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The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

In recent years, organizational agility (OA) has garnered significant attention from the academic community. Despite a substantial rise in the academic literature on OA, the nuanced understanding of OA among academicians, practitioners and policymakers is limited. To address this research gap, the current study attempts to synthesize the academic literature on organizational literature, understand the evolution of OA literature and state the potential research gaps that may open multiple research avenues.

The current study critically evaluates academic literature published in peer-reviewed journals using the bibliometric approach to map the intellectual structure of identified 224 articles on published literature on OA between 2001 and 2022.

The findings outline OA's evolutionary trend, most prolific authors, journals, affiliations and countries. Further, network analysis is deployed to unearth prominent OA themes. After that, four key themes of OA from each cluster have been identified and evaluated.

The study is based on the literature drawn from the SCOPUS database. Although the SCOPUS database is one of the largest databases, the authors believe that the SCOPUS does not contain some publications that might have offered some different insights. Secondly, the bibliometric analysis does not offer the opportunity to provide critical insights into published literature, which is one of the main limitations of bibliometric-based studies. However, despite some of these limitations, the authors believe that the study is a useful guide for scholars, practitioners and policymakers who do not have much information related to OA literature.

This article provides a pioneering review of the OA literature using bibliometrics and network analysis. The results and potential directions for further research may assist researchers in increasing the relevance of OA in the current uncertain and ambiguous environment.

The existing literature offers various perspectives on integrating cryptocurrencies into investment portfolios; yet, there is a gap in understanding the behaviours, attitudes and cross-investment links of individual investors. This study, grounded in the modern portfolio theory and the random walk theory, aims to add empirical insights that are specific to the UK context. It explores four hypotheses related to the influence of socio-demographics, digital adoption, cross-investment behaviours and financial attitudes on cryptocurrency owners.

This study uses a logistic regression model with secondary data from the Financial Lives Survey 2020 to assess the factors impacting cryptocurrency ownership. A total of 29 variables are used, categorized into four groups aligned with the hypotheses. Additionally, hierarchical clustering analysis was conducted to further explore the cross-investment links.

The study reveals a significant lack of diversification among UK cryptocurrency investors, a pronounced inclination towards high-risk investments such as peer-to-peer lending and crowdfunding, and parallels with gambling behaviours, including financial dissatisfaction and a propensity for risk-taking. It highlights the influence of demographic traits, risk tolerance, technological literacy and emotional attitudes on cryptocurrency investment decisions.

This study provides valuable insights into cryptocurrency regulation and retail investor protection, underscoring the necessity for tailored financial education and a holistic regulatory approach for investment products with comparable risk levels, with the aim of minimizing regulatory arbitrage. It significantly enhances our understanding of the unique dynamics of cryptocurrency investments within the evolving financial landscape.

The sustainable development goals (SDGs) are introduced to guide achieving the sustainable goals and tackle the global problems. United Nations members may perform activities to achieve the predetermined goals and report on their SDG activities. The comprehension and commitment of several stakeholders are essential for the effective implementation of the SDGs. Countries encourage their stakeholders to perform and report their activities to meet the SDGs. The purpose of this study is to investigate the extent to which corporations’ annual reports address the SDGs to assess and comprehend their level of commitment to, priority of and integration of SDGs within their reporting structure. This research makes it easier to evaluate corporations’ sustainability performance and contributions to global sustainability goals by looking at the extent to which they address the SDGs.

In the study, it is revealed to what extent the reports meet the SDGs with the multilabel text classification approach. The SDG classification is carried out by examining the report with the help of a text analysis tool based on an enhanced version of gradient boosting. The implementation of a machine learning-based model allowed it to determine which SDGs are associated with the company’s operations without the requirement for the report’s authors to perform so. Therefore, instead of reading the texts to seek for “SDG” evidence as typically occurs in the literature, SDG proof was searched in relevant texts.

To show the feasibility of the study, the annual reports of the leading companies in Turkey are examined, and the results are interpreted. The study produced results including insights into the sustainable practices of businesses, priority SDG selection, benchmarking and business comparison, gaps and improvement opportunities identification and representation of the SDGs’ importance.

The findings of the analysis of annual reports indicate which SDGs they are concerned about. A gap in the literature can be noticed in the analysis of annual reports of companies that fall under a particular framework. In addition, it has sparked the idea of conducting research on a global scale and in a time series. With the aid of this research, decision-making procedures can be guided, and advancements toward the SDGs can be achieved.

