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1 – 10 of over 3000Helen Cripps, Abhay Singh, Thomas Mejtoft and Jari Salo
The purpose of this research is to investigate the use of Twitter in business as a medium for knowledge sharing and to crowdsource information to support innovation and enhance…
Abstract
Purpose
The purpose of this research is to investigate the use of Twitter in business as a medium for knowledge sharing and to crowdsource information to support innovation and enhance business relationships in the context of business-to-business (B2B) marketing.
Design/methodology/approach
This study uses a combination of methodologies for gathering data in 52 face-to-face interviews across five countries and the downloaded posts from each of the interviewees' Twitter accounts. The tweets were analysed using structural topic modelling (STM), and then compared to the interview data. This method enabled triangulation between stated use of Twitter and respondent's actual tweets.
Findings
The research confirmed that individuals used Twitter as a source of information, ideas, promotion and innovation within their industry. Twitter facilitates building relevant business relationships through the exchange of new, expert and high-quality information within like-minded communities in real time, between companies and with their suppliers, customers and also their peers.
Research limitations/implications
As this study covered five countries, further comparative research on the use of Twitter in the B2B context is called for. Further investigation of the formalisation of social media strategies and return on investment for social media marketing efforts is also warranted.
Practical implications
This research highlights the business relationship building capacity of Twitter as it enables customer and peer conversations that eventually support the development of product and service innovations. Twitter has the capacity for marketers to inform and engage customers and peers in their networks on wider topics thereby building the brand of the individual users and their companies simultaneously.
Originality/value
This study focuses on interactions at the individual level illustrating that Twitter is used for both customer and peer interactions that can lead to the sourcing of ideas, knowledge and ultimately innovation. The study is novel in its methodological approach of combining structured interviews and text mining that found the topics of the interviewees' tweets aligned with their interview responses.
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Jacob Mickelsson, Joep J.G.M. van Haren and Jos G.A.M. Lemmink
Corporate social responsibility (CSR) is an increasingly important issue for service brands in fast fashion retailing, as consumers' negative impressions about retailers' CSR…
Abstract
Purpose
Corporate social responsibility (CSR) is an increasingly important issue for service brands in fast fashion retailing, as consumers' negative impressions about retailers' CSR activities influence brand experience. Consumers' impressions of CSR efforts arise based on agendas communicated through many channels from different sources. The paper unravels the ‘wrinkles’, i.e. possible mismatches in CSR communication around service brands by studying differences between the three main sources of fast fashion brand-related CSR agendas: Autonomous company communication, news media and social media postings by consumers.
Design/methodology/approach
The authors use structural topic modeling (STM) to analyze a corpus of texts focusing on the CSR efforts of three major fast fashion service brands over three years. The texts included 89 items of company communication (CSR reports and press releases), 5,351 news media articles about the brands' CSR efforts and 57,377 consumer generated tweets about the brands.
Findings
The STM analysis extracted 26 different CRS-related topics from the texts. Results showed differences in how much the three sources emphasized topics. The brands' own communication puts emphasis on environmental responsibility. News media tended to report on economic issues, treatment of employees and specific CSR-related events. Twitter showed more activity in discussing incident-based and emotionally charged topics.
Research limitations/implications
The results feed into the ongoing discussion about how companies' CSR communication relates to communication in the press and among consumers. The authors highlight themes in the individual topics that are emphasized by the three sources, and discuss how CSR themes emerge in the overall transformative agenda.
Practical implications
The paper highlights how fast fashion service brands can identify and understand different CSR agendas arising around their brand. Insight into such agendas can be used to tailor the brands' communication strategies.
Originality/value
The paper contributes to the understanding of the factors behind fashion service brands' CSR reputation, highlighting how the three main sources of CSR reputation (company reports, news and social media) emphasize different types of agendas.
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In identifying both the topics of interest and key limitations of the extant organizational security research, both opportunities for future research as well as some underlying…
Abstract
Purpose
In identifying both the topics of interest and key limitations of the extant organizational security research, both opportunities for future research as well as some underlying challenges for conducting this research may be revealed.
Design/methodology/approach
To identify the leading organizational cybersecurity research topics of interest and their key limitations, the author conducted a topic modeling analysis of the organizational level studies published in the Association for Information Systems (AIS) senior scholars' “basket of eight journals” (Association for Information Systems, 2022) over the past five years.
