The purpose of this paper is to explore and describe how research on quality management (QM) has evolved historically. The study includes the complete digital archive of three academic journals in the field of QM. Thereby, a unique depiction of how the general outlines of the field as well as trends in research topics have evolved through the years is presented.
The study applies cluster and probabilistic topic modeling to unstructured data from The International Journal of Quality & Reliability Management, The TQM Journal and Total Quality Management & Business Excellence. In addition, trend analysis using support vector machine is performed.
The study identifies six central, perpetual themes of QM research: control, costs, reliability and failure; service quality; TQM – implementation and performance; ISO – certification, standards and systems; Innovation, practices and learning and customers – research and product design. Additionally, historical surges and shifts in research focus are recognized in the study. From these trends, a decrease in interest in TQM and control of quality, costs and processes in favor of service quality, customer satisfaction, Six Sigma, Lean and innovation can be noted during the past decade. The results validate previous findings.
Of the identified central themes, innovation, practices and learning appears not to have been documented as a fundamental part of QM research in previous studies. Thus, this theme can be regarded as a new perspective on QM research and thereby on QM.
Carnerud, D. (2018), "25 years of quality management research – outlines and trends", International Journal of Quality & Reliability Management, Vol. 35 No. 1, pp. 208-231. https://doi.org/10.1108/IJQRM-01-2017-0013Download as .RIS
Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited
For a considerable period of time, quality has been at the center of management studies and debates concerning theoretical as well as practical issues (Aune, 1999; Boaden, 1996, 1997; Chiles and Choi, 2000; Dale et al., 2001; Dean and Bowen, 1994; Fisher and Nair, 2009; Garvin, 1988; Kroslid, 1999; Miller, 1996; Rogberg, 2006; Sousa and Voss, 2002; Zairi, 1994). Hence, it is fair to say that defining, delimiting and describing the essence of quality and its various incarnations in the field of management studies has fascinated scholars for years. Indeed, Perla and Parry (2011) and Schoengrund (1996) would argue that this fascination has continued for centuries. During recent decades, many different quality-related topics have appeared in theory as well as in practice, such as: quality control (QC), total quality control, company-wide quality control, zero quality control, quality improvement, quality management (QM), total quality, total quality management (TQM) and business excellence (BE) (Bergman and Klefsjö, 2010; Dahlgaard et al., 2007; Dale et al., 2007; Oakland, 2014). Additionally, with the aim of underlining the shared origins and values of the topics, all-inclusive descriptions such as the quality movement and quality revolution are also present (Dahlgaard-Park, 1999, 2011; Senge, 1992; Winter, 1994). The multitude of terminologies would perhaps not pose such a dilemma if it was simply a question of a number of synonyms, however, as Klefsjö et al. (2008) elegantly summarize the issue – do we really agree on what we are talking about and does it matter? Barley and Kunda (1992), Giroux and Landry (1998), Xu (2000) and Zbaracki (1998) would most probably agree with Klefsjö et al. (2008) that terminologies and rhetoric do matter and that they influence the evolution and development of academic fields and communities.
In recent years, scholars have noted that the interest in quality-specific topics seems to have decreased on account of other up-and-coming management research topics such as BE, Six Sigma and Lean (Andersson et al., 2006; Dale et al., 2000; Dahlgaard and Dahlgaard-Park, 2006; Foley, 2001). The emergence of these new, distinctive and sometimes competing management perspectives and initiatives has added new fuel to the existing debate regarding specific quality-related occurrences being possible fads or fashions (Abrahamson and Eisenman, 2008; Abrahamson and Fairchild, 1999; Bergquist et al., 2012; Brown, 2013; Hackman and Wageman, 1995; Rahman, 2004; Singh and Smith, 2006; Van Der Wiele et al., 2000).
Consequently, fundamental issues regarding the research field and its evolution can be said to be as relevant as ever before.
In an attempt to investigate the issue of changes in QM research throughout the years and its current outlooks, Lo and Chai (2012) apply quantitative methods on bibliometric data. A quantitative approach is advocated since the only, or to the best of their knowledge predominant, method of analyzing QM research and its transformations through the years seems to be a qualitative one (Lo and Chai, 2012). Not discarding valuable insights gained from previous studies, Lo and Chai (2012) nonetheless see a value in complementary quantitative approaches which might help to discover and describe novel patterns and perspectives previously omitted. Reflecting on the results of their study, Lo and Chai (2012) conclude that quantitative analysis coupled with qualitative evaluations of core results does indeed generate noteworthy perspectives relevant to the concepts and assumptions investigated. Hence, they suggest additional studies based on quantitative methodology, emphasizing comparative studies of academic journals within the field as a way of broadening the view and understanding of QM (Lo and Chai, 2012). This study responds to this request from Lo and Chai (2012) and applies text mining processes to explore and describe how QM research has evolved over 25 years in three scientific journals.
Over the last few decades, data mining has been used frequently within business intelligence (BI), and text mining is now expanding as a method to extract knowledge (Carneiro Moro et al., 2014; Chakrabarti, 2003; Kent, 2014). Text mining and data mining share the same purpose: to look for valuable patterns, correlations and trends in large data sets with the help of statistical and mathematical techniques; a process too complex and resource-demanding for manual processing (Aggarwal and Zhai, 2012a, b; Liu, 2011). Not surprisingly, text mining is increasingly used in quality and business development and new areas of application are being continuously developed and tested in theory as well as in practice (Choudhary et al., 2009; Finch, 1999; Heim and Field, 2007; Kent, 2014; Khamis et al., 2013; Köksal et al., 2011; Lo, 2008).
Research on QM literature with the aim of identifying changes over time has been undertaken previously (Ahire et al., 1995; Gupta et al., 2014; Rahman and Sohal, 2002; Sila and Ebrahimpour, 2002; Zain et al., 2001). Martínez-Lorente et al. (1998) note that the term TQM started to become popular in the mid-1980s, but that the elements that shaped it were actually developed during the 1950s-1970s. Dereli et al. (2011) conclude that QM has started to attract an increasing amount of interest from the service industry during the last decade. Furthermore, they distinguish an interest in ISO and quality certifications in the literature and ask for further studies which identify their distribution over the years (Dereli et al., 2011). Lo and Chai (2012) establish that QM research has evolved around customer satisfaction, implementation of TQM, monitoring quality cost, measuring service quality and studying TQM outcomes. Core research themes from which succeeding themes have sprung are found to be service quality, customer satisfaction and TQM framework identification (Lo and Chai, 2012). Furthermore, conceptual developments are noticed; from an initial focus on statistical control, a gradual shift has taken place toward strategic aspects such as improving general and key business processes (Lo and Chai, 2012). Subsequently, recent developments in TQM consist of a shift toward providing quality service and measuring its success (Lo and Chai, 2012).
Dahlgaard-Park et al. (2013) determine that the total number of articles in the field of TQM has been decreasing after having reached its peak in 1995. Whereas, the number of papers focusing on techniques and tools within the QM framework in terms of Lean, Just-in-Time/Toyota Productions System, Benchmarking and Six Sigma has been increasing (Dahlgaard-Park et al., 2013). Additionally, papers focusing on core values/key principles regarding the need to build a quality culture in terms of leadership, people-based management, continuous improvement, management based on facts, and focus on the customer have slightly increased during the last decade. Dahlgaard-Park et al. (2013) conclude that their findings establish that QM is now at a more mature stage, where focus has shifted from tools, techniques and core values which are needed for building a quality and BE culture. Furthermore, Dahlgaard-Park et al. (2013) find that organizational culture is becoming increasingly important for organizations in the pursuit of quality and excellence. With the exception of Lo and Chai (2012), the studies are mainly literature reviews conducted through a qualitative approach, with the methods of Dereli et al. (2011) and Martínez-Lorente et al. (1998) balancing in-between. Moreover, as noted by Dahlgaard-Park et al. (2013), both Lo and Chai (2012) and Dereli et al. (2011) are based on the same study object, Total Quality Management and Business Excellence (TQMBE), and both studies together cover a period of only 15 years (1995-2008 and 1996-2010). With the aim of extending the scope of years studied as well as academic journals included, Dahlgaard-Park et al. (2013) incorporate literature from 25 years (1987-2011) from ABI/INFORM Complete periodical database containing more than 6,800 academic journals. Nevertheless, they still fall back on qualitative methodology and literature reviews. Consequently, the gap for quantitative studies on QM literature over a longer time period, including more journals than TQMBE, remains open. With the aim of bridging this gap to some extent, this study includes 25 years of data from TQMBE as well as The International Journal of Quality & Reliability Management (IJQRM) and The TQM Journal (TQMJ).
Materials and methods
Data mining process
Within the field of text- and data mining, a Cross Industry Standard Process for Data Mining (CRISP-DM) has been developed consisting of six phases: business understanding, data understanding, data preparation, modeling, evaluation and deployment (Wirth and Hipp, 2000). This study has been conducted according to the CRISP-DM standard and an overview of the work process is described below and visualized in Figure 1.
Business understanding includes definition of the study objectives; formulation of the problem; and formation of the strategy to tackle the problem (Wirth and Hipp, 2000). The purpose of this study was to explore and describe how research on QM has evolved historically through the application of text mining methodology on the digital archives of scientific journals in the field. One core strategy for achieving the purpose was to follow in the footsteps of Lo and Chai (2012) and Dereli et al. (2011), and collect a data set in which a minimum amount of screening of data was performed. It was believed that such an approach would facilitate comparison and validation of results as well as offer a relevant way to explore and describe QM’s historical development.
Understanding data refers to the collection and initial exploration and evaluation of the data allowing for possible changes in scope and strategy (Marbán et al., 2009). The following points guided the search for scientific journals from which data could be collected:
QM or TQM had to be in the title;
The purpose of the journal should be to present a broad scope of QM research;
Journals with a long publication history were prioritized;
The journals had to be peer-reviewed;
Source normalized impact per paper (SNIP), impact per publication (IPP) and SCImago Journal Rank (SJR) had to be available and acceptable for each journal;
The database structure had to allow large-scale data collection; and
The journals should, if possible, be distributed by different publishing houses.
Assessment of scientific journals according to the above stated guidelines singled out IJQRM, TQMJ and TQMBE as suitable objects for the purpose of the study. The three journals were judged to have a broad enough scope on QM so that specific niches did not distort the representation, with IJQRM and TQMBE as borderline cases since they emphasize reliability management and BE, respectively. However, IJQRM had the most extensive digital archive accessible, which indicated that the journal was an early adopter and conveyor of QM research giving it a unique position vs other journals in the field. Whereas, TQMBE was originally published under the name TQM, adding BE to the title in 2003, signaling that TQM was its main domain. Satisfactory SNIP, IPP and SJR values were available and stable for all three journals, with TQMBE having a unique position vs the two other journals as it also had impact factor (IF) obtainable. Furthermore, IJQRM and TQMJ were published by the Emerald Group, whereas, TQMBE was from the Taylor & Francis Group. Also, testing showed it was possible to collect data from the three scientific journals (rendered data set described in more detail under data source). Last but not least, Lo and Chai (2012) highlight TQMJ as an appropriate source for potential comparison studies and identify IJQRM as one of the journals which has been cited most. Preparing data includes cleansing the data of distorting information and values as well as narrowing down the elements and variables to be included and processed (Kurgan and Musilek, 2006). Data available from IJQRM, TQMJ and TQMBE were: year of publication, author(s), title, abstract, keywords and type of publication (e.g. research paper, book review, editorial, etc.). Collecting and studying journal abstracts is common in text mining (Feldman and Sanger, 2007). The purpose of abstracts is to summarize the main points of a research paper and they are generally accessible online free of charge, hence, database creation consisting of research paper abstracts offers a cost-efficient approach for researchers interested in the specific kind of studies (Delen and Crossland, 2008). Usage of keywords is also an alternative approach, however, it is seen as a less reliable data source since researchers are relatively free to choose keywords; consequently, there is a risk of adding keywords that help index the research paper rather than give it an accurate classification (Miner, 2012). Consequently, research paper abstract, year of publication and scientific journal were selected as variables for the study. A subsequent step was to isolate the research paper abstracts from other journal content such as book reviews, editorials and errata. As the strategy was to collect and organize a database with a minimum amount of screening, no other profiling or labeling of data was conducted.
The modeling phase concerns the choice and calibration of methods to analyze the data (Reinartz, 2002). Cluster analysis was chosen as the principal method of data analysis (lengthier description found under Cluster analysis) and is visualized in Figure 2. As illustrated in Figure 2, the key operator, besides the assistant operators selecting attributes, reading from and writing to Excel, is process documents from data. Via this operation, a word vector is generated, which is needed to perform cluster modeling (as well as probabilistic topic modeling). In order to generate a word vector, conventional sub operations are required, such as transforming all cases into either lower or upper case (transform cases), tokenizing, filtering stop words and filtering tokens, visualized in Figure 3 (Weiss et al., 2012).
In order to create a word vector, it is also necessary to select an underlying scheme for the vector (Weiss et al., 2012). Examples of such schemes are term frequency, term occurrences and term frequency-inverse document frequency (tf-idf) (Weiss et al., 2012). According to Weiss et al. (2012), tf-idf is a well-known method of classifying words according to their relative importance in a data set.
Hence, tf-idf was chosen as classification scheme for the cluster modeling sessions.
In order to obtain an overall view as well as a more delimited perspective on specific time periods, cluster modeling was carried out both on the extensive data set and on demarcated phases in time. Since TQMBE started publishing in 1990, this year was chosen as the baseline for all modeling activities. As the data set then covered 25 years, intervals of five years were considered suitable as they gave some room for changes between each interval. The time periods thus became: 1990-1994, 1995-1999, 2000-2004, 2005-2009 and 2010-2014. Additionally, time series analysis was assessed to give a valuable insight on the overall data set as it offers a way to study data from a longitudinal perspective (Chakrabarti, 2003; Djurfeldt and Barmark, 2010). Hence, trend analysis using support vector machine and moving average on each journal’s full publications track record as well as on the entire, joint, data set was carried out.
Evaluation of the modeling means results are secured and compared with the objectives of the study (Mariscal et al., 2010). In this study, probabilistic topic modeling was selected as the principal method for the evaluation and interpretation of the cluster modeling results (lengthier description found under probabilistic topic modeling).
Deployment relates to the objectives of the study and ensures that the results are applied accordingly (Mariscal et al., 2010). The discussion and conclusions in this study are considered to correspond to the deployment phase of CRISP-DM.
The data source of the study consists of research paper abstracts from three scientific journals focusing on QM:
The online catalogue of IJQRM starts in 1984. In 1998, the International Journal of Quality Science merged into IJQRM. TQMJ has been online since 2008 but its predecessor, The TQM Magazine, started publishing online in 1988. In 1998, Training for Quality merged into The TQM Magazine. TQMBE was established in 1990 under the name TQM and has been online from the start. In 2003, BE was added to the title rendering its current name. During 1990-2014, a total of 4,227 research papers with corresponding abstracts were published: IJQRM 1,336 papers, TQMJ 1,271 papers and TQMBE 1,620 papers. As data for each journal’s total publication history was gathered, this was used for time series analysis, consisting of a total of 4,412 papers with corresponding abstracts; IJQRM 1,475 papers, TQMJ 1,317 papers and TQMBE 1,620 papers.
