Abstract
This study focuses on examining the thematic landscape of the history of scholarly publication in business ethics. We analyze the titles, abstracts, full texts, and citation information of all research papers published in the field’s leading journal, the Journal of Business Ethics, from its inaugural issue in February 1982 until December 2016—a dataset that comprises 6308 articles and 42 million words. Our key method is a computational algorithm known as probabilistic topic modeling, which we use to examine objectively the field’s latent thematic landscape based on the vast volume of scholarly texts. This “big-data” approach allows us not only to provide time-specific snapshots of various research topics, but also to track the dynamic evolution of each topic over time. We further examine the pattern of individual papers’ topic diversity and the influence of individual papers’ topic diversity on their impact over time. We conclude this study with our recommendation for future studies in business ethics research.
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Notes
Authors’ calculation.
Indeed, Arnold et al. (2015) list governance as 15th, and the topic would probably have been ranked even lower were it not bundled, in these authors’ analysis, with agency theory.
Journal of Business Ethics—Springer, link.springer.com/journal/10,551.
For example, the author of a hypothetical paper on the ethics and legality of bribery must necessarily (if not necessarily consciously) decide how much space (or how many words) to allocate to defining bribery, how much space to allocate to questions of the ethics of bribery, and how much space to allocate to the legality of bribery.
Note the statistical analogy with voting systems: a first-past-the-poll voting system in a 2-party jurisdiction can end up allocating 100% of votes to a single party, even if the “other” party got 49% of the votes in every local election. Categorizing papers by “main topic” is effectively a first-past-the-poll voting system. Such systems have their merits, but their results can easily misrepresent the shape of the electorate.
In this paper, we have listed only the twenty words with the highest frequency within each word cluster.
We generate the smoothed lines using locally weighted scatterplot smoothing (LOWESS).
We thank an anonymous reviewer for pointing out this important difference.
See Citizens United v. Federal Election Com'n, 130 S. Ct. 876, 558 U.S. 310, 175 L. Ed. 2d 753 (2010) and Burwell v. Hobby Lobby Stores, Inc., 134 S. Ct. 2751, 573 U.S., 189 L. Ed. 2d 675 (2014), respectively.
The same phrase is allowed to be used in multiple topics, although the weight might be different.
Both are Dirichlet distributions—hence the name of LDA. We can think of a Dirichlet distribution as an urn containing many multi-faced dice. Each die in the urn is different in terms of the probability of its each face showing up in a roll. For the per-document topic distribution, the number of faces is the number of topics. For the per-topic word distribution, the number of faces is the total number of words.
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Acknowledgements
We are grateful to Professor Laura Hartman, the Director of Susilo Institute for Ethics in the Global Economy at Questrom School of Business, Boston University, for her insightful comments and suggestions along the development of this study.
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Appendices
Appendices
Appendix 1: Latent Dirichlet Allocation
We chose the latent Dirichlet allocation (LDA) (Blei et al. 2003) model as our probabilistic topic modeling method. As the first and the most mature topic model, LDA has emerged as a powerful and commonly used technique to reveal thematic information from digital archives (Griffiths and Steyvers 2004). Recently, it has received growing attention in various areas of management research, especially in the study of the historical evolution of ideas. For instance, recent work by Kaplan and Vakili (2015) mined topics in a body of knowledge concerning patents; Wang et al. (2015) employed topic modeling to conduct a historical review of research on consumer behavior; and Chen and Zhao (2015) used topic modeling to study the field of information systems. All of these studies used LDA.
An example will help convey the intuition behind LDA. Figure 7 is the abstract of “Ethical leadership behavior and employee justice perceptions: the mediating role of trust in organizations”—a research paper by Xu et al. (2016). Suppose a dedicated scholar read through every published JBE article and learned all the coherent topics in them. Upon reading the article in Fig. 7, she may identify that three of the topics are addressed here. We highlight these three topics in different colors: blue indicates the topic “ethical leadership”; green indicates the topic “employee ethical behaviors”; and yellow indicates the topic “virtue ethics.” The scholar can also decide the weights Xu et al. put on the three topics in the article. If the three topics are addressed equally in the article, she may annotate (“ethical leadership” = 0.33, “employee ethical behaviors” = 0.33, and “virtue ethics” = 0.34, where all weights must add up to 1.0).
