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This study aims to unveil within the current academic literature the principal directions in the ethnic entrepreneurship and small business marketing research context.

An automated literature analysis procedure was undertaken, attempting to cover all literature published on the subject since 1962. A total of 188 articles were analysed using text mining and topic modelling.

The results show a lack of framing of ethnic entrepreneurship literature outside the narrower scope of migration. Some core themes were found (e.g. network, diversity) around which several other themes orbit, including both related issues to the ethnic factor (e.g. barriers and minorities) and managerial issues (e.g. marketing and production).

Ethnic minority business and small business marketing research has seen a growing number of publications. However, a careful review of existing work is missing.

The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research.

On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided.

The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services.

This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods.

The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience.

The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).

The authors have systematically reviewed 534 corporate social responsibility communication (CSRC) papers, updating the current debate about the ontological and epistemological paradigms that characterize the field, and providing evidence of the interactions between these paradigms and the related methodological choices. The purpose of this paper is to provide theoretical and methodological implications for future research in the CSRC research domain.

The authors used the Scopus database to search for titles, abstracts and related keywords with two queries sets relating to corporate social responsibility (e.g. corporate ethical, corporate environmental, social responsibility, corporate accountability) and CSRC (e.g. reporting, disclosure, dialogue, sensemaking). The authors identified 534 empirical papers (2000–2016), which the authors coded manually to identify the research methods and research designs (Creswell, 2013). The authors then developed an ad hoc dictionary whose keywords relate to the three primary CSRC approaches (instrumental, normative and constitutive). Using the software Linguistic Inquiry and Word Count, the authors undertook an automated content analysis in order to measure these approaches’ relative popularity and compare the methods employed in empirical research.

The authors found that the instrumental approach, which belongs to the functionalist paradigm, dominates the CSRC literature with its relative weight being constant over time. The normative approach also belongs to the functionalist paradigm, but plays a minor yet enduring role. The constitutive approach belongs to the interpretive paradigm and grew slightly over time, but still remains largely beyond the instrumental approach. In the instrumental approach, many papers report on descriptive empirical analyses. In the constitutive approach, theory-method relationships are in line with the various paradigmatic traits, while the normative approach presents critical issues. Regarding methodology, according to the findings, the literature review underlines three major limitations that characterize the existing empirical evidence and provides avenues for future research. While multi-paradigmatic research is promoted in the CRSC literature (Crane and Glozer, 2016; Morsing, 2017; Schoeneborn and Trittin, 2013), the authors found no empirical evidence.

This is the first paper to systematically review empirical research in the CSRC field and is also the first to address the relationship between research paradigms, theoretical approaches, and methods. Further, the authors suggest a novel way to develop systematic reviews (i.e. via quantitative, automated content analysis), which can now also be applied in other literature streams and in other contexts.

The reliance on teams in today’s work environment underscores the importance of understanding how teams function. To better understand teams, one must be able to measure team dynamics or interaction. The purpose of this chapter is to outline an unobtrusive approach to measuring team dynamics from verbal communications.

The basic premise of this approach is that the words we use provide insight into how we feel and think at any given time. The methodology described in this chapter employs a lexical analytic approach to examining team dynamics. To best accomplish this, we first identify the principal features or dimensions of teamwork and then we propose lexical measures that may map to these processes.

This approach can be employed to track team functioning over time “at a distance” without interrupting task performance.

This chapter describes an approach to measuring relevant teamwork dimensions through verbal content. This approach has the potential to give us direct, unobtrusive insight into the emotional and cognitive states of teams. It is original in its examination of how team dynamics can be indexed in speech.

The purpose of this paper is to investigate to what extend an automated, algorithm-based analysis of online conversations of stakeholders in social media and other Internet media can be used for reputation management.

Examination of the reputation of the 5,000 companies with the largest number of employees in Germany based on communication with these companies in 350m online sources on the German-speaking Internet within one year. The method is grounded on an adapted reputation model based on Fombrun.

