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This study aims to explore US hotel guests’ privacy concerns with a twofold aim as follows: to investigate the privacy categories, themes and attributes most commonly discussed by guests in their reviews and to examine the influence of cultural proximity on privacy concerns.

This study combined automated text analytics with content analysis. The database consisted of 68,000 hotel reviews written by US guests lodged in different types of hotels in five European cities. Linguistic Inquiry Word Count, Leximancer and SPSS software were used for data analysis. Automated text analytics and a validated privacy dictionary were used to investigate the reviews by exploring the categories, themes and attributes of privacy concerns. Content analysis was used to analyze the narratives and select representative snippets.

The findings revealed various categories, themes and concepts related to privacy concerns. The two most commonly discussed categories were privacy restriction and outcome state. The main themes discussed in association with privacy were “room,” “hotel,” “breakfast” and several concepts within each of these themes were identified. Furthermore, US guests showed the lowest levels of privacy concerns when staying at American hotel chains as opposed to non-American chains or independent hotels, highlighting the role of cultural proximity in privacy concerns.

Hotel managers can benefit from the results by improving their understanding of hotel and service attributes mostly associated with privacy concerns. Specific suggestions are provided to hoteliers on how to increase guests’ privacy and on how to manage issues related to cultural distance with guests.

This study contributes to the hospitality literature by investigating a neglected issue: on-site hotel guests’ privacy concerns. Using an unobtrusive method of data collection and text analytics, this study offers valuable insights into the categories of privacy, the most recurrent themes in hotel guests’ reviews and the potential relationship between cultural proximity and privacy concerns.

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.

This paper aims at understanding the dynamics underlying toleration as a complex social phenomenon and its pattern on Facebook during the June 30th revolution in Egypt. Thanks to the huge advances in ICT, internet-mediated research (IMR) has become one of the most prominent research methodologies in social sciences. Discussions on social network sites cannot be neglected in studying the dynamics complex and emerging social phenomena such as changes in public opinion, culture, attitudes and virtues.

To fulfill this aim, the researchers used web content analysis as a method inside IMR paradigm to analyze the discussions on Tamarrod’s Facebook page in the period from June 30th to July 5th and to examine the emerging overall pattern of toleration.

The results show indications that toleration is inherent in the Egyptian culture, and that the Egyptian society still keeps its reputation as a highly tolerant society, even in crises periods where tensions are witnessed everywhere. Moreover, the results also show that the web content analysis process proposed in this study is highly reliable and valid.

The importance of the study lies in introducing a computational and empirical approach to analyze web content in a semi-automated way and proving its validity and reliability to study social phenomena such as toleration.

The purpose of this research is to determine firms' sustainability efforts through triple bottom line reporting on the World Wide Web. Sustainability reporting can assist in brand differentiation to stakeholder groups and ultimately lead to a positive corporate reputation.

Automated web content analysis was used to determine and differentiate 39 oil and gas firms' reporting of economic, social and environmental disclosures across Europe, North America and Asia. Firms were benchmarked for their disclosures against key terms derived from the Global Reporting Initiative.

North American firms disclose the greatest amount of TBL information for both environmental and economic indicators. European firms are the most prevalent reporters of social indicators. Asian firms displayed the most positive bias to their sustainability reporting.

Future research would benefit from linking firms' TBL reporting with firm performance as well as including a greater range of countries and industries for comparative purposes.

Firms should demonstrate a greater completeness of information across the three TBL indicators to effectively manage their relationships with their key stakeholders. Information should be unbiased and honest for firms to successfully legitimacy.

This paper uses automated content analyse to differentiate disclosure levels of TBL indicators across three different geographical regions.

Stakeholders and their organizations are increasingly involved in governance of higher education, not only within institutions or at system level, but also in various supra-national and intergovernmental processes. For these, as well as pragmatic reasons (ease of access and relatively simple methods for analysis), this chapter advocates for a more systematic approach to studying stakeholder organizations, their participation in and impact on governance of higher education. Specifically, the chapter: (1) provides a three-fold nested conceptualization of policy positions of stakeholder organizations, comprising issues, preferences concerning these issues, and the normative basis utilized to legitimize said preferences; (2) presents advantages and disadvantages of different methodological approaches to analyzing policy positions of stakeholder organizations, including qualitative and quantitative content analysis, employing either human coding or computer-assisted coding of policy documents; and (3) highlights different insights one can gain from analyzing policy positions of stakeholder organizations. It combines (thus far limited) insights from higher education studies with the more generic literature on interest groups, and uses examples from European level stakeholder organizations to illustrate its points.

News algorithms not only help the authors to efficiently navigate the sea of available information, but also frame information in ways that influence public discourse and citizenship. Indeed, the likelihood that readers will be exposed to and read given news articles is structured into news algorithms. Thus, ensuring that news algorithms uphold journalistic values is crucial. In this regard, the purpose of this paper is to quantify journalistic values to make them readable by algorithms through taking an exploratory approach to a question that has not been previously investigated.

The author matched the textual indices (extracted from natural language processing/automated content analysis) with human conceptions of journalistic values (derived from survey analysis) by implementing partial least squares path modeling.

The results suggest that the numbers of words or quotes news articles contain have a strong association with the survey respondent assessments of their balance, diversity, importance and factuality. Linguistic polarization was an inverse indicator of respondents’ perception of balance, diversity and importance. While linguistic intensity was useful for gauging respondents’ perception of sensationalism, it was an ineffective indicator of importance and factuality. The numbers of adverbs and adjectives were useful for estimating respondents’ perceptions of factuality and sensationalism. In addition, the greater numbers of quotes, pair quotes and exclamation/question marks in news headlines were associated with respondents’ perception of lower journalistic values. The author also found that the assessment of journalistic values influences the perception of news credibility.

This study has implications for computational journalism, credibility research and news algorithm development.

It represents the first attempt to quantify human conceptions of journalistic values with textual indices.

Taking a mixed-method approach, this research developed and validated a novel, value-centric experience framework delineating robotic involvement in the service encounter and its subsequent impact on customers' experiences.

Focused on robotic restaurant businesses where robots are mainly involved in food production processes, this research utilized online customer review data and a multistage, mixed-method design for empirical examination. Automated thematic analysis was first adopted to facilitate a comprehensive understanding of the empirical reality as reflected in online customer reviews. Following an analytic induction process, a theoretical framework was developed integrating prior literature with the empirical reality to uncover the relationships across robotic involvement, experiential values and evaluative outcomes. A theory-driven, dictionary-based content analysis together with path analysis further enabled empirical validation of the developed theoretical framework.

The current research developed and validated a value-centric experience framework to theorize robotic involvement in the service encounter and its downstream impact on customers' experiences. Specifically, this framework conceptualizes robotic involvement as a five-dimensional composition of robotic visibility, competency, performanism, co-creativity and prominence. In addition, the framework specifies seven dimensions of experiential values revolving around high-tech–high-touch robotic service encounters, namely sensorial, utilitarian, hedonic, social, agentic, epistemic and aesthetic values. Following empirical validation, this framework sheds light on robotic involvement and experience design for high-tech–high-touch service businesses aiming to incorporate robots in their service encounters.

Drawing on classic service role theory and consumption value theory, this research developed and validated a novel theoretical model connecting robotic involvement dimensions with experiential consumption values and downstream customer evaluative outcomes. This research and theoretical framework open an exciting avenue for future research in robotic services and customer experiences.

Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.

We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.

Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.

Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

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 present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale.

The authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface.

The study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool.

Interview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections.