Hospitality human resource management discussion

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Please read the article that I post and read the requirement in the word document. The written critiques should be written/typed like notes so that the students can use these to participate in class discussions. Thus, bullet points will be perfect.

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Every week, research articles published in tourism and hospitality journals will be given to students. All students will be required to read and critique the articles. Each student will be required to participate in discussions. Students should come well-prepared with a few discussion points and questions on methodology, contribution, writing-style, etc of the article. Critiques of research articles will enhance student understanding of conducting and disseminating scientific research. 

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ARTICLE IN PRESS Hospitality Management 26 (2007) 131–147 Text mining a decade of progress in hospitality human resource management research: Identifying emerging thematic development Neha Singh, Clark Hu, Wesley S. Roehl National Laboratory for Tourism & eCommerce (NLTeC), School of Tourism & Hospitality Management (STHM), Temple University (062-62), 1700 N. Broad Street, Suite 201, Philadelphia, PA 19122-0843, USA Abstract The authors identified the emerging research streams based on the published research literature in human resource management (HRM) from 1994 to 2003 in the International Journal of Hospitality Management. Textual data were collected and content-analyzed by a text-mining program aided by human judgments. The results from the content analysis of both the computer-aided and human judgmental methods were then integrated and conceptually graphed to map meaningful findings that were logically precise, humanly readable, and computationally tractable. Through this unique approach, nine major HRM research themes emerged and each thematic development based on time and country was interpreted and discussed. r 2005 Elsevier Ltd. All rights reserved. Keywords: Conceptual graphs; Content analysis; Hospitality research; Human resource management (HRM); Text mining 1. Introduction Historically, one of the biggest challenges facing the hospitality industry is human resource management (Olsen, et al., 1990). Evidence suggests that human resource management (HRM) will continue to be one of the challenges faced by managers throughout the foreseeable future. For example, in the USA the Bureau of Labor Statistics Corresponding author. Tel.: +1 215 204 5612; fax: +1 215 204 8705. E-mail addresses: (N. Singh), (C. Hu), (W.S. Roehl). 0278-4319/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhm.2005.10.002 ARTICLE IN PRESS 132 N. Singh et al. / Hospitality Management 26 (2007) 131–147 predicts that the overall economy will grow by 21 million jobs between 2002 and 2012, a 1.4% average annual rate of change (Berman, 2004). Jobs in the leisure and hospitality sector of the US economy, meanwhile, will grow faster than the overall economy, achieving a 1.7% average annual rate during this period. Across the US economy job growth will outpace labor force growth, which is forecast to increase by 17.4 million workers during the 2002–2012 time period (Toosi, 2004). This competitive job market will be further complicated by the aging of the population, migration, and higher rates of labor force participation by women and by people aged 55 years and older (Toosi, 2004). Many other developed economies will experience similar patterns of job growth during the 2002–2012 periods (Berman, 2004). The challenge to find and nurture employees in a tightening labor market is especially important in the hospitality industry. Even though in today’s environment where technological advancements have revolutionized the concept of hospitality services, it is impossible to offer superior guest experiences to customers without well-trained and knowledgeable employees (MacVicar and Rodger, 1996). It is the one resource that cannot be imitated easily in a short period of time and can offer competitive advantage to hospitality organizations. Pringle and Kroll (1997) argued that intangible knowledgebased resources (e.g., human capital) are more likely to lead to a sustainable competitive advantage when the environment is changing rapidly. The human capital (knowledge, skills, and behavior) reinforces the importance of people-related competencies that ultimately link to a firm’s success (Wright et al., 1994). Therefore, effective human resource management can be considered as the new source of competitiveness (Chan et al., 2004). Understanding how to effectively manage this competitive source for better organization performance is of great concern for all hospitality establishments. Given these challenges from the labor market and the continuing importance of hightouch to the provision of successful hospitality products, it is worthwhile to evaluate the current state of research on hospitality human resource issues. Being able to describe the state of current research may help identify strengths and weaknesses in the literature and identify areas in which future research should focus. 