The sheer volume of articles makes human understanding, organizing, and annotating of the entire JBE archive a daunting—if not impossible—task. We use LDA to conduct the above analysis automatically. LDA builds upon the intuition that articles often exhibit multiple topics, and these topics are expressed using different words.Footnote 10 LDA uses a probabilistic model and random variables to express the relationship between documents, topics, and words. The model inference process matches the probabilistic model with the observed JBE archive and provides the outputs we presented in the paper. Next, we introduce the assumptions behind LDA and inference process.
As with all other machine learning models, LDA starts with several statistical assumptions. First, an article is a “bag of words,” meaning that LDA ignores the order of words in an article. The order of the words is, of course, important for readers to understand an article. However, as Blei et al. (2003) have argued, this simplification (i.e., ignoring word order) can result in enhanced computational efficiency while largely preserving the semantic themes in the article. Second, LDA assumes that all articles in the archive share the same set of topics, but the topic proportions differ across articles (from 0 to 100% for any given topic). This assumption is based on the fact that all JBE authors share a body of knowledge, while different authors are experts in various sub-domains. As such, their articles will have different foci. Third, LDA assumes that each topic is manifested in the form of a cluster of topic-related keywords. These topic-related keywords are from fixed vocabulary of the totality of the article archive. Fourth, LDA assumes that the set of words contained in any given article is a combination of topic-related words from a variety of topics. The third and fourth assumptions lead to the generative process of LDA.
LDA is a generative model. The model assumes that the observed JBE articles are generated from a probabilistic process characterized by hidden (latent) random variables. The random variables are hidden from us, but all of the authors know about them. For our purpose, we focus on two sets of hidden random variables. The first is the per-document topic distribution. The parameters of this distribution could tell us, for each article, the likely weights of each topic. The second is the per-topic word distribution, the parameters of which could indicate the most likely words under each topic.Footnote 11
With the two hidden distributions, LDA assumes that the procedure of writing a JBE article (i.e., generating the bag of words in the article) follows two main steps:
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1.
The authors pick the weight distribution of the topics for each article according to the per-document topic distribution. For example, 80% about CSR and 20% supply chain-related issues.
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2.
The authors choose the corresponding bag of words according to how words are related to topics (the per-topic word distribution). For example, use the word “social” 20 times, “stakeholder” 10 times, “contract” 5 times, “retailer” 3 times.
Given the collection of the JBE articles, which are the observable outcomes of the above generative process, our goal of the analysis is to uncover the per-document topic distribution and the per-topic word distribution. The inference algorithm of LDA reverses the above generative process using hierarchical Bayesian inference (Blei 2012). In other words, the algorithm tries to answer: assuming all the articles are indeed generated according to the above two steps, what are the most plausible document-topic distribution and topic-word distribution that gave rise to the observed JBE articles? The inference process gave us the results presented in the paper, namely (1) which topic(s) are covered in a given article and their proportions, and (2) the representative words for each topic.
Lastly, we describe our implementation details. We first downloaded our data from JSTOR Data for Research (dfr.jstor.org). We used gensism (Rehurek and Sojka 2010), an open-source natural language processing package for Python, for preprocessing and LDA inference. In the preprocessing stage, we used lemmatization to remove the inflectional endings of words. We excluded the most common (top 50), rare (less than 10 occurrences), and stop-words following the convention. The most common words are words that are generic terms such as business, ethic, study, research, while the rare words are usually study specific. Neither can help us learn the pervading themes in the articles. We then identified the most frequent bigrams and trigrams (two- and three-word terms) and concatenated them to words. In the LDA inference stage, gensism implements the online variational Bayes algorithm outlined in Bach et al. (2010). We chose the number of topics K = 18 using the perplexity measure, which is commonly used to evaluate a language model’s performance (Asuncion et al. 2009). In addition, the solution presented agrees with our human judgment in terms of semantic coherence and interpretation.
