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This paper aims to examine how competitive intelligence (CI) relates to the strategy formulation process of firms.

Due to the novelty of the phenomenon and to the depth of the investigation required to grasp the mechanisms and logics of CI, a multiple case study has been performed related to four companies located in Brazil that adopted CI practices within dedicated business units to inform and support strategic decision-making.

The authors provide detailed empirical evidence on the connection and use of CI practices throughout each stage of the strategy formulation process. Moreover, the study suggests that CI practices, despite their strategic relevance and diffusion, are still extensively adopted for tactical use.

This study sheds light on how CI practices may inform, support, and be integrated in the strategy formulation process, as few studies have done before.

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 study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

Due to the recent development of Big Data and artificial intelligence (AI) technology solutions in customer relationship management (CRM), this paper provides a systematic overview of the field, thus unveiling gaps and providing promising paths for future research.

A total of 212 peer-reviewed articles published between 1989 and 2020 were extracted from the Scopus database, and 2 bibliometric techniques were used: bibliographic coupling and keywords’ co-occurrence.

Outcomes of the bibliometric analysis enabled the authors to identify three main subfields of the AI literature within the CRM domain (Big Data and CRM as a database, AI and machine learning techniques applied to CRM activities and strategic management of AI–CRM integrations) and capture promising paths for future development for each of these subfields. This study also develops a three-step conceptual model for AI implementation in CRM, which can support, on one hand, scholars in further deepening the knowledge in this field and, on the other hand, managers in planning an appropriate and coherent strategy.

To the best of the authors’ knowledge, this study is the first to systematise and discuss the literature regarding the relationship between AI and CRM based on bibliometric analysis. Thus, both academics and practitioners can benefit from the study, as it unveils recent important directions in CRM management research and practices.

The purpose of this paper is clearly illustrate this convergence and the prescriptive recommendations that such documents entail. There is a significant amount of research into the ethical consequences of artificial intelligence (AI). This is reflected by many outputs across academia, policy and the media. Many of these outputs aim to provide guidance to particular stakeholder groups. It has recently been shown that there is a large degree of convergence in terms of the principles upon which these guidance documents are based. Despite this convergence, it is not always clear how these principles are to be translated into practice.

In this paper, the authors move beyond the high-level ethical principles that are common across the AI ethics guidance literature and provide a description of the normative content that is covered by these principles. The outcome is a comprehensive compilation of normative requirements arising from existing guidance documents. This is not only required for a deeper theoretical understanding of AI ethics discussions but also for the creation of practical and implementable guidance for developers and users of AI.

In this paper, the authors therefore provide a detailed explanation of the normative implications of existing AI ethics guidelines but directed towards developers and organisational users of AI. The authors believe that the paper provides the most comprehensive account of ethical requirements in AI currently available, which is of interest not only to the research and policy communities engaged in the topic but also to the user communities that require guidance when developing or deploying AI systems.

The authors believe that they have managed to compile the most comprehensive document collecting existing guidance which can guide practical action but will hopefully also support the consolidation of the guidelines landscape. The authors’ findings should also be of academic interest and inspire philosophical research on the consistency and justification of the various normative statements that can be found in the literature.

Advances in Big Data, artificial Intelligence and data-driven innovation bring enormous benefits for the overall society and for different sectors. By contrast, their misuse can lead to data workflows bypassing the intent of privacy and data protection law, as well as of ethical mandates. It may be referred to as the ‘creep factor’ of Big Data, and needs to be tackled right away, especially considering that we are moving towards the ‘datafication’ of society, where devices to capture, collect, store and process data are becoming ever-cheaper and faster, whilst the computational power is continuously increasing. If using Big Data in truly anonymisable ways, within an ethically sound and societally focussed framework, is capable of acting as an enabler of sustainable development, using Big Data outside such a framework poses a number of threats, potential hurdles and multiple ethical challenges. Some examples are the impact on privacy caused by new surveillance tools and data gathering techniques, including also group privacy, high-tech profiling, automated decision making and discriminatory practices. In our society, everything can be given a score and critical life changing opportunities are increasingly determined by such scoring systems, often obtained through secret predictive algorithms applied to data to determine who has value. It is therefore essential to guarantee the fairness and accurateness of such scoring systems and that the decisions relying upon them are realised in a legal and ethical manner, avoiding the risk of stigmatisation capable of affecting individuals’ opportunities. Likewise, it is necessary to prevent the so-called ‘social cooling’. This represents the long-term negative side effects of the data-driven innovation, in particular of such scoring systems and of the reputation economy. It is reflected in terms, for instance, of self-censorship, risk-aversion and lack of exercise of free speech generated by increasingly intrusive Big Data practices lacking an ethical foundation. Another key ethics dimension pertains to human-data interaction in Internet of Things (IoT) environments, which is increasing the volume of data collected, the speed of the process and the variety of data sources. It is urgent to further investigate aspects like the ‘ownership’ of data and other hurdles, especially considering that the regulatory landscape is developing at a much slower pace than IoT and the evolution of Big Data technologies. These are only some examples of the issues and consequences that Big Data raise, which require adequate measures in response to the ‘data trust deficit’, moving not towards the prohibition of the collection of data but rather towards the identification and prohibition of their misuse and unfair behaviours and treatments, once government and companies have such data. At the same time, the debate should further investigate ‘data altruism’, deepening how the increasing amounts of data in our society can be concretely used for public good and the best implementation modalities.