2. Foundations for this study In contrast to manufacturing-related activities, service work involves primarily symbolic interactions—interchanges with other people of tangible products as well as intangible services (Fenkel, 2000). Hospitality work is a long-established area of service work, associated with consumption at leisure (Korczynski, 2002). Similar to any other service industries, there is a growing emphasis in hospitality on improving the management of human resources. Rooted from service HRM, hospitality HRM can be characterized by unique attributes of service work—intangibility, perishability, variability, simultaneous production and consumption, and inseparability—which makes human resource management (HRM) a central concern of hospitality professionals (Korczynski, 2002). Hospitality firms compete against one another primarily on the level of services that they can offer to their customers. Due to this competition, employees that are involved in providing these services can be considered as one of the most important resources possessed by hospitality firms (Goldsmith et al., 1997). The hospitality industry is a labor intensive industry and thus, provides a wonderful environment to explore issues of HRM. ARTICLE IN PRESS N. Singh et al. / Hospitality Management 26 (2007) 131–147 133 2.1. Recent content analysis studies in the hospitality discipline Content analysis has been used in several studies to analyze research articles in hospitality management for their research methods and subject areas. For example, Baloglu and Assante (1999) examined research contents by analyzing subject areas and research methods through 1073 articles in five hospitality journals published between 1990 and 1996. They found that most articles focused on human resource area and lodging and foodservice industries. They also found that the survey method was the most frequently used research design and field studies/experiments were the least used ones. Similarly, Bowen and Sparks (1998) employed content analysis to investigate hospitality marketing research by categorizing topic areas, research focuses and methodologies. Their sample consisted of 131 articles published in eight hospitality journals from 1990 to mid-1997. They derived nine categories of research techniques and identified twelve future research areas in hospitality marketing. Crawford–Welch and McCleary (1992) reported their analysis of 653 articles in five hospitality journals for the period 1983–1989 to study the research nature, the focus, and the statistical use of descriptive or inferential multivariate statistics. They suggested that the field of hospitality administration lacked in rigorous and sophisticated quantitative research. In another content-analysis study, Chon et al. (1989) studied 1251 articles published in four hospitality journals over a 20-year span to identify author types (academics/practitioners), research methods, and subject areas. They discovered that most articles were published by academic faculty regarding the hospitality administration subjects and that ‘‘discussion’’ and ‘‘description’’ were the most frequently used research methods but ‘‘surveys’’ and ‘‘experiments’’ were the least frequently used ones. One common observation of these past studies is that they heavily relied on human judgments to ‘‘contentanalyze’’ the published articles by descriptively categorizing publications into defined characteristics. In the area of hospitality HRM research, the similar approach of content analysis was observed in recent studies. An earlier study conducted by Roehl (1993) investigated 26 HRM articles or research notes in five hospitality/tourism journals published during 1988–1992 period. Roehl found that two HR issues (training/education and labor markets) dominated examined literature and that those authors housed in different countries addressed different HR topics. Guerrier and Deery (1998) surveyed 156 articles and books on HRM (including organizational behavior) in the hospitality industry for evaluating the current state of hospitality HRM research. Based on their literature review, they summarized their findings in five themes: labor market trends, employee attitudes, organization structure and culture, hospitality managers and management work, and human resource management practices. Based on his observations in the US, Woods (1999), however, took a different approach to review HR literature and ‘‘conceptually predicted’’ two possible futures for HRM in the new millennium: (1) human resources department will become the organizational leader and much more important to organizations’ daily and long-term decision making, and (2) human resources will be replaced by outsourcing and technology (i.e., virtual HR where all services will be available instantaneously on-demand to the employee at most convenience). Woods envisioned that hospitality companies were heading towards these bipolar future possibilities in the new millennium. Finally, Lucas and Deery (2004) reviewed 103 published HRM articles in five hospitality journals during 2002 and 2003 and found that the research agenda of these articles ARTICLE IN PRESS 134 N. Singh et al. / Hospitality Management 26 (2007) 131–147 mirrored mainstream HR research and theory and focused around general HRM, employee resourcing, employee development and employee relations. Although Lucas and Deery used similar approach to previous content analyses, one interesting observation is that they presented their findings by each journal in a series of tables describing topic and emergent HRM issues listed with each article’s actual/annotated title and key words. They further argued that ‘‘the essence of each paper is captured by the key words (Lucas and Deery, 2004, p. 426).’’ In contrast to the ‘‘breadth’’ approach used in previous HRM content-analysis studies, the authors of the current study employed both the ‘‘breadth’’ and ‘‘depth’’ approaches to review HRM research by investigating into high-frequency keywords in all reviewed articles. Two techniques were used in this study: content analysis based on human judgment as well as a computer aided text-mining of analyzing articles in details by keywords. 2.2. Content analysis The first methodological technique used in this study’s two-step approach to the literature is content analysis. The content analysis approach is a form of scientific inquiry that has commonly been regarded as a useful method for social science studies, especially in consumer research (Kassarjian, 1977). Content analysis calls for the categorization of the various elements or components to help researchers explain trends (Kassarjian, 1977; Krippendorff, 2003). Each step in this research process must be carried out on the basis of explicitly formulated rules and procedures. Even though it requires the researcher to use her/his judgment in making decisions about the data, the decisions must be guided by an explicit set of rules that minimize—although probably never quite eliminate—the possibility of subjective predisposition. The judgments also need to be quantified for precise summary of findings and for interpretation and inference. The findings must have theoretical relevance and be generalized (Kolbe and Burnett, 1991). 2.3. Text mining The second methodology used in this study is text mining. Text mining is about looking for relationships, patterns or trends in textual data. It aims to extract useful knowledge from unstructured or semi-structured text and enable users to discover patterns from the extracted information. It is best suited for learning and discovering information that was previously unknown. While text mining may work with almost any kind of information, it delivers the best results when used with information that is text-based, valuable and explicit text (Semio Corporation, 2004). It helps discover and organize the relationships of concepts in textual data. However, it is up to the domain experts to interpret its meaning and relevance to acquired information. Although most of the past studies using traditional content analysis have addressed the challenge of finding interesting patterns and concepts by human judgments, the text mining approach adds additional value to knowledge discovery due to computer aided analysis (Feldman et al., 1998). The mining parameters can be manipulated by the domain expert but the underlying mining algorithms are designed to follow scientific instructions. According to Karanikas and Theodoulidis (2002), most text mining objectives fall under nine categories of operations: feature extraction, test-base navigation, search and retrieval, ARTICLE IN PRESS N. Singh et al. / Hospitality Management 26 (2007) 131–147 135 clustering (unsupervised classification), categorization (supervised classification), summarization, trends analysis, associations, and visualizations. Feature extraction is to distinguish which noun phrase is a person, place, organization or other distinct objects. This operation should include term extractions and calculate the number of times each term appears in the text analyzed (keyword frequency). Text-base navigation enables the text miner to see related terms in context and connect important relationships between them. The third operation allows the user to search and retrieve relevant information based on pre-specified search criteria. Clustering is the operation of grouping keywords on the basis of some similarity or dissimilarity measure. The most common clustering algorithms are based on statistical classification procedures. Categorization is the operation to define a set of domain-specific terms and the relationships between them through classification algorithms mentioned before. Summarization is the operation to reduce the amount of textual data while maintaining its key elements. Trends analysis is used for discovering trends from time-dependent textual data. Association analysis is to associate one extracted pattern with another pattern found. Visualizations utilize feature extraction and key term indexing in order to build a graphical representation that can help user identifying the main topics or concepts by their importance. 