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This literature review is on advanced computer analytics, which is a major trend in the field of Human Resource Management (HRM). The authors focus specifically on computer-assisted text analysis (CATA) because text data are a prevalent yet vastly underutilized data source in organizations. The authors gathered 341 articles that use, review, or promote CATA in the management literature. This review complements existing reviews in several ways including an emphasis on CATA in the management literature, a description of the types of software and their advantages, and a unique emphasis on findings in employment. This examination of CATA relative to employment is based on 66 studies (of the 341) that bear on measuring constructs potentially relevant to hiring decisions. The authors also briefly consider the broader machine learning literature using CATA outside management (e.g., data science) to derive relevant insights for management scholars. Finally, the authors discuss the main challenges when using CATA for employment, and provide recommendations on how to manage such challenges. In all, the authors hope to demystify and encourage the use of CATA in HRM scholarship.

The purpose of this study is to investigate the link between downsizing cues and market performance prior to and after a major crisis. We use a recent exogenous shock – the COVID-19 pandemic – to test hypotheses on the nature of the relationship between downsizing cues and market performance within two distinct groups: pre and post-crisis. We purport that the sudden increase in uncertainty brought about by a major crisis widens information asymmetry between firms and their shareholders, and that top managers sending downsizing cues to the market with high levels of authenticity may be more likely to trigger positive market reactions.

The authors rely on computer-assisted text analysis (CATA) methodology, event-study methodology and a data set of 952 pre- and post-crisis earnings conference calls held by 476 S&P 500 firms to test the hypotheses in this study.

The authors find that downsizing cues have no effect on market performance in the pre-crisis group, but are negatively related to market performance in the post-crisis group. The authors also find that authenticity cues positively mitigate the relationship between downsizing cues and market performance relationship in the post-crisis group.

This empirical study extends our knowledge of the influence of a major crisis on the relationship between downsizing and market performance by leveraging the revelatory power of an exogenous environmental shock. The authors also explore the role played by managerial authenticity and find that the market is more inclined to accept post-crisis downsizing efforts when top managers are perceived as authentic.

This paper aims to investigate whether cues of morality can mitigate stock sell-offs in the face of earnings uncertainty prior to earnings conference calls and draws on moral foundations theory to study the effect of universal moral cues (harm/care and fairness/reciprocity rhetoric) and primarily conservative moral cues (ingroup/loyalty, authority/respect and purity/sanctity rhetoric) on market performance.

The study relies on a longitudinal data set of 1,920 firm-quarter observations corresponding to calls held by firms listed on the S&P 500 in 2015 and relies on computer-assisted-text-analysis and event-study methodology to test hypotheses.

The results suggest that cues of universal moral foundations have a mitigating effect on stock sell-offs and are able to create firm value; while cues primarily conservative moral foundations are not found to have an effect on market performance.

This investigation highlights why earnings conference calls may serve as a valuable tool for communicating a firm’s moral inclination and why universal morality may appeal to a wider range of shareholders than primarily conservative morality.

This study aims to demonstrate the application of computer-aided text analysis (CATA) software in identifying primary associations and impressions of a specified tourist destination.

The Leximancer software is applied on primary information to analyze the concepts evoked by a destination. Because no specific planning has been done for destination image marketing strategies for rural tourism in Andalusia, this study visualizes and determines clusters of the main attributes associated with this destination.

The analysis identifies the main clusters among associations and impressions of the destination that can be useful in developing strategies.

Only a target segment is studied, with a relatively small sample size.

Leximancer can not only be applied to online user-generated content, but primary information can also be mapped to generate a holistic destination image. Furthermore, identification of the relevant attributes and impressions can serve to identify unique assets to help tourism organizations develop a destination.

Several implications concerning destination marketing are outlined.

Although previous studies have applied Leximancer and other CATA software, the present research uses a new approach. Deriving the primary information on destination image using an unstructured methodology, the concepts evoked by a destination are mapped. Because there is a lack of research on rural tourism in Andalusia and its destination image, its associated attributes are studied.

本论文展示如何使用CATA分析软件来确定一个具体旅游目的地的主要关系和印象。

Leximancer软件主要用于分析一个目的地引发的相关概念。因为安达卢西亚至今未有确定的乡村旅游目的地形象营销策略, 本论文视觉化和决定与此目的地相关的主要因素群。

研究结果指出了此安达卢西亚目的地的主要相关群和印象群, 这些对于指定战略计划有很大帮助。

本论文只研究了单一市场群, 分析样本量较小。

Leximancer不仅可以用来分析用户生成内容, 还可以分析主要信息, 以展示整体旅游目的地形象。此外, 分析指出的相关因素和印象群可以用来确立独特的资源组合, 帮助旅游机构开发旅游目的地。

本论文结构指出了多个旅游目的地营销的相关启示

尽管文献中有关Leximancer和其他CATA软件的使用文章, 本论文创立了新的使用方法。基于对旅游目的地形象的主要信息进行非结构性研究, 本论文对于旅游目的地的概念进行展开论述。由于至今未有针对安达卢西亚的乡村旅游研究以及旅游目的地形象研究, 本研究论述了其相关因素。

关键词 安达卢西亚、旅游目的地形象、旅游目的地因素、CATA软件、Leximancer

A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational research. However, these best practices are currently scattered across several methodological and empirical manuscripts, making it difficult for scholars new to the technique to implement DBCTA in their own research. To better equip researchers looking to leverage this technique, this methodological report consolidates current best practices for applying DBCTA into a single, practical guide. In doing so, we provide direction regarding how to make key design decisions and identify valuable resources to help researchers from the beginning of the research process through final publication. Consequently, we advance DBCTA methods research by providing a one-stop reference for novices and experts alike concerning current best practices and available resources.