This study examines the effect of resources (e.g. tangible resources, human skills and intangible resources) that are utilized as a bundle of standard practices on sustainable net zero economy implementation and their further impact on financial, environmental and social performance among small- and medium-level enterprises in business markets. The moderating effect of big data analytical intelligence is also examined.

The samples were selected from the paper and chemical manufacturing industries of South Africa. The data analysis was performed using variance-based structural equation modeling.

The results show that tangible resources, human skills and intangible resources positively influence sustainable net zero economy adoption. However, intangible resources have a more substantial influence on sustainable net zero economy implementation. This shows that adopting a sustainable net zero economy depends more on a bundle of common practices, including sustainability culture, employee training and knowledge management, and managers must create the necessary action plans accordingly. In addition, sustainable net zero economy adoption positively influences financial performance, environmental performance and social performance. However, sustainable net zero economy adoption has a more substantial influence on social performance. Therefore, implementing a net zero economy will be more advantageous to society and to local communities.

To achieve a sustainable net zero economy, managers should recognize the significance of resource management. While managing tangible resources and human skills is crucial, intangible resources, such as culture and organizational learning, require more attention. Additionally, the ability of small- and medium-sized enterprises to explore, store, share and apply knowledge is crucial to achieving net zero. Therefore, managers should make use of Industry 4.0-based digital technologies for effective knowledge management. Moreover, net zero economy adoption can significantly enhance societal performance. Hence, while making budgeting decisions, managers must consider the potential of the firm's resources to improve social performance.

This study is the first to investigate the impact of human skills and tangible and intangible resources on the adoption of a sustainable net zero economy by companies, using empirical evidence. The research expands on the concept of the practice-based view (PBV) in the implementation of sustainable net zero economies by small- and medium-sized business-to-business enterprises.

The paper presents a research methodology that could be used to carry out a systematic literature review on the current state of the art of the technological development in the field of the digitalization and unphysicalization of supply chains (SCs). A three-dimensional conceptual framework focusing on the relationship between Digital Technologies (DTs), business processes and SC performance is presented. The study identifies the emerging practices and areas of SC management that could be positively affected by the implementation of DTs. With this in mind, the emerging practices have a high probability to be considered future best practices.

A systematic literature review was conducted on DTs in SC management. The methodology used aims to algorithmically and objectively standardize the information incorporated into thousands of scientific documents. Selected papers were analyzed to investigate the recent literature on SC digitalization and unphysicalization. A total of 87 DTs were selected to be analyzed and subsequently grouped into 11 macro-categories. 17 business processes linked to SC management are taken into account and 17 different impacts on SC management are presented. From a set of 1,585 papers, 5,060 emerging practices were collected and singularly summarized combining DT, business process and impact on SC performance.

A unique analytical perspective provided represents an important evolution when trying to organize the current literature on SC management. The widely used DTs in the practices and the most considered business processes and impacts are highlighted and described. The three-dimensional conceptual framework is graphically represented to allow for the emergence of the best combinations of DT, business process and impact on SC performance. These combinations suggest the most promising areas for the implementation of the emerging practices for SC digitalization and unphysicalization. Additional findings identify and define the most important contexts in which Big Data contributes to SC performance.

The research methodology used is offering progress through which to systemize the current practices as well as detect the potential of digitalization and unphysicalization under the three-dimensional conceptual framework. The paper provides a structured proposal for promising future research directions, assuming that the five research gaps as findings of this research could be the basis for prescriptions, as well as a future research agenda and theory development. Moreover, this research contributes to current managerial issues concerning SC management, referred to data and information management, efficiency and productivity of SC processes, market performance, SC relationship management and risk management in SC.

The data economy mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human experiences into behavioral data that can be harnessed in the marketing sphere. This study aims to focus on investigating the domain of data economy with the methodological lens of quantitative bibliometric analysis of published literature.

The bibliometric analysis seeks to unravel trends and timelines for the emergence of the data economy, its conceptualization, scientific progression and thematic synergy that could predict the future of the field. A total of 591 data between 2008 and June 2021 were used in the analysis with the Biblioshiny app on the web interfaced and VOSviewer version 1.6.16 to analyze data from Web of Science and Scopus.

This study combined findable, accessible, interoperable and reusable (FAIR) data and data economy and contributed to the literature on big data, information discovery and delivery by shedding light on the conceptual, intellectual and social structure of data economy and demonstrating data relevance as a key strategic asset for companies and academia now and in the future.

Findings from this study provide a steppingstone for researchers who may engage in further empirical and longitudinal studies by employing, for example, a quantitative and systematic review approach. In addition, future research could expand the scope of this study beyond FAIR data and data economy to examine aspects such as theories and show a plausible explanation of several phenomena in the emerging field.

The researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies.

This study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.