An excerpt of SNIP, IPP and SJR values between 2011 and 2014 is shown in Table I, which supports the notion of all three journals being recognized and reliable resources of the QM academic community. According to Lo and Chai (2012), TQMBE represents a global and unbiased perspective on QM as more than 50 percent of its content originated from authors outside of the UK. Dereli et al. (2011) find that 24 percent of the authors originate from the UK, which supports the claim of Lo and Chai (2012). However, the findings of Dereli et al. (2011) also show that 41 percent of TQMBE authors originate from Europe, which could indicate a bias toward Europe. This line of argument could also find support in the results of Lo and Chai (2012) as the UK, Europe (Continental) and Scandinavia are presented and treated as different entities instead of one shared, in which case they would also add up to approximately 40 percent. Additionally, the findings of Dereli et al. (2011) show that 50 percent of the authors in TQMBE originate from English-speaking countries which could indicate a partiality in favor of the English-speaking world. No bibliometric studies could be found regarding IJQRM and TQMJ, hence, it is possible that the journals are biased and represent regional rather than international perspectives on QM.
Cluster analysis is a collection of multivariate techniques whose main goal is to group data based on its inherent characteristics (Kaufman and Rousseeuw, 2009). Cluster analysis should primarily be seen as an exploratory and descriptive technique since results are highly dependent on the specific variables used in the data set and clusters will always be formed regardless of the existence of any actual structure in the data (Hair et al., 2014). As the purpose of the study was both exploratory and descriptive, cluster analysis was chosen as the principal method of analysis.
Even though several clustering algorithms exist, k-means clustering algorithms dominate in that they are often used as a synonym for clustering algorithms all together (Wu, 2012). The issue of choosing the optimal k-Means algorithm depends on the data at hand as well as which algorithms are available in the software package used (Arthur and Vassilvitskii, 2007; Jain, 2010; Stuti and Veenu, 2013). RapidMinerStudio® was used for cluster modeling; and experimentation revealed that using Squared Euclidean Distance as a divergence measure with Bregman divergences as a measure gave stable results.
One of the critical issues to decide upon when applying cluster modeling is choosing an appropriate stopping rule to determine which number of clusters best represents the data structure (Child, 2006). There is no standard objective selection process for choosing stopping rules; in addition, the specific theoretical and practical research situation needs to be taken into consideration as important conceptual issues may lie embedded in the data, e.g. manageability and communicability (Hair et al., 2014). Modeling started on the overall data set and was stopped at seven clusters as additional clusters only resulted in the creation of new extremely small clusters and no overall changes in results, see Table II. For analytic and comparability purposes, in the following modeling sequences, the number of clusters was kept to seven.
In cluster analysis, there is no single method for validating, evaluating and labeling clusters (Aggarwal and Zhai, 2012a, b). This troubling fact is even more of a concern when it comes to evaluation of unstructured data (text) as this is still a relatively novel area of research and classical processes for validating structured data (numbers) are not applicable (Larose, 2005; Miner, 2012). However, one automated procedure on the rise for this purpose is probabilistic topic modeling (Blei, 2012; Mimno, 2012; Meeks and Weingart, 2012).
Probabilistic topic modeling
Probabilistic topic modeling encompasses several associated methods that group words into topics on the basis of their most probable association (Aggarwal and Zhai, 2012a, b; Newman et al., 2009; Welling et al., 2008; Xie and Xing, 2013). Although it is a relatively new method, topic models are generally considered to be a fast and effective way to identify and portray the most frequently occurring and probable themes and subjects in unstructured data sets (Blei et al., 2003; Steyvers and Griffiths, 2007). Figure 4 illustrates, in simplified terms, how topic models operate and are applied in the study. Figure 4 also shows how words in research paper abstracts are identified and organized into topics according to the probability of word association. Further information on how the results display and were analyzed is given below and in Figure 5.
One well-tested and conventional distributed algorithm for probabilistic topic modeling is the Latent Dirichlet Allocation (LDA) (Blei et al., 2003). As LDA is well documented and available through the R® topic modeling package (https://cran.r-project.org/), it was chosen for topic modeling in this study. As opposed to cluster modeling, in probabilistic topic modeling, the tf-idf formula is incompatible for word vector creation as every word is assigned a probability for every topic (Blei and Lafferty, 2009). In its place, term frequency is the principal method for generating word vectors (Blei and Lafferty, 2009). Consequently, term frequency was selected as the word vector scheme for the probabilistic topic modeling sessions. After testing, it was decided that a topic model containing five topics would be suitable as it allows for several different topics to display, while five topics is still a manageable amount for manual analysis. Following the same line of thought, it was decided to display only the first eight words of a topic in the analysis, i.e. the eight words which have the highest probability to be associated. As topic models operate on random seeding for the creation of probability distributions, the outcome will always be slightly different from one execution to the next (Hornik and Grün, 2011). Therefore, in R, there is a possibility of running the same model several times on the same data set after which the best fit is presented – a function called nstart (Hornik and Grün, 2011). In simple terms, the more runs a topic model does, the more stable the outcome. At some point, however, the results will start to differ only marginally and relative stability arises. Initial modeling showed that the topic models stabilized at 1000 runs (nstart 1000). Consequently, doubling the amount of runs should not have any significant impact on the result. Hence, to strengthen the reliability of the modeling results, each topic model was deployed twice on the same data set with 1,000 runs (nstart 1000) and 2,000 runs (nstart 2000), respectively. Figure 5 shows the results from topic modeling cluster 0 and cluster 5.4.0 and confirms that the difference between 1,000 runs (nstart 1000) and 2,000 runs (nstart 2000) is minimal and, hence, that additional runs would be superfluous.
When it comes to the analysis, evaluation and interpretation of topic models, no machine learning models have yet outperformed that of human judgment even if steady progress is being made in the field (AlSumait et al., 2009; Dacres et al., 2013; Chang et al., 2009; Chemudugunta et al., 2008a, b; Wallach et al., 2009; Xie and Xing, 2013). Czarniawska (2014) summarizes the issue by stating that many researchers have concluded that no software can interpret the collected field material and say “what it means” or “what it could mean”, which creates the possibility of several competing or complementing interpretations or perspectives. The viewpoint is applicable on the conducted study; interpretations and evaluations represent the author’s assessments. Figure 5 illustrates how results from topic modeling cluster 0 and cluster 5.4.0 were evaluated and interpreted and act as exemplification of the overall analytical process. In Figure 5, unique words for cluster 0 and cluster 5.4.0 are marked in green and yellow respectively and then summarized in the far right column. Figure 5 illustrates that words such as quality and management are common in all clusters and consequently are not highlighted. Also, Figure 5 illustrates how process appears in cluster 0, but is only included in the summary of cluster 5.4.0. This is because process as well as quality and management appear in several clusters, but, in cluster 5.4.0, the word appears more often and therefore becomes a distinguishing word for the given cluster. The results from each cluster and topic were treated in this way and the outcome is presented in Figures 7-12.