Content analysis is a methodology that has been used in many academic disciplines as a means to extract quantitative measures from textual information. The purpose of this paper is to document the use of content analysis in the supply chain literature. The authors also discuss opportunities for future research.

The authors conduct a literature review of 13 leading supply chain journals to assess the state of the content analysis-based literature and identify opportunities for future research. Additionally, the authors provide a general schema for and illustration of the use of content analysis.

The findings suggest that content analysis for quantitative studies and hypothesis testing purposes has rarely been used in the supply chain discipline. The research also suggests that in order to fully realize the potential of content analysis, future content analysis research should conduct more hypothesis testing, employ diverse data sets, utilize state-of-the-art content analysis software programs, and leverage multi-method research designs.

The current research synthesizes the use of content analysis methods in the supply chain domain and promotes the need to capitalize on the advantages offered by this research methodology. The paper also presents several topics for future research that can benefit from the content analysis method.

Joint ventures (JVs) are known to create value for their parent firms, in part due to the mutually beneficial sharing of information that occurs at the JV level. Market orientation (MO) is a well-documented strategic orientation that has received little attention in the JV literature, despite considerable research suggesting that MO has a positive effect on performance. This study posits that the MO skills contributed to a new JV by parent firms are likely to play a central role in a shareholder's assessment of the potential for success of a newly announced JV, thereby triggering changes in market value for parent firms.

Computer-Assisted-Text-Analysis (CATA) is used to calculate MO heterogeneity from annual reports, and event-study methodology is used to assess parent firm performance. The authors rely on a US sample of 82 public JV parents involved in 41 new equally-weighted JV formation announcements.

The authors find that heterogeneity on MO's behavioral components (customer orientation, competitor orientation, and coordination) is negatively related to parent performance, while heterogeneity on MO's profitability component is positively related to parent performance. However, the effect of MO's long-term focus heterogeneity on parent performance was not supported.

The results suggest that the benefits of information sharing in partnerships may be of a nuanced nature when it comes to MO. Although heterogeneity in profitability inclination created value for parent firms announcing a new JV; heterogeneity in customer, competitor and coordination market orientations did not appear to be rewarded by shareholders.

The purpose of this paper is to evaluate the effectiveness of a novel method for performing content analysis in managerial research – crowdsourcing, a system where geographically distributed workers complete small, discrete tasks via the internet for a small amount of money.

The authors examined whether workers from one popular crowdsourcing marketplace, Amazon's Mechanical Turk, could perform subjective content analytic tasks involving the application of inductively generated codes to unstructured, personally written textual passages.

The findings suggest that anonymous, self-selected, non-expert crowdsourced workers were applied content codes efficiently and at low cost, and that their reliability and accuracy compared to that of trained researchers.

The authors provide recommendations for management researchers interested in using crowdsourcing most effectively for content analysis, including a discussion of the limitations and ethical issues involved in using this method. Future research could extend the findings by considering alternative data sources and coding schemes of interest to management researchers.

Scholars have begun to explore whether crowdsourcing can assist in academic research; however, this is the first study to examine how crowdsourcing might facilitate content analysis. Crowdsourcing offers several advantages over existing content analytic approaches by combining the efficiency of computer-aided text analysis with the interpretive ability of traditional human coding.

This paper aims to provide details of a study on the widely shared definition of e‐government and to help scholars – especially young scholars – to understand the scope and meaning of the field.

From 1998‐2007, a ten‐year time‐span, 632 articles from the three world‐leading academic databases, including Wiley InterScience, Elsevier ScienceDirect, and SCI Expanded, were retrieved and 324 were analyzed using CATA software (Concordance 3.20), to identify the vocabulary that was used frequently by e‐government scholars. Then the distinct vocabulary was used to construct the widely shared definition of e‐government.

In those 324 articles, 57 words generated from the text analysis formed the basis for imputing a widely shared definition of the field of e‐government. The definition was conceptualized by six elements.

Two limitations of the pool of articles selected may be noted. First, articles were drawn from three leading academic databases in an effort to distinguish e‐government from other fields; but such an approach omitted any consideration of how e‐government definitions varied from different academic fields. Second, because the pool of articles was drawn only from these three, journals excluded by these databases were thus omitted.

The study is unique in that it discusses the definition of e‐government by an exploratory approach. The universal shared definition extracted could serve as either a screen or a magnet for future research. And the methodology could be applied to several academic fields, including administration and management, library and information science, e‐records management, computer science, etc.

Lean culture has been noted to be an underdeveloped concept. The purpose of this paper is to increase the understanding of Lean culture by determining its leading cultural clusters.

Content analysis was used to perform top relevant keywords exploration and qualitative analysis on main text of 33 reference books, 21 Lean generic and 12 Lean healthcare, consolidated as three cases (Lean general, Lean Liker et al. and Lean healthcare).

Four emergent Lean’s leading cultural clusters: operations, change, collectivity and humanity were identified inductively from ten 10 relevant keywords, namely, in order of importance: work, time, process, Lean, system, improvement, production, patient, people and team. Saliency of the word “time” is noteworthy. Cross-validation of these cultural clusters is demonstrated through sociotechnical systems theory.

Content analysis is shown to be an effective research method enabling inductive analysis. Identification of four leading clusters should support productive further research on Lean culture.

The four cultural clusters indicate to healthcare and other domains managers, who wish to improve their Lean cultural transformation success rate, to focus their attention to what their organization actually does (operations), to how improvement happens (change) and to how everything (collectivity) and everyone (humanity) work together in their organization.

This work applies innovative content analysis on Lean reference books. It highlights the importance of time as an underappreciated Lean culture element. It provides evidence and additional support for link between Lean and sociotechnical systems theory.