Hospitality human resource management discussion Please read the article that I post and read the requirement in the word document. The written critiques s

Hospitality human resource management discussion 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. I posted a sample critique, please have a look.The critique will no longer than 1 page please. 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
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.
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.
N. Singh et al. / Hospitality Management 26 (2007) 131–147
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
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
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
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
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,
N. Singh et al. / Hospitality Management 26 (2007) 131–147
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
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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