Outlines and trends in the data
Figure 6 displays a visual representation of the publications of each journal as well as the total number of publications during 1990-2014. In addition, Figure 6 shows a trend analysis using support vector machine as well as sliding average on the totality. From the time series analysis, it is evident that the total number of published papers is showing a positive trend, which could be interpreted as showing that QM in general is still a vibrant and active academic discipline. On the other hand, the positive trend is largely due to TQMBE increasing the number of annually published papers. In turn, TQMJ is showing an overall negative trend reaching a peak in 1992, after which a ten-year decrease in annual publications is manifested, whereupon it increased somewhat and stabilized. The development of IJQRM can be said to lie in-between the two other journals, with a decrease in publications between 1995 and 1999 after which it also stabilized – displaying a slight overall positive trend. This evolvement does not say anything about the standard of the publications, meaning that an increased number of publications is not necessarily better than a lower number – it could all be a consequence of conscious strategic decisions from the editorial boards. However, as all three journals show a relatively stable track record for the last 12 years, this could indicate that research on QM has reached a more stable phase, supporting the conclusions by Dahlgaard-Park et al. (2013) that QM has matured as an academic discipline. It is good to keep in mind the number of publications as the cluster modeling on both the totality as well as the five time periods is executed on the complete data set, not an equalized sample from each journal. As showed in the methodological section, there is not a significant difference regarding the number of publications between the journals when it comes to the overall data set. But, looking at the two time periods 1990-1994 and 2000-2014, the results can be accused of being biased toward TQMJ, respectively TQMBE rather than showing a representative picture of the specific time periods. However, given that the three journals together represent the research field, the results do portray what topics were more widespread than others at the time and thus provide fair snapshots of the time periods.
A visualization for the overall modeling as well as the five time series is presented in Figures 7-12. In Figure 7, clusters containing 5 percent or more of the total number of abstracts, i.e. minimum 211 abstracts, are classified as central topics and are marked in green. Clusters containing 4 percent of the total amount of abstracts, i.e. more than 169 abstracts and less than 211, are categorized as semi-central topics and are marked in orange. According to this classification, six central topics are identified which together represent 55 percent of the total amount of data: control, costs, reliability and failure; Service quality; TQM – implementation & performance; ISO – certification, standards and systems; Innovation, practices and learning and customers – research and product design. Five semi-central topics are identified which together represent 20 percent of the total amount of data: quality awards and business excellence frameworks (BEFs); Performance management & measurement; Process control & improvement; TQM – improvement, customers, management and employees and Systems & standards. Together the 11 central and semi-central topics cover 75 percent of the data, with the remaining 25 percent of data dispersed among the 15 residual clusters. A coverage of 75 percent would indicate that the 11 topics represent the majority of the published research and can thus be seen as a valid summary of QM research between 1990 and 2014. Regarding cluster 5 and its subsequent division into seven new clusters, no common denominators for either cluster 5 or its two dominant clusters 5.0 and 5.4 are identified, which is why they have been left blank.
Compared with the findings of Lo and Chai (2012) who found QM research to be concentrated around implementation of TQM, monitoring quality costs, measuring service quality, customer satisfaction and studying TQM outcomes, there are apparent similarities and overlaps which are presented in Tables II and III.
It is worth noting that customer satisfaction and customers – research and product design might not have as clear a fit as the other central topics. A look at Figures 7-12 clarifies the matching as customer satisfaction and service quality are repeatedly bundled together, which is why it is fair to say that service quality and customers – research & product design do indeed correspond to the categories measuring service quality and customer satisfaction in the findings of Lo and Chai (2012). However, ISO – certification, standards and systems and innovation, practices and learning do not have an explicit counterpart in the findings of Lo and Chai (2012). But, as Dereli et al. (2011) found that ISO and certifications were central themes in QM research, the topic is backed by previous research. This leaves innovation, practices and learning without justification in preceding findings, indicating that it can be seen as a previously overlooked central theme of QM research. From the results, it is possible to observe that IJQRM dominates cluster 0 relating to control, costs, reliability and failure as well as cluster 4 on QFD. This could endorse that IJQRM has a unique niche vis-à-vis the other two journals, which is also suggested by the title – referring to reliability management. TQMBE, in turn, dominates cluster 2 concerning service quality and has a lead in cluster 3 on TQM – implementation and performance. Cluster 6 covering ISO – certification, standards and systems is evenly distributed between the journals as well as cluster 1 covering Six Sigma and Lean. TQMJ does not dominate any cluster but is instead evenly distributed over all clusters. Given that TQMJ is the journal with the smallest number of annual publications for the last 12-year period, it shows that it does not lack coverage of any of the identified topics. All in all, the data shows that there is an overlap between the three journals’ coverage of QM research but with some distinct focus areas, which is expected. This could be taken as confirmation that the three journals do indeed cover the same research areas and are thus relevant objects of comparison when looking at the evolution of QM research.
A study of Figures 8-12 gives a more detailed understanding of how the aggregated central topics have taken shape through the years. Service quality and TQM – implementation have consistently been present in the data from 1990 onwards. From 2000 onwards, performance was embraced as central topic within TQM research. At the same time, service quality was matched with customer satisfaction, highlighting a relationship between the two topics. This does not mean that customers – research and product design only belong to service quality but that the topic is scattered among many clusters in the divided modeling session and is therefore not apparent in other clusters. Control, costs, reliability and failure is also present from the start in the form of QC, process control and quality costs & design with reliability added in 1995. Failure cannot be seen as a specific subject in Figures 8-12 which is explained in the same way as customers: in itself failure is not large enough to show in the divided modeling sessions as opposed to the aggregated results. ISO shows up in the data from 1995 with a gradual shift from implementation toward performance, with certification as the joining link. As with customers and failure, standards and systems are not shown in the divided modeling session but are naturally constant companions of both implementation and performance. As a response to Dereli et al. (2011) who called for results portraying the development of research on ISO over time, the study shows that ISO is constantly oscillating between 5 and 10 percent of the research abstracts in each time period with a peak in 2000-2004 (Figure 10). Lastly, from innovation, practices and learning, only innovation is apparent as a sole cluster in Figures 8-12 which is explained by practices and learning being topics associated with many other themes, but, when data is divided, these do not appear independently.
In relation to Lo and Chai (2012), who identified service quality, customer satisfaction and TQM framework identification as core research themes from which succeeding themes evolved, the study partly supports their findings. However, a comparison between findings from a chronological perspective is awkward primarily since their data starts in 1996 and the division of time periods is hence not identical. Additionally, TQM framework identification is an ambiguous term which needs a more detailed description if it is to be comparable with the results of this study. Nonetheless, the results fully support the notions of Lo and Chai (2012) that QM has undergone conceptual developments, from an initial focus on statistical control with a shift toward strategic aspects and a current research interest in providing quality service and measuring its success. In the study, the shifts are manifested as quality control, quality costs and process control feature in 40 percent of the abstracts in 1990-1994 (Figure 8) whereas in 2010-2014 (Figure 12), they only correspond to 17 percent of the abstracts. Inversely, service quality has gone from an initial coverage of 6 percent in 1990-1994 (Figure 8) to, together with customer satisfaction, incorporating 22 percent of the abstracts in 2010-2014 (Figure 12). The development of service quality and customer satisfaction could also be seen as being supported by the findings of Dereli et al. (2011), who state that QM has attracted increased interest from the service industry.