Appendix 2. Gini Coefficients
To analyze topic diversity, we utilize the way of calculating Gini coefficient—a measure usually used for income inequality (Gini 1909). In our analysis, the Gini coefficient is instead used to measure the inequality of the weight across the multiple topics examined by LDA model in a paper. The value of the Gini coefficient varies between “0” and “1”. The Gini coefficient “0” indicates complete equality, meaning the weight of each topic identified by the LDA model is the same. In this case, the diversity is high because all the topics are discussed with the same amount of scholarly attention (extremely spread). The Gini coefficient “1” indicates complete inequality, meaning that only one topic is discussed or gains all the scholarly attention (extremely concentrated). In this case, the diversity is low. Therefore, there is a negative relationship between the Gini coefficient and diversity. To make our analyses and results more intuitive, we transform Gini coefficient to the value of diversity by multiplying the initial Gini coefficient with “− 1”.
Appendix 3. Representative Articles Under Each Topic
Topic | Article title | Authors | Vol. | Iss. | Year |
---|---|---|---|---|---|
Virtue ethics | How virtue fits within business ethics | J. T. Whetstone | 33 | 2 | 2001 |
Virtue theory as a dynamic theory of business | S. Arjoon | 28 | 2 | 2000 | |
Integrating personalism into virtue-based business ethics: the personalist and the common good principles | D. Mele | 88 | 1 | 2009 | |
Aristotelian virtue and business ethics education | S. M. Mintz | 15 | 8 | 1996 | |
The language of managerial excellence: virtues as understood and applied | J. T. Whetstone | 44 | 4 | 2003 | |
Employee ethical behaviors | Organizational dissidence: the case of whistle-blowing | J. P. Near, M. P. Miceli | 4 | 1 | 1985 |
A behavioral model of ethical and unethical decision making | M. Bommer, C. Gratto, J. Gravander, M. Tuttle | 6 | 4 | 1987 | |
An integrative model for understanding and managing ethical behavior in business organizations | W. E. Stead, D. L. Worrell, J. G. Stead | 9 | 3 | 1990 | |
The challenge of ethical behavior in organizations | R. R. Sims | 11 | 7 | 1992 | |
Organizational ethics: a stacked deck | H. R. Smith, A. B. Carroll | 3 | 2 | 1984 | |
Consumer ethics | Consumers’ ethical beliefs: the roles of money, religiosity and attitude toward business | S. J. Vitell, J. J. Singh, J. Paolillo | 73 | 4 | 2007 |
Marketing dataveillance and digital privacy: using theories of justice to understand consumers’ online privacy concerns | L. Ashworth, C. Free | 67 | 2 | 2006 | |
Exploring the structure of ethical attributions as a component of the consumer decision model: the vicarious versus personal perspective | J. Whalen, R. E. Pitts, J. K. Wong | 10 | 4 | 1991 | |
Relational consequences of perceived deception in online shopping: the moderating roles of type of product, consumer’s attitude toward the internet and consumer’s demographics | S. Roman | 95 | 3 | 2010 | |
The end of religion? Examining the role of religiousness, materialism, and long-term orientation on consumer ethics in Indonesia | A. Denni, T. Fandy | 123 | 3 | 2014 | |
Corporate social responsibility | Corporate social responsibility theories: mapping the territory | E. Garriga, D. Mele | 53 | 2 | 2004 |
Concepts and definitions of CSR and corporate sustainability: between agency and communion | M. van Marrewijk | 44 | 3 | 2003 | |
Corporate social responsibility and resource-based perspectives | M. C. Branco, L. L. Rodrigues | 69 | 2 | 2006 | |
Corporate social responsibility (CSR): theory and practice in a developing country context | D. Jamali, R. Mirshak | 72 | 3 | 2007 | |
Measuring corporate social responsibility: a scale development study | D. Turker | 85 | 4 | 2009 | |
Gender | Gender-based barriers to senior management positions: understanding the scarcity of female CEOs | J. G. Oakley | 27 | 4 | 2000 |
Managers, values, and executive decisions: an exploration of the role of gender, career stage, organizational level, function, and the importance of ethics, relationships and results in managerial decision-making | J. H. Barnett, M. J. Karson | 8 | 10 | 1989 | |
Gender differences in managerial careers: yesterday, today, and tomorrow | C. Kirchmeyer | 37 | 1 | 2002 | |
Mentoring in organizations: implications for women | R. J. Burke, C. A. McKeen | 9 | 5 | 1990 | |
Gender and ethical orientation: a test of gender and occupational socialization theories | E. S. Mason, P. E. Mudrack | 15 | 6 | 1996 | |
Accounting ethics | Legislated ethics: from Enron to Sarbanes–Oxley, the impact on corporate America | H. Rockness, J. Rockness | 57 | 1 | 2005 |
Historical perspectives: development of the codes of ethics in the legal, medical and accounting professions | J. F. Backof, C. L. Martin, Jr. | 10 | 2 | 1991 | |
Beyond bean counting: establishing high ethical standards in the public accounting profession | J. R. Cohen, L. W. Pant | 10 | 1 | 1991 | |
Reactions to ethical dilemmas: a study pertaining to certified public accountants | G. A. Claypool, D. F. Fetyko, M. A. Pearson | 9 | 9 | 1990 | |
Cultural and socioeconomic constraints on international codes of ethics: lessons from accounting | J. R. Cohen, L. W. Pant, D. J. Sharp | 11 | 9 | 1992 | |
Marketing ethics | Children and the changing world of advertising | E. S. Moore | 52 | 2 | 2004 |
Persuasive advertising, autonomy, and the creation of desire | R. Crisp | 6 | 5 | 1987 | |
The ethics of psychoactive ads | M. R. Hyman, R. Tansey | 9 | 2 | 1990 | |
Ethical dimensions of advertising executions | I. D. Nebenzahl, E. D. Jaffe | 17 | 7 | 1998 | |
Advertising and behavior control | R. L. Arrington | 1 | 1 | 1982 | |
Ethical decision making | Ethical decision making: a review of the empirical literature | R. C. Ford, W. D. Richardson | 13 | 3 | 1994 |
A review of the empirical ethical decision-making literature: 1996–2003 | M. J. O’Fallon, K. D. Butterfield | 59 | 4 | 2005 | |
A review of empirical studies assessing ethical decision making in business | T. W. Loe, L. Ferrell, P. Mansfield | 25 | 3 | 2000 | |
The effects of culture on ethical decision-making: an application of Hofstede’s typology | S. J. Vitell, S. L. Nwachukwu, J. H. Barnes | 12 | 10 | 1993 | |
Judging the morality of business practices: the influence of personal moral philosophies | D. R. Forsyth | 11 | 6 | 1992 | |
Ethics education | Concerns of college students regarding business ethics | R. F. Beltramini, R. A. Peterson, G. Kozmetsky | 3 | 3 | 1984 |
Ethics, CSR, and sustainability education in the “financial times” top 50 global business schools: baseline data and future research directions | L. J. Christensen, E. Peirce, L. P. Hartman, W. M. Hoffman, J. Carrier | 73 | 4 | 2007 | |
Measuring the impact of teaching ethics to future managers: a review, assessment, and recommendations | J. Weber | 9 | 3 | 1990 | |
Do company ethics training programs make a difference? An empirical analysis | J. T. Delaney, D. Sockell | 11 | 9 | 1992 | |
Designing and delivering business ethics teaching and learning | R. R. Sims, E. L. Felton, Jr. | 63 | 3 | 2006 | |
Corporate personhood | The concept of corporate responsibility | K. E. Goodpaster | 2 | 1 | 1983 |
Self-interest and business ethics: some lessons of the recent corporate scandals | T. L. Carson | 43 | 4 | 2003 | |
Corporate Environmental Responsibility | J. DesJardins | 17 | 8 | 1998 | |
A critical perspective of integrative social contracts theory: recurring criticisms and next generation research topics | T. W. Dunfee | 68 | 3 | 2006 | |
A social contract account for CSR as an extended model of corporate governance (i): rational bargaining and justification | L. Sacconi | 68 | 3 | 2006 | |
International development | Public policies on corporate social responsibility: the role of governments in Europe | L. Albareda, J. M. Lozano, T. Ysa | 74 | 4 | 2007 |
The global compact selected experiences and reflections | G. Kell | 59 | 2 | 2005 | |
Child labor and multinational conduct: a comparison of international business and stakeholder codes | A. Kolk, R. van Tulder | 36 | 3 | 2002 | |