The central result of the study is the identification of the ideal balance between the different dimensions leading to the best overall reputation. The resulting correlation matrix with the respective correlation coefficients (according to Pearson) thus forms the basis for the optimal reputation architecture.

The discovered “optimal reputation architecture” refers to a German context. Future studies should investigate in how far the adapted model and the “optimal reputation architecture” also work for other cultures. It can be assumed that there may be differences as different dimensions, for example, sustainability, may have a different importance in other cultural contexts. Apart from the question if the “optimal reputation architecture” is also valid for other cultural contexts, the concept has to be validated for German companies as well as it is just based on the two described studies.

The method used shows that social listening can deliver valuable results for research in the field of reputation management as it expands the possibilities to investigate reputation on a large scale. The approach shows in how far scientific research can be expanded beyond classic content analysis as the number of items which can be analysed exceeds that of classic analytical approaches by far. Explicit and implicit experiences, which are the drivers of reputation, can be systematically recorded and analysed using social listening, thus delivering valuable insights in how stakeholders perceive the performance of a company in different dimensions.

Measuring the reputation on the basis of social listening is very important for practical applications in companies, because the data is available digitally and can deliver up-to-date reputation values almost in real time – so that the communication can be aligned very quickly with current events. This makes it easier to implement and control the interaction between companies and their environment in the digital space.

The classic approach in reputation management is traditional market research. It is relatively expensive and takes a relatively long time to produce results. Reputation management based on social listening digitises reputation measurement, lowers costs and delivers results in a very timely manner. It might be the future of reputation measurement. This is relevant not only for practical purposes but also for scientific approaches.

The purpose of this study is to diagnose the trajectory of influential conceptual articles in developing a research stream. The authors uncover the knowledge diffusion through influential conceptual articles and identify characteristics that make conceptual articles influential in their field.

This study draws on scientometrics, specifically an integrated approach combining quantitative citation counts with qualitative citation practices analysis that offers a comprehensive understanding of the nature and context of citations. The authors use the case of customer engagement – a prominent contemporary marketing and service research stream – to explore the trajectory of influential articles in shaping a new research stream.

This research shows that influential articles contribute to the reciprocal knowledge diffusion within and outside their home discipline. They provide anchor points for conceptual framing, conceptual refining and conceptual reconciliation – three application patterns of citations that are pivotal to navigate theory discovery and theory justification in a research field.

The study analyzes the early impact period of two influential customer engagement articles to understand the developments leading to the establishment of a new research stream. Future research drawing on automated citation and bibliometric methods may consider extended time periods.

This study traces the trajectory of influential articles in marketing and service research. The authors identify characteristics of influential conceptual articles, and recommend practices to develop a conceptual paper with the potential for an influential trajectory. It shows that while marketing and service research has a tradition of “borrowing” theories from other fields, seminal articles “lend” theories to other fields.

This work aims to examine the communication on Twitter of the most responsible companies in Spain to identify the topics covered on corporate social responsibility (CSR) from the perspective of happiness and social marketing. In addition, the profiles of the messages that show an association with the impact of the messages have been identified.

An empirical analysis of the Twitter posts of Spain's ten most responsible companies has been carried out. The methodology of this work combines data mining techniques, sentiment analysis and content analysis, both from a quantitative and qualitative approach.

The results show that most brand tweets do not deal with CSR-related topics. The topics they address the most are those related to sports and the weather. From the perspective of social marketing, conversational-type tweets are the most published and have achieved the most significant reaction from the public. In addition, four messages' profiles have been identified based on the company and the emotional connotation associated with the impact, giving rise to more outstanding promotion of social causes.

Our main contribution to this work has been to value positive communication and social marketing to promote better CSR on Twitter. In this sense, it has been verified that there is a relationship between the public's reaction, the affective connotation and the company that issues the messages.

This paper aims to investigate the characteristics and differences in sentiment expression in movie review documents from four online opinion genres – blog postings, discussion board threads, user reviews, and critic reviews.