Regarding terminology, it is worth noting how QM and TQM appear in Figures 7-12. In Figure 7 TQM is identified as a specific category both in cluster 3 (TQM implementation and performance) and cluster 5.4.2 (TQM improvement, customers, management and employees), whereas QM is not represented in any cluster. In Figures 8-12, QM and TQM are both represented in separate as well as shared clusters. The reasons for this outcome are twofold. First, TQM is most probably a more common abbreviation than QM in abstracts, which is why TQM is visible in the probabilistic topic modeling results as opposed to QM. The results show that quality and management appear in a multitude of clusters, indicating that QM and consequently QM is indeed the terminology most commonly used in QM research. The reason for not emphasizing QM in Figure 7 is that total also appears in some tables indicating that TQM, hence TQM, is also present in some clusters. Therefore, with the aim of not tangling up and muddling the categorization between what is QM, TQM or both, only TQM was emphasized as it clearly appeared in cluster 3 and 5.4.2. In the results from the time series modeling, the dividing lines between QM and TQM were easier to identify because the terminologies have been spelled out, even if QM is not apparent as an abbreviation in these results other than as two separate words. Reconnecting to Martínez-Lorente et al. (1998), who found TQM to be on the rise during the 1980s, the results show a dominance of TQM in the early 1990s (Figure 8), where it alone stood for 27 percent of the research abstracts. After this, TQM seems to have faded out as a particular area of research representing only 9 percent of the abstracts in 2010-2014 (Figure 12). This development of TQM is supported by Dahlgaard-Park et al. (2013) who found the topic of TQM to be decreasing whereas papers focusing on techniques and tools within the QM framework, such as Lean and Six Sigma, were seen to be increasing. The study also identifies a steady increase of abstracts on Lean and Six Sigma. Six Sigma represented 2 percent of the abstracts in 2000-2004 (Figure 10), after which, together with Lean, it covered 5 percent of the abstracts in 2005-2009 (Figure 11) and eventually 8 percent of the abstracts in 2010-2014 (Figure 12). Then, comparing the three last time periods, it seems as if research in 2000-2009 (Figures 10 and 11) was more dispersed between different tools and techniques than in 2010-2014 (Figure 12), which could point toward a consolidation within the field. Furthermore, Dahlgaard-Park et al. (2013) established that organizational culture is becoming increasingly important for organizations in the pursuit of quality and excellence. Interpreted narrowly, organizational culture does not seem to attract any specific interest in QM research since the topic does not generate even a small cluster. On the other hand, organizational culture could be classified as all-encompassing terminology which permeates many of the identified clusters but is not used in its own right – which is why it is not revealed in the data.
Initially, it is important to acknowledge that cluster modeling will always bundle together on the basis of similarity. It is highly likely that the cluster modeling is influenced by the formal similarity or structure of each abstract, meaning that abstracts from each journal adhere to specific standards, whereby they have a higher probability of being matched as similar. The same goes for subfields – a subfield may present its research or write abstracts in a comparable way, which in turn may influence the cluster modeling. This could be the reason for specific techniques and tools such as ISO, Six Sigma being singled out, possibly overemphasizing their importance and giving them undeserved presence in the spotlight. Ultimately, it is important not to take the modeling results as definitive but as a complementary perspective on existing findings, exploring and describing QM research over time. Also, it is important to keep in mind that the study only covers journals and articles published in English, which inherently favors native English speakers (Cho, 2004; Duszak and Lewkowicz, 2008; Flowerdew, 1999; Uzuner, 2008). Taken together with the results from Dereli et al. (2011), showing that 50 percent of contributing authors in TQMBE are linked to English-speaking countries, coupled with the results from Lo and Chai (2012), revealing that approximately 40 percent of the contributors are from Europe, the study could be suspected of portraying QM from a European and English-speaking perspective, rather than a global one. When studying differences between North American-based scholars in strategic management and those established elsewhere in the native English-speaking world, Pilkington and Lawton (2014) found clear disparities in research method and epistemology between North American and non-North American English language academics. According to Pilkington and Lawton (2014), the impact implications on strategic management teaching, executive development and consulting are likely to be significant. However, further studies are needed to establish the existence and range of such possible effects. Given that bibliometric studies regarding country affiliation of authors have only been found for TQMBE, analyzing and discussing the results of the current study from such perspectives becomes somewhat ambiguous. However, as the areas are closely related, it is plausible that the findings of Pilkington and Lawton (2014) are also valid for QM. This would suggest that QM as a discipline could benefit from studies which enhance our understanding of how contexts such as language and domicile have influenced the discipline.
Looking at the results, it is evident that QM research has undergone shifts and transitions – from an initial domination of TQM and control of quality, costs and processes toward a predominance of service quality and customer satisfaction as well as Six Sigma, Lean and innovation. On the other hand, it is important not to exaggerate or be blinded by the changes but to keep in mind the central themes which are perpetual and remain at the heart of QM research year after year, although there might be surges in popularity. Otherwise, an overemphasis on temporary trends risks providing justification for the criticism regarding theoretical fuzziness and its possible effects on the field voiced by researchers such as Singh and Smith (2006), Foley (2001), Boaden (1996), Giroux and Landry (1998).
Lastly, the study should, on account of its methodological approach, be mainly seen as exploratory and descriptive in nature, not explanatory. The purpose of the study was to explore and describe how QM research has evolved historically. Therefore, it is beyond the scope of the study to make any statements on the theoretical, practical or conceptual similarities or differences between the research topics manifested in the data as well as why they have come about. Given the applied methods, it would have been possible to classify the modeling results deductively, based on previous findings and theories, thereby, tying it closer to existing theorization on the issue. But, on account of the study being exploratory and descriptive, an inductive path was chosen to give leverage to the possibility of identifying new perspective and dimensions, however, grounded in data.
The purpose of the study was to explore and describe how research on QM has evolved historically. Thus, topics of QM research and their development over time have been documented. Central topics within QM research are found to be:
control, costs, reliability and failure;
TQM – implementation and performance;
ISO – certification, standards and systems;
innovation, practices and learning; and
customers – research and product design.
Of the central topics, all besides innovation, practices and learning have previously been identified as core themes by the studies of Lo and Chai (2012) and Dereli et al. (2011). Hence, innovation, practices and learning can be seen as a newly identified central theme within QM research which prior studies have overlooked.
Furthermore, it is shown that QM research has undergone shifts of research focus – most notably from a dominance of TQM and control of quality, costs and processes toward service quality and customer satisfaction as well as Six Sigma, Lean and innovation.
Finally, the study can be said to support the notion of Lo and Chai (2012) that quantitative studies are indeed a fruitful methodological pathway that needs further attention when conducting exploratory and descriptive studies.
Managerial and policy implications
The study brings new perspectives on the evolution of QM which might aid researchers as well as practitioners to position and comprehend their efforts from fresh dimensions, be they academic or applied. For individuals and organizations that have applied and abandoned QM initiatives at a fast pace, the central topics might help to find a more lasting approach to QM, which could lead to a less volatile future. Conversely, conservative gatekeepers of traditional QM perceptions may perhaps see an opportunity for innovation and progress in that some newly introduced terminologies might not merely be temporary fashions and fads, but actually bring valuable knowledge and learning to the table.
The data set for this study consists of research paper abstracts from three scientific journals. Consequently, it is possible that outlines and trends of QM research that could be identified through other scientific journals and scientific publications, such as books; book reviews; general reviews; secondary articles; editorials; guest editorials; awards for excellence (notifications); conference proceedings; introductions or summaries from conference and notes from the publisher, are omitted. The Quality Management Journal (QMJ) in particular could have added a valuable perspective to the study. QMJ is highly ranked and cited by the QM research community and, as it is published by the American Society for Quality, it supposedly could represent how QM research in the USA has evolved from 1993 and onwards. Since the current study could be suspected of incorporating a European bias, as discussed in the methodological section, such a complementary viewpoint could strengthen the study’s validity. Although, given that bibliometric studies regarding country affiliation of authors has only been found for TQMBE, actions aiming to reduce regional bias may well, unintentionally, have the opposite effect. Also, the inclusion of QMJ would not reduce the bias in favor of native English speakers and regions. For this purpose, it would be necessary to include academic journals written in languages other than English. Furthermore, inclusion of IJQRM and TQMBE in the data set may have skewed the results in favor of reliability management and BE, giving the topic a more prominent place than it actually deserved – especially as the data was not screened prior to modeling. Lastly, classifications and interpretations have been made qualitatively and are thus exposed to the risk of subjective judgements negatively influencing the evaluation process. As a way to minimize such personal bias and thereby improve what Arbnor and Bjerke (2009) refer to as internal and external validity, it would have been beneficial to utilize validation strategies, such as peer-review or member checking, proposed by Creswell (2013). Accordingly, in order to improve the accuracy of future studies, it could be sensible to reconcile preliminary classifications and findings with viewpoints from scholars representing a diverse set of perspectives.
Future studies could gain in reliability and relevance by including additional scientific journals in the data set. For studies where minimal profiling of data is prioritized, QMJ would be an additional data source to consider. Screening and profiling of data prior to modeling could be a way to further strengthen the reliability and validity of results, focusing more distinctly on QM and TQM. It would also expand the list of scientific journals from which data could be collected, as it would allow specific writings on the selected topics to be pinpointed as opposed to complete publishing records. Additionally, it could be possible to include discontinued journals in the data set. This would increase the list of possible journals to include in the study in addition to, possibly, amplifying trends and fads. Taking on a Kuhnian viewpoint, such case selection could perhaps help to identify anomalies and crises within QM as well as clarify how normal science within the paradigms has evolved and where it is heading. Potential journals which could be considered for this purpose are: International Journal of Quality Science (merged into IJQRM in 1998); Journal of Quality Management (discontinued as of 2002), International Journal of Applied Quality Management (discontinued as of 2000) and the Asian Journal of Quality (discontinued as of 2012). Furthermore, to increase the relevance of a time series analysis using number of published papers as baseline data, it would be fitting to expand the data so that it contains SNIP, IPP and SJR values (or IF when available) for each scientific journal. This way, it would be possible to link or evaluate the trend regarding the number of issues annually with the assessed importance by the academic community. Additionally, further bibliometric studies, with the aim of mapping basic information as well as tracing links between authors, journals, countries, etc., could help broaden our understanding of how QM has evolved as well as why.
The CRISP-DM process (Wirth and Hipp, 2000) with summary of key actions performed in the study
SNIP, IPP and SJR scores for IJQRM, TQMJ and TQMBE 2011-2014
|Title||SNIP 2011||IPP 2011||SJR 2011||SNIP 2012||IPP 2012||SJR 2012||SNIP 2013||IPP 2013||SJR 2013||SNIP 2014||IPP 2014||SJR 2014|
Results from clustering TQMJ, TQMBE & IJQRM abstracts 1990-2014
|7 clusters||7 clusters (% of total)||Cluster 5||Cluster 5 (%)||Cluster 5 (% of total)||Cluster 5.0||Cluster 5.0 (%)||Cluster 5.0 (% of total)||Cluster 5.4||Cluster 5.4 (%)||Cluster 5.4 (% of total)|
Corresponding categories Lo and Chai (2012)
|Lo and Chai (2012)||Central topics||Semi-central topics|
|Monitoring quality costs||Control, costs, reliability & failure||Process control & improvement|
|Measuring service quality||Service quality|
|Implementation of TQM||TQM – implementation and performance||Quality awards and business excellence frameworks (BEFs)/TQM – improvement, customers, management & employees|
|Studying TQM outcomes||TQM – implementation and performance||Performance management and measurement/systems and standards|
|Customer satisfaction||Customers – research and product design|
Abrahamson, E. and Eisenman, M. (2008), “Employee-management techniques: transient fads or trending fashions?”, Administrative Science Quarterly, Vol. 53 No. 4, pp. 719-744.
Abrahamson, E. and Fairchild, G. (1999), “Management fashion: lifecycles, triggers, and collective learning processes”, Administrative Science Quarterly, Vol. 44 No. 4, pp. 708-740.
Aggarwal, C.C. and Zhai, C.X. (2012a), “A survey of text clustering algorithms”, Mining Text Data, pp. 77-128.
Aggarwal, C.C. and Zhai, C.X. (2012b), Mining Text Data, Kluwer Academic Publishers, Boston, MA.
Ahire, S.L., Landeros, R. and Golhar, D.Y. (1995), “Total quality management: a literature review and an agenda for future research”, Production and Operations Management, Vol. 4 No. 3, pp. 277-306.
AlSumait, L., Barbará, D., Gentle, J. and Domeniconi, C. (2009), “Topic significance ranking of LDA generative models”, in Buntine, W., Grobelnik, M., Mladenic, D. and Shawe-Taylor, J. (Eds), Machine Learning and Knowledge Discovery in Databases, Springer, Berlin and Heidelberg, pp. 67-82.
Andersson, R., Eriksson, H. and Torstensson, H. (2006), “Similarities and differences between TQM, six sigma and lean”, The TQM Magazine, Vol. 18 No. 3, pp. 282-296.
Arbnor, I. and Bjerke, B. (2009), Methodology for Creating Business Knowledge, 3rd ed., Sage, London.
Arthur, D. and Vassilvitskii, S.G.H. (2007), “K-means++: the advantages of careful seeding”, Discrete Algorithms: Proceedings of the Eighteenth Annual ACM-SIAM Symposium, (SODA ‘07), pp. 1027-1035.
Aune, A. (1999), Nordic School of Quality Management, Chapter 2, Studentlitteratur, Lund.
Barley, S.R. and Kunda, G. (1992), “Design and devotion: surges of rational and normative ideologies of control in managerial discourse”, Administrative Science Quarterly, Vol. 37 No. 3, pp. 363-399.
Bergman, B. and Klefsjö, B. (2010), Quality from Customer Needs to Customer Satisfaction, Studentlitteratur, Lund.
Bergquist, B., Eriksson, H., Garvare, R., Hallencreutz, J., Langstrand, J., Vanhatalo, E. and Zobel, T. (2012), “Alive and kicking – but will quality management be around tomorrow? A Swedish academia perspective”, Quality Innovation Prosperity, Vol. 16 No. 2, pp. 1-18.
Blei, D.M. and Lafferty, J.D. (2009), “Topic models”, in Srivastava, A.N. and Sahami, M. (Eds), Text Mining: Classification, Clustering, and Applications, CRC Press, Boca Raton, FL, pp. 71-94.
Blei, D.M., Ng, A.Y. and Jordan, M.I. (2003), “Latent Dirichlet allocation”, Journal of Machine Learning Research, Vol. 3, January, pp. 71-94.
Blei, D. (2012), “Probabilistic topic models”, Communications of the ACM, Vol. 55 No. 4, pp. 77-84.
Boaden, R.J. (1996), “Is total quality management really unique?”, Total Quality Management, Vol. 7 No. 5, pp. 553-570.
Boaden, R.J. (1997), “What is total quality management … and does it matter?”, Total Quality Management, Vol. 8 No. 4, pp. 153-171.
Brown, A. (2013), “Quality: where have we come from and what can we expect?”, The TQM Journal, Vol. 25 No. 6, pp. 585-596.
Carneiro Moro, S.M., Ribeiro Cortez, P.A. and Ferreira Rita, P.M.R. (2014), “Business intelligence in banking: a literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation”, Expert Systems with Applications, Vol. 42 No. 3, pp. 1314-1324.
Chakrabarti, S. (2003), Mining the Web: Discovering Knowledge from Hypertext Data, Morgan Kaufmann, Amsterdam.
Chang, J., Boyd-Graber, J., Wang, C., Gerrish, S. and Blei., D. (2009), “Reading tea leaves: how humans interpret topic models”, in Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I. and Culotta, A. (Eds), Proceeding from conference Neural Information Processing Systems 22 2009 (NIPS 2009), Vancouver, BC, December 6-11, pp. 288-296.
Chemudugunta, C., Smyth, P. and Steyvers, M. (2008a), “Combining concept hierarchies and statistical topic models”, Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 1469-1470.
Chemudugunta, C., Smyth, P. and Steyvers, M. (2008b), “Text modeling using unsupervised topic models and concept hierarchies”, available at: https://arxiv.org/abs/0808.0973
Child, D. (2006), The Essentials of Factor Analysis, A&C Black, London.
Chiles, T.H. and Choi, T.Y. (2000), “Theorizing TQM: an Austrian and evolutionary economics interpretation”, Journal of Management Studies, Vol. 37 No. 2, pp. 185-212.
Cho, S. (2004), “Challenges of entering discourse communities through publishing in English: perspectives of nonnative-speaking doctoral students in the United States of America”, Journal of Language, Identity, and Education, Vol. 3 No. 1, pp. 47-72.
Choudhary, A.K., Oluikpe, P.I., Harding, J.A. and Carrillo, P.M. (2009), “The needs and benefits of text mining applications on post-project reviews”, Computers in Industry, Vol. 60 No. 9, pp. 728-740.
Creswell, J.W. (2013), Qualitative Inquiry & Research Design Choosing Among Five Approaches, 3rd ed., Sage publications, Thousand Oaks, CA.
Czarniawska, B. (2014), Ute på fältet, inne vid skrivbordet (1. uppl.), Studentlitteratur, Lund.
Dacres, S., Haddadi, H. and Purver, M. (2013), “Topic and sentiment analysis on OSNs: a case study of advertising strategies on Twitter”, CoRR abs/1312.6635.
Dahlgaard, J.J. and Dahlgaard-Park, S.M. (2006), “Lean production, six sigma quality, TQM and company culture”, The TQM Magazine, Vol. 18 No. 3, pp. 263-281.
Dahlgaard, J.J., Kristensen, K. and Kanji, G.K. (2007), Fundamentals of Total Quality Management Process Analysis and Improvement, Taylor & Francis, London.
Dahlgaard-Park, S.M. (1999), “The evolution patterns of quality management: some reflections on the quality movement”, Total Quality Management, Vol. 10 Nos 4/5, pp. 473-480.
Dahlgaard-Park, S.M. (2011), “The quality movement – where are you going?”, Total Quality Management & Business Excellence, Vol. 22 No. 5, pp. 493-516.
Dahlgaard-Park, S.M., Chen, C.K., Jang, J.Y. and Dahlgaard, J.J. (2013), “Diagnosing and prognosticating the quality movement – a review on the 25 years quality literature (1987-2011)”, Total Quality Management & Business Excellence, Vol. 24 Nos 1/2, pp. 1-18.
Dale, B.G., Van Der Wiele, T. and Van Iwaarden, J. (2007), Managing Quality, Blackwell Publishing, Malden, MA.
Dale, B.G., Wu, P.Y., Zairi, M., Williams, A.R.T. and Van Der Wiele, T. (2001), “Total quality management and theory: an exploratory study of contribution”, Total Quality Management, Vol. 12 No. 4, pp. 439-449.
Dale, B.G., Zairi, M., Van der Wiele, A. and Williams, A.R.T. (2000), “Quality is dead in Europelong live excellence-true or false?”, Measuring Business Excellence, Vol. 4 No. 3, pp. 4-10.
Dean, J.W. and Bowen, D.E. (1994), “Management theory and total quality: improving research and practice through theory development”, Academy of Management Review, Vol. 19 No. 3, pp. 392-418.
Delen, D. and Crossland, M.D. (2008), “Seeding the survey and analysis of research literature with text mining”, Expert Systems With Applications, Vol. 34 No. 3, pp. 1707-1720.
Dereli, T., Durmuşoğlu, A., Delibaş, D. and Avlanmaz, N. (2011), “An analysis of the papers published in total quality management & business excellence from 1995 through 2008”, Total Quality Management & Business Excellence, Vol. 22 No. 3, pp. 373-386, doi: 10.1080/14783363.2010.532337.
Djurfeldt, G. and Barmark, M. (2010), Statistisk verktygslåda 2 – multivariat analys, Studentlitteratur, Lund.
Duszak, A. and Lewkowicz, J. (2008), “Publishing academic texts in English: a Polish perspective”, Journal of English for Academic Purposes, Vol. 7 No. 2, pp. 108-120.
Feldman, R. and Sanger, J. (2007), The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data, Cambridge University Press, Cambridge.
Finch, B.J. (1999), “Internet discussions as a source for consumer product customer involvement and quality information: an exploratory study”, Journal of Operations Management, Vol. 17 No. 5, pp. 535-556.
Fisher, N.I. and Nair, V.N. (2009), “Quality management and quality practice: perspectives on their history and their future”, Applied Stochastic Models in Business and Industry, Vol. 25 No. 1, pp. 1-28.
Flowerdew, J. (1999), “Problems in writing for scholarly publication in English: the case of Hong Kong”, Journal of Second Language Writing, Vol. 8 No. 3, pp. 243-264.
Foley, J.K. (2001), “From quality management to organization excellence: don’t throw the baby out with the bath water’”, Proceedings of the Fourth International and Seventh National Research Conference on Quality Management, Sydney, pp. 154-177.
Garvin, D.A. (1988), Managing Quality: The Strategic and Competitive Edge, Simon and Schuster, New York, NY.
Giroux, H. and Landry, S. (1998), “Schools of thought in and against total quality”, Journal of Managerial Issues, Vol. 10 No. 2, pp. 183-203.
Gupta, V., Garg, D. and Kumar, R. (2014), “Depiction of total quality management during a span of 2003-2013”, Journal of Engineering and Technology, Vol. 4 No. 2, pp. 81-86.
Hackman, J.R. and Wageman, R. (1995), “Total quality management: empirical, conceptual, and practical issues”, Administrative Science Quarterly, Vol. 40 No. 2, pp. 309-342.
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2014), Multivariate Data Analysis, 7th ed., Pearson Education Limited, Essex.
Heim, G.R. and Field, J.M. (2007), “Process drivers of e-service quality: analysis of data from an online rating site”, Journal of Operations Management, Vol. 25 No. 5, pp. 962-984.
Hornik, K. and Grün, B. (2011), “Topicmodels: an R package for fitting topic models”, Journal of Statistical Software, Vol. 40 No. 13, pp. 1-30.
Jain, A.K. (2010), “Data clustering: 50 years beyond K-means”, Pattern Recognition Letters, Vol. 31 No. 8, pp. 651-666.
Kaufman, L. and Rousseeuw, P.J. (2009), Finding Groups in Data: An Introduction to Cluster Analysis, Vol. 344, John Wiley & Sons, Hoboken.
Kent, E.L. (2014), “Text analytics – techniques, language and opportunity”, Business Information Review, Vol. 31 No. 1, pp. 50-53.
Khamis, N., Rilling, J. and Witte, R. (2013), “Assessing the quality factors found in in-line documentation written in natural language: the JavadocMiner”, Data & Knowledge Engineering, Vol. 87, September, pp. 19-40.
Klefsjö, B., Bergquist, B. and Garvare, R. (2008), “Quality management and business excellence, customers and stakeholders: do we agree on what we are talking about, and does it matter?”, The TQM Journal, Vol. 20 No. 2, pp. 120-129.
Köksal, G., Batmaz, İ. and Testik, M.C. (2011), “A review of data mining applications for quality improvement in manufacturing industry”, Expert Systems With Applications, Vol. 38 No. 10, pp. 13448-13467.
Kroslid, D. (1999), “In search of quality management: rethinking and reinterpreting”, PhD-dissertation, Linköping University.
Kurgan, L.A. and Musilek, P. (2006), “A survey of knowledge discovery and data mining process models”, The Knowledge Engineering Review, Vol. 21 No. 1, pp. 1-24.
Larose, D.T. (2005), Discovering Knowledge in Data an Introduction to Data Mining, Wiley-Interscience, Hoboken, NJ.
Liu, B. (2011), Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 2nd ed., Springer, Heidelberg.
Lo, Q.Q. and Chai, K.H. (2012), “Quantitative analysis of quality management literature published in total quality management and business excellence (1996-2010)”, Total Quality Management & Business Excellence, Vol. 23 Nos 5/6, pp. 629-651.