Corporate social responsibility for developing country multinational corporations: Lost war in pertaining global competitiveness? | P. Gugler, J. Y. J. Shi | 87 | S1 | 2009 | |
Corporate governance and institutional transparency in emerging markets | C. C. J. M. Millar, T. I. Eldomiaty, C. J. Choi, B. Hilton | 59 | 2 | 2005 | |
Qualitative case studies | Ethical attitudes of students and business professionals: a study of moral reasoning | J. A. Wood, J. G. Longenecker, J. A. McKinney, C. W. Moore | 7 | 4 | 1988 |
A comparison of five business philosophies | P. Miesing, J. F. Preble | 4 | 6 | 1985 | |
Student perceptions of ‘job politics’ as practised by those climbing the corporate career ladder | M. M. Pressley, D. E. Blevins | 3 | 2 | 1984 | |
Plane truth: a qualitative study of employee dishonesty in the airline industry | E. D. Scott | 42 | 4 | 2003 | |
Real estate sales agents and the code of ethics: a voice stress analysis | D. E. Allmon, J. Grant | 9 | 10 | 1990 | |
Managerial morality | Everyday moral issues experienced by managers | J. A. Waters, F. Bird, P. D. Chant | 5 | 5 | 1986 |
The ethics of insider trading | P. H. Werhane | 8 | 11 | 1989 | |
What is really unethical about insider trading? | J. Moore | 9 | 3 | 1990 | |
The nature of managerial moral standards | F. Bird, J. A. Waters | 6 | 1 | 1987 | |
Predictors of ethical decisions regarding insider trading | D. E. Terpstra, M. G. C. Reyes, D. W. Bokor | 10 | 9 | 1991 | |
Doing well by doing good | The relationship between corporate social performance, and organizational size, financial performance, and environmental performance: an empirical examination | P. A. Stanwick, S. D. Stanwick | 17 | 2 | 1998 |
An empirical investigation of the relationship between change in corporate social performance and financial performance: a stakeholder theory perspective | B. M. Ruf, K. Muralidhar, R. M. Brown, J. J. Janney, K. Paul | 32 | 2 | 2001 | |
The association between corporate social-responsibility and financial performance: the paradox of social cost | M. L. Pava, J. Krausz | 15 | 3 | 1996 | |
The link between corporate social and financial performance: evidence from the banking industry | W. G. Simpson, T. Kohers | 35 | 2 | 2002 | |
Corporate social and financial performance: an investigation in the UK supermarket industry | G. Moore | 34 | 4 | 2001 | |
Ethical leadership | Toward an understanding of ethical climate: Its relationship to ethical behavior and supervisory influence | J. C. Wimbush, J. M. Shepard | 13 | 8 | 1994 |
Leaders, values, and organizational climate: examining leadership strategies for establishing an organizational climate regarding ethics | M. W. Grojean, C. J. Resick, M. W. Dickson, D. B. Smith | 55 | 3 | 2004 | |
The virtuous influence of ethical leadership behavior: evidence from the field | M. J. Neubert, D. S. Carlson, K. M. Kacmar, J. A. Roberts, L. B. Chonko | 90 | 2 | 2009 | |
A cross-cultural examination of the endorsement of ethical leadership | C. J. Resick, P. J. Hanges, M. W. Dickson, J. K. Mitchelson | 63 | 4 | 2006 | |
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Impacts of corporate code of conduct on labor standards: a case study of Reebok’s athletic footwear supplier factory in China | X. Yu | 81 | 3 | 2008 | |
Fair trade: three key challenges for reaching the mainstream | A. Hira, J. Ferrie | 63 | 2 | 2006 | |
What do corporations have to do with fair trade? Positive and normative analysis from a value chain perspective | D. Reed | 86 | S1 | 2009 | |
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The limits of shareholder value | P. Koslowski | 27 | 2 | 2000 | |
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Appendix 4: Topical Trend of Journal of Business Ethics and Business Ethics Quarterly
See Fig. 8.
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Liu, Y., Mai, F. & MacDonald, C. A Big-Data Approach to Understanding the Thematic Landscape of the Field of Business Ethics, 1982–2016. J Bus Ethics 160, 127–150 (2019). https://doi.org/10.1007/s10551-018-3806-5
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DOI: https://doi.org/10.1007/s10551-018-3806-5