Findings
Leading topics include (1) organizational security research concerns governance and strategic level decision-making and their role in shaping organizational security successes and failures, (2) cybercriminals and organizations' ability to monitor and detect them from both within and outside the firm; (3) cost, liability and security negligence, (4) organizations' innovation dispositions for security products and services and (5) organizational breach response efficacy; while key limitations of this study include the following: (1) scholars' ability to propose and assess strategic and operational level threat response recommendations, (2) their understanding how influence is formed and maintained among employees and groups and (3) their measurement instruments and models.
Originality/value
Organizations remained plagued by an ever-emerging set of threats to the security of their digital and informational assets. New threats are regularly discovered and remedies to existing threats are continually proven ineffective against these new threats. Providing an orientation to the current research on organizational security can help advance their security efforts.
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Federico Barravecchia, Luca Mastrogiacomo and Fiorenzo Franceschini
Digital voice-of-customer (digital VoC) analysis is gaining much attention in the field of quality management. Digital VoC can be a great source of knowledge about customer needs…
Abstract
Purpose
Digital voice-of-customer (digital VoC) analysis is gaining much attention in the field of quality management. Digital VoC can be a great source of knowledge about customer needs, habits and expectations. To this end, the most popular approach is based on the application of text mining algorithms named topic modelling. These algorithms can identify latent topics discussed within digital VoC and categorise each source (e.g. each review) based on its content. This paper aims to propose a structured procedure for validating the results produced by topic modelling algorithms.
Design/methodology/approach
The proposed procedure compares, on random samples, the results produced by topic modelling algorithms with those generated by human evaluators. The use of specific metrics allows to make a comparison between the two approaches and to provide a preliminary empirical validation.
Findings
The proposed procedure can address users of topic modelling algorithms in validating the obtained results. An application case study related to some car-sharing services supports the description.
Originality/value
Despite the vast success of topic modelling-based approaches, metrics and procedures to validate the obtained results are still lacking. This paper provides a first practical and structured validation procedure specifically employed for quality-related applications.
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Dean Neu and Gregory D. Saxton
This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social…
Abstract
Purpose
This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.
Design/methodology/approach
A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.
Findings
The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.
Research limitations/implications
These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.
Originality/value
The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.
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Milad Soltani, Alexios Kythreotis and Arash Roshanpoor
The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning…
Abstract
Purpose
The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research.
Design/methodology/approach
In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.
Findings
This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.
Research limitations/implications
In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.
Originality/value
Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.
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Abderahman Rejeb, Karim Rejeb, Andrea Appolloni, Yasanur Kayikci and Mohammad Iranmanesh
The purpose of this study is to investigate the structure and dynamics of academic articles relating to public procurement (PP) in the period 1984–2022 (up to May). The…
Abstract
Purpose
The purpose of this study is to investigate the structure and dynamics of academic articles relating to public procurement (PP) in the period 1984–2022 (up to May). The researchers also intend to analyse how this knowledge domain has grown since 1984.
Design/methodology/approach
A bibliometric analysis was carried out to examine the existing state of PP research. Based on 640 journal articles indexed in the Scopus database and written by 1,247 authors over nearly four decades, a bibliometric analysis was conducted to reveal the intellectual structure of academic works pertaining to PP.
Findings
Findings reveal that PP research from Scopus has significantly increased in the past decade. Major journals publishing PP research are International Journal of Procurement Management, Journal of Cleaner Production, Journal of Purchasing and Supply Management and Public Money and Management. Results also indicate that authors’ cooperation network is fragmented, showing limited collaboration among PP researchers. In addition, results suggest that the institutional collaboration network in PP research mirrors what is commonly referred to as the North–South divide, signifying insufficient research collaboration between developed and developing countries’ institutions. According to the co-occurrence keyword network and topic modelling, PP revolves around five main themes, including innovation, corruption, sustainable and green PP, PP contracts and small and medium enterprises. Based on these results, several directions for future research are suggested.
Social implications
This paper provides an increased understanding of the entire PP field and the potential research directions.
Originality/value
To the best of the authors’ knowledge, this study is the first-ever application of bibliometric techniques and topic modelling to examine the development of PP research since 1984 based on scholarly publications extracted from the Scopus database.
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Isabel Botero, Giuseppe Pedeliento, Cristina Bettinelli and Edgar Centeno-Velázquez