3. Objectives & methodology In order to advance our current understanding of hospitality HRM issues, this study was undertaken to fulfill two important objectives. Firstly, this study introduced a unique content analysis approach to integrate both quantitative (text mining) and qualitative (expert judgmental) methods, an interesting methodology for content analysis studies. Secondly, the authors analyzed HRM research published in the International Journal of Hospitality Management between 1994 and 2003 to evaluate the current state of research on hospitality human resource issues. The journal was carefully chosen because of its seniority, reputation, and most importantly, for its relatively high concentration of published HRM articles among the widely recognized hospitality journals (Jones, 1998). For this study, the research process started by reviewing the title, keywords, and abstract of each article. Out of all articles published during the 1994–2003 period in the journal, 40 articles were found to be human resource-related because they addressed human resource issues such as organization behavior, labor/management relations, personnel, training/ education, employee development, evaluation, or labor markets, as identified by previous studies (Baum, 1993; Chon et al., 1989; Roehl, 1993). All of those articles were optically scanned and the texts (including the articles’ title, abstract and the entire body without references) were extracted into a single text file. This textual data was then analyzed using CATPAC (Woelfel and Woelfel, 1997) to identify high frequency keywords and clustered to present the prominent themes within the field of human resources. CATPAC is a computer-aided text-mining program that can read text and assist the user in summarizing its main ideas. It is based on artificial neural networks (Woelfel, 1993) to detect textual patterns by cluster-analyzing identified keywords’ associations based on distance measures. It makes no linguistic assumptions for analysis (Lowe, no date) but it provides efficient clustering and visualization of the keyword-clusters (dendograms or icicle plots). Human judgment along with an iterative process can be used to choose the number of manageable keywords for deriving the most reasonable clusters that can be interpreted. This choice is critical because if the number of keywords considered is too small, then the ARTICLE IN PRESS 136 N. Singh et al. / Hospitality Management 26 (2007) 131–147 keyword-clusters will be too broad to reveal details and if the number is too large the results might not be manageable. Another useful feature of CATPAC is that it allows the user to conceptually graph prominent themes based on its output of keyword-clusters. Conceptual graphs can express meaning in a form that is logically precise, humanly readable, and computationally tractable (Montes-y-Gómez et al., 2001, 2002). It should be noted that to graph and interpret the results of CATPAC into a conceptual framework, it was important for the authors to carefully read all the articles. The categories that eventually emerged were from an in-depth analysis of the content of each article, as well as from the textual themes and frequencies of the computer aided content analysis. Results were drawn from this integrated approach to provide the readers with a visual and explained summarization of the emerging research streams. 4. Results & discussion 4.1. Text mining and conceptual graphing Based on an iterative process of investigating the icicle plots for solutions with 30 to 100 highest frequency keywords, the icicle plot with 60 keywords was found to be the most reasonable and manageable. A solution with 60 keywords was selected based on two criteria: the clustering and visualization in icicle plots and the number of keywords with the highest frequency. All the identified keywords occurred 90 or more times in the textual database. Similar to the process of naming the factors that result from factor analysis the next step in the analysis process required assigning names to clusters of keywords that emerged from the analyses. Therefore, the major HRM research categories and their respective keywords that emerged from the icicle plot are: HOSPITALITY CAREER ¼ [Attitudes, Employee, Career, Employment, Experience, Working; Manager, Research, Issue, Perceptions]; TRAINING ¼ [Education, Important, Skills, Training]; SATISFACTION ¼ [Characteristics, Positive, Knowledge, Compare, Satisfaction, Opportunity]; TURNOVER/RECRUITMENT ¼ [Culture, Organization, Interview, Turnover, Decision, Workers]; LEGAL ISSUES ¼ [Employer, Labor, Family, Managerial, Problem, Older, Organizational, Service; GENDER ¼ [Gender, Significant, Human Resources, Performance]; WORKPLACE ¼ [Change, department, style, perceived, staff]; PERSONNEL DEVELOPMENT ¼ [Coaching, food service, empowerment, stress, minimum, social, expectation, countries, selection, companies, needs, wage] and PERFORMANCE MEASUREMENT ¼ [Development, Office, Quality, Measure]. Fig. 1 shows HRM emerging research themes and their respective keywords ...
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Topic: The mining a decade of progress in hospitality human resource management research:
emerging thematic development

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