Lo, S. (2008), “Web service quality control based on text mining using support vector machine”, Expert Systems with Applications, Vol. 34 No. 1, pp. 603-610.
Marbán, O., Mariscal, G. and Segovia, J. (2009), “A data mining and knowledge discovery process model”, in Ponce, J. and Karahoca, A. (Eds), Data Mining and Knowledge Discovery in Real Life Applications, I-Tech, Vienna, February, pp. 1-16.
Mariscal, G., Marban, O. and Fernandez, C. (2010), “A survey of data mining and knowledge discovery process models and methodologies”, The Knowledge Engineering Review, Vol. 25 No. 2, pp. 137-166.
Martínez-Lorente, A.R., Dewhurst, F. and Dale, B.G. (1998), “Total quality management: origins and evolution of the term”, The TQM Magazine, Vol. 10 No. 5, pp. 378-386.
Meeks, E. and Weingart, S. (2012), “The digital humanities contribution to topic modeling”, Journal of Digital Humanities, Vol. 2 No. 1, pp. 1-6.
Miller, W.J. (1996), “A working definition for total quality management (TQM) researchers”, Journal of Quality Management, Vol. 1 No. 2, pp. 149-159.
Mimno, D. (2012), “Computational historiography: data mining in a century of classics journals”, Journal on Computing and Cultural Heritage (JOCCH), Vol. 5 No. 1, p. 3.
Miner, G. (red.) (2012), Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications, 1st ed., Academic Press, Waltham, MA.
Newman, D., Asuncion, A., Smyth, P. and Welling, M. (2009), “Distributed algorithms for topic models”, Journal of Machine Learning Research, Vol. 10 No. 2009, pp. 1801-1828.
Oakland, J.S. (2014), Total Quality Management and Operational Excellence: Text with Cases, 4th ed., Routledge, New York, NY.
Perla, R.J. and Parry, G.J. (2011), “The epistemology of quality improvement: it’s all Greek”, BMJ Quality and Safety, Vol. 20 No. S1, pp. i24-i27.
Pilkington, A. and Lawton, T.C. (2014), “Divided by a common language? Transnational insights into epistemological and methodological approaches to strategic management research in englishspeaking countries”, Long Range Planning, Vol. 47 No. 5, pp. 299-311.
Rahman, S.U. (2004), “The future of TQM is past. Can TQM be resurrected?”, Total Quality Management & Business Excellence, Vol. 15 No. 4, pp. 411-422.
Rahman, S.U. and Sohal, A.S. (2002), “A review and classification of total quality management research in Australia and an agenda for future research”, International Journal of Quality & Reliability Management, Vol. 19 No. 1, pp. 46-66.
Reinartz, T. (2002), “Stages of the discovery process”, in Klösgen, W. and Zytkow, J.M. (Eds), Handbook of Data Mining and Knowledge Discovery, Oxford University Press, Inc., New York, NY, pp. 185-192.
Rogberg, M. (2006), “Den modeföljande organisationen. Om acceptansen av TQM och andra populära managementmodeller”, PhD-dissertation, Stockholm School of Economics, Stockholm.
Schoengrund, C. (1996), “Aristotle and total quality management”, Total Quality Management, Vol. 7 No. 1, pp. 79-92.
Sebastianelli, R., Tamimi, N. and Iacocca, K. (2015), “Improving the quality of environmental management: impact on shareholder value”, International Journal of Quality & Reliability Management, Vol. 32 No. 1, pp. 53-80.
Senge, P. (1992), “The real message of the quality movement: building learning organizations”, Journal for Quality and Participation, Vol. 15 No. 2, pp. 30-38.
Sila, I. and Ebrahimpour, M. (2002), “An investigation of the total quality management survey based research published between 1989 and 2000: a literature review”, International Journal of Quality & Reliability Management, Vol. 19 No. 7, pp. 902-970.
Singh, P.J. and Smith, A. (2006), “Uncovering the faultlines in quality management”, Total Quality Management & Business Excellence, Vol. 17 No. 3, pp. 395-407.
Sousa, R. and Voss, C. (2002), “Quality management re-visited: a reflective review and agenda for future research”, Journal of Operations Management, Vol. 20 No. 1, pp. 91-109.
Steyvers, M. and Griffiths, T. (2007), “Probabilistic topic models”, Handbook of Latent Semantic Analysis, Vol. 427 No. 7, pp. 424-440.
Stuti, K. and Veenu, M. (2013), “Evaluation of text document clustering approach based on particle swarm optimization”, Central European Journal of Computer Science, Vol. 3 No. 2, pp. 69-90.
Uzuner, S. (2008), “Multilingual scholars’ participation in core/global academic communities: a literature review”, Journal of English for Academic Purposes, Vol. 7 No. 4, pp. 250-263.
Van Der Wiele, A., Williams, A.R.T. and Dale, B.G. (2000), “Total quality management: is it a fad, fashion, or fit?”, ASQ Quality Management Journal, Vol. 7 No. 2, pp. 65-79.
Wallach, H.M., Murray, I., Salakhutdinov, R. and Mimno, D. (2009), “Evaluation methods for topic models”, Proceedings of the 26th Annual International Conference on Machine Learning, ACM, pp. 1105-1112.
Weiss, S.M., Indurkhya, N. and Zhang, T. (2012), Fundamentals of Predictive Text Mining, Springer London, London.
Welling, M., Chemudugunta, C. and Sutter, N. (2008), “Deterministic latent variable models and their pitfalls”, In SDM, pp. 196-207.
Winter, S.G. (1994), “Organizing for continuous improvement: evolutionary theory meets the quality revolution”, in Baum, J.A. and Singh, J.V. (Eds), Evolutionary Dynamics of Organizations, Oxford University Press, pp. 90-108.
Wirth, R. and Hipp, J. (2000), “CRISP-DM: towards a standard process model for data mining”, Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, pp. 29-39.
Wu, J. (2012), Advances in K-Means Clustering: A Data Mining Thinking, Springer Science & Business Media, Heidelberg.
Xie, P. and Xing, E. (2013), “Integrating document clustering and topic modeling”, Uncertainty in Artificial Intelligence – Proceedings of the Twenty-Ninth Conference, Bellevue, WA, August 11-15.
Xu, Q. (2000), “On the way to knowledge: making a discourse at quality”, Organization, Vol. 7 No. 3, pp. 427453.
Zain, Z.M., Dale, B.G. and Kehoe, D.F. (2001), “Total quality management: an examination of the writings from a UK perspective”, The TQM Magazine, Vol. 13 No. 2, pp. 129-137.
Zairi, M. (1994), “TQM: what is wrong with the terminology”, The TQM Magazine, Vol. 6 No. 4, pp. 6-8.
Zbaracki, M.J. (1998), “The rhetoric and reality of total quality management”, Administrative Science Quarterly, Vol. 43 No. 3, pp. 602-636.
Glenisson, P., Glänzel, W., Janssens, F. and De Moor, B. (2005), “Combining full text and bibliometric information in mapping scientific disciplines”, Information Processing & Management, Vol. 41 No. 6, pp. 1548-1572.
Nair, A. (2006), “Meta-analysis of the relationship between quality management practices and firm performance – implications for quality management theory development”, Journal of Operations Management, Vol. 24 No. 6, pp. 948-975.
Newman, D., Chemudugunta, C., Smyth, P. and Steyvers, M. (2006), “Analyzing entities and topics in news articles using statistical topic models”, in Mehrotra, S., Zeng, D., Chen, H., Thuraisingham, B. and Wang, F.Y. (Eds), Intelligence and Security Informatics, Springer, Berlin and Heidelberg, pp. 93-104.
Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P. and Steyvers, M. (2010), “Learning author-topic models from text corpora”, ACM Transactions on Information Systems (TOIS), Vol. 28 No. 1, pp. 1-38.