, thereby benefitting the organization.
Keyword: Standard Working Hours, Productivity, Job Satisfaction
1. Introduction
39
employers, however, those opposing legislation believe that mandatory standard working hour would affect workers of all skill and seniority levels and hence, influence the operational flexibility of businesses which has long been a key competitive edge of Hong Kong. Thus, an empirical research concerning relationship between working hours and productivity can directly acknowledge the increasing public concerns over the impact of long working hours on workers’ health and productivity, the discussion on determinants of productivity also in the end shed light on the optimal future labor market policy direction, and facilitate Hong Kong Government in mapping out the way of legislation ahead.
40
2. Research Objectives
The key point of the contention focused on the effect of Hong Kong’s business competitiveness which related to productivity and efficiency. This paper aims to study the relationships between working hours and productivity, by comparing long and median working hours’ industries in Hong Kong which were the food services and the information and communication industries. In addition, by enhancing the employees’ productivity, the organizations’ competitiveness in the business market is increased.
Thus, this paper discourse in parallel what factors influencing employees’ productivity, more specifically, this paper centers around the food services industry which the working hour was relatively long compared to information and communication industry which the working hour was comparatively low and at the median of all industries (Table 2) (Census and Statistics Department 2011). Moreover, this paper collects opinion from management and non
[image:43.612.104.511.297.503.2]-management level and analyzes what scheme is recommendable to these two industries.
Table 2 also indicates that the median hours of work in Hong Kong was 48 hours in 2010, which was still higher than other developed countries , such as the United States, which the working hours was limited at 40 hours per week by law (International Labour Office 2004). Also, it was found that long working hours mainly happen in retailing, accommodation and food services industries (54 hours) (Table 2) (Census and Statistics Department 2011). In short, this paper consists of three research objectives:
To develop a conceptual framework for studying labor productivity in long working hours industry.
To examine empirically if these conceptual variables are related to labor productivity
To evaluate how these conceptual variables are inter-linked with each other, and investigate
if these inter-linkages are differ between management and non-management employees
41
3. Literature Review
International Labour Office (2004) emphasizes various aspects in the analytical framework of relating working hours and labor productivity, namely wages, work arrangement, job content and information technology skills, working condition and personal health, and stress and job satisfaction are considered in constructing the conceptual framework.
With regard to wage, long working hours is a widely known labour market phenomenon in Hong Kong and wages is always theoretically and practically regarded as the most critical factor affecting productivity of employees. In general, employees are eager to earn as much as they can in order to improve their living standards, Akerlof (1984) conducted a case study to examine the effect of wages towards labor productivity and found that productivity was much higher after the incentive system was executed, and the quantity of produced parts was up by 140 percent. In addition, fair wage was another vital issue that employers need to consider in order to enhance satisfaction and motivation of employees, some studies find that if wage was set on acceptable and fair level, positive effect on boosting up productivity can be observed and finally beneficial to the organization (Lindbeck and Snower 1987). Hence, adjustment of wages needed to be considered seriously by the organization, wages of acceptable level could enhance the function of productiveness and benefit the competitiveness of organization.
For work arrangement, Schramm (2010) provided a proof that flexible working hour arrangements could boost labor productivity. One typical example is accounting firms, which were among the top three longer working hours organizations, a certified public accounting firm – LBA in the America started to apply work-life balance strategies in 2006, the working hours of
LBA was fixed at 55 hours per week, Monday to Friday and half-day on Saturday. The managing
director proposed all employees kept 55 working hours per week, but employee could re-arrange
the working times, including that employees were not required to report duties on Saturdays compulsorily, the profit growth rose from eighteen to twenty percent in two years and this accounting firm was named on the list of fifty fastest growing private companies in their region. Not only employees, but the firm was also the winner on these changes on working arrangement (Trayner 2008). Flexible working arrangements and work life balance were not very popular in Hong Kong, and many employees felt unsatisfied on this (Vernon 2009). Working arrangements was another critical factor to affect the productivity of employees. The effect of flexible working hours and work life balances between work, family and self-enhancement would be investigated
in this study.
Regarding to job content and information technology skills, different jobs contain different job duties and natures, thus need different types of employees to handle. Knowledge and qualifications influence employees’ ability of using technology, thus skills mismatching could reduce the productivity and quality of outputs and be harmful to the organization in long term. No matter how advanced the technology is, if employees are not knowledgeable to apply technology on performing their jobs, technology is, in principle, useless and could not enhance productivity nor efficiency. Hence, there was a linkage between job content and information technology skills (Mahambare 2010). Figure 1 shows the inter-linkage amongst advanced
42
characteristics did not match, the productivity and quality would not necessarily be enhanced (Anderson 1989).
With reference to working environment and personal health, Heuvel et al. (2010) found that the percentage of absence on sickness for good performance and bad performance employees was similar at 48 percent and 52 percent respectively, besides, employees who frequently moved inside or out of office, performed physical tasks, and had shift duty would decrease the probability of bad performance. In addition, high emotions, low self-discipline, temporary or
part-time contract would cause poorer job performance. It was found that the working conditions
43
4. Methodology
A descriptive questionnaire was used to collect primary sample data with various questions on wages, working arrangements, job contents and information and technology skills, working environment and personal health, and stress and job satisfaction, the questionnaire also allows us to investigate the difference of importance and correlation on those critical factors in long and median working hour’s industries. Employees from two industries were invited to participate the interview, food services industry was selected as its working hour is the longest (54 weekly working hours) while information and communication industry was selected as its working hours was around the median (Table 2). Only full time local employees currently working in these two designated industries can be qualified as target participants. The number of respondents engaged in the food services industry and the information and commu