A collection of movie review documents was harvested from the four types of web sources, and a sample of 520 movie reviews were analysed to compare the content and textual characteristics across the four genres. The analysis focused on document and sentence length, part‐of‐speech distribution, vocabulary, aspects of movies discussed, star ratings used and multimedia content in the reviews. The study also identified frequently occurring positive and negative terms in the different genres, as well as the pattern of responses in discussion threads.

Critic reviews and blog postings are longer than user reviews and discussion threads, and contain longer sentences. Critic reviews and blogs contain more nouns and prepositions, whereas discussion board and user reviews have more verbs and adverbs. Critic reviews have the largest vocabulary and also the highest proportion of unique terms not found in the other genres. The most informative sentiment words in each genre are provided in the paper. With regard to content, critic reviews are more comprehensive in coverage, and discuss the movie director much more often than the other genres. User reviews discuss the scene aspects (including action and visual effects) more often than the other genres, while blogs tend to talk about the cast, and discuss the music and sound slightly more often.

The study only analysed movie review documents. Similar content and text analysis studies can be carried out in other domains, such as commercial product reviews, celebrity reviews, company reviews and political opinions to compare the results.

The main contribution of the study is the sentiment content analysis results across genres, which show the similarities and differences in content and textual characteristics in the four online opinion genres. The insights will be useful in designing automatic sentiment summarisation methods for multiple online genres.

This paper aims to introduce the emerging artificial-intelligence-based readability metrics (Coh-Metrix) to examine the effects of firm size on audit proposal readability.

Coh-Metrix readability measures use emerging computation linguistics technology to better assess document readability. These metrics measure co-relations of words, sentences and paragraphs on multi-dimensions rather than adopting the unidimensional “bag of words” approach that examines words in isolation. Using eight Coh-Metrix orthogonal principal component factors, the authors analyze the Chang and Stone (2019) data set comprised of 370 hand-collected audit proposals submitted by audit firms for the US state and local governments’ audit service contracts.

Audit firm size has a significant impact on the readability of audit proposals. Specifically, as measured by the traditional readability metric, the proposals from smaller firms are more readable than those submitted by larger firms. Furthermore, decomposed readability metrics indicate that smaller firm proposals evidence stronger (deep) text cohesion, whereas larger firm proposals evidence a stronger narrative structure and higher connectivity (relational indicators) among proposal elements. Unlike the traditional readability metric, however, the emergent readability metrics are uncorrelated with auditor selection.

Work remains to develop and validate Coh-Metrix measures that are specific to the context of accounting and auditing practice. Future research can use emerging readability measures to examine various textual features (e.g. text cohesion) in finance or accounting related documents.

The results provide practitioners with insight into the proposal writing strategies and practices of larger and smaller firms. In addition, the results highlight the differing audit firm selection outcomes from traditional and Coh-Metrix readability metrics.

This study introduces new data and holistic readability measures to the auditing literature.

Drawing upon a contractarian lens of corporate social responsibility (CSR), this study aims to explore community construals of happiness and evaluates conceptual boundaries of CSR for happiness.

Using a mixed-methods design, natural language processing and thematic analysis techniques were used to analyse large volumes of textual survey data collected from over 1,000 research participants through an online survey.

Results indicated that lay construals of happiness were primarily defined in terms of socioeconomic conditions and psychoemotional experiences. In explicating the boundary conditions, community perceptions regarding the extent of businesses’ social responsibilities for happiness were evidenced in five themes: that businesses have a responsibility not to harm happiness, a responsibility to enable conditions for happiness to occur, a responsibility to exercise awareness of happiness implications in decision-making, a responsibility for happiness that is limited by strategic purpose and resource capability and a responsibility for happiness that is limited by stakeholder proximity.

This study contributes to the theoretical and empirical foundation of CSR for happiness while simultaneously developing and applying a novel approach for processing and analysing large volumes of qualitative survey-based data.