This paper's purpose is to explore the current interest and knowledge that UK consumers hold around agroforestry. Despite the many reported benefits of agroforestry systems, uptake in the UK, as well as other temperate nations, has been low. As the consumer has a role to play in the transition of agriculture to methods that are more environmentally friendly it is vital to have an understanding of their perceptions. Yet to date no work has looked at agroforestry from the perspective of the UK consumer.

An online survey was conducted using a convenience sample accessed by floating a link through social media and messaging apps. The survey was also shared to the members of a private Facebook group associated with an organic vegetable box service. A mix of multiple choice and open text boxes were used. The survey received 139 responses.

Non-parametric tests indicate that this sample of UK consumers would be mostly likely to buy, and willing to pay more for, agroforestry produce; and the sample showed a split group regarding familiarity. Inductive thematic analysis of the qualitative data highlighted some important barriers to the purchase as well as capturing a snapshot of this sample's perceptions.

This paper presents, to the authors knowledge, the first set of data regarding a sample of UK consumers' perspective of agroforestry produce. The findings could bolster producers' confidence in adopting agroforestry practices, but also highlight the need for policymakers to bolster consumer support through parallel means.

Grounded on the conservation of resources (COR) theory, this study examines the relationship between tyrannical leadership and knowledge hiding. Additionally, this study aims to investigate the mediating role of psychological distress and the moderating role of psychological safety.

Data was gathered from 435 employees in the corporate sector in China. The study used the partial least squares structural equation modelling approach to assess the proposed connections and analysed the data collected with the help of SmartPLS 4 software.

In the study, it was found that there is a positive relationship between tyrannical leadership and knowledge hiding, and this association is mediated by psychological distress. Additionally, the results asserted that the positive effect of tyrannical leadership on knowledge hiding through psychological distress is less pronounced when there is a greater degree of psychological safety.

Leaders should avoid being tyrannical and adopt a supportive leadership style. They should be aware of the effects of their behaviour on employee well-being, provide resources to help employees cope with distress and foster a culture of psychological safety. This approach promotes knowledge sharing, innovation and employee well-being within the organisation.

This study contributes to the existing literature by investigating a new factor that influences knowledge hiding: tyrannical leadership. Furthermore, it explains that employees who experience tyrannical leadership are more prone to psychological distress, such as anxiety and fear, and are likelier to engage in knowledge-hiding behaviours. Finally, the study identifies psychological safety as a factor that can mitigate the negative effects of tyrannical leadership on knowledge hiding.

This paper aims to critically examine the accounting and information systems literature to understand the changes that are occurring in the management accounting profession. The changes the authors are interested in are linked to technology-driven innovations in managerial decision-making and in organizational structures. In addition, the paper highlights research gaps and opportunities for future research.

The authors adopted a grounded theory literature review method (Wolfswinkel et al., 2013) to achieve the study’s aims.

The authors identified four research themes that describe the changes in the management accounting profession due to technology-driven innovations: structured vs unstructured data, human vs algorithm-driven decision-making, delineated vs blurred functional boundaries and hierarchical vs platform-based organizations. The authors also identified tensions mentioned in the literature for each research theme.

Previous studies display a rather narrow focus on the role of digital technologies in accounting work and new competences that management accountants require in the digital era. By contrast, the authors focus on the broader technology-driven shifts in organizational processes and structures, which vastly change how accounting information is collected, processed and analyzed internally to support managerial decision-making. Hence, the paper focuses on how management accountants can adapt and evolve as their organizations transition toward a digital environment.

This research paper aims to investigate the effects of internal audit’s (IA) use of data analytics and the performance of consulting activities on perceived IA quality.

The authors conduct a 2 × 2 between-subjects experiment among upper and middle managers where the use of data analytics and the performance of consulting activities by internal auditors are manipulated.

Results highlight the importance of internal auditor use of data analytics and performance of consulting activities to improve perceived IA quality. First, managers perceive internal auditors as more competent when the auditors use data analytics. Second, managers perceive internal auditors’ recommendations as more relevant when the auditors perform consulting activities. Finally, managers perceive an improvement in the quality of relationships with internal auditors when auditors perform consulting activities, which is strengthened when internal auditors combine the use of data analytics and the performance of consulting activities.

From a theoretical perspective, this research builds on the IA quality framework by considering digitalization as a contextual factor. This research focused on the perceptions of one major stakeholder of the IA function: senior management. Future research should investigate the perceptions of other stakeholders and other contextual factors.

This research suggests that internal auditors should prioritize the development of the consulting role in their function and develop their digital expertise, especially expertise in data analytics, to improve perceived IA quality.

This research tests the impacts of the use of data analytics and the performance of consulting activities on perceived IA quality holistically, by testing Trotman and Duncan’s (2018) framework using an experiment.