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Today, companies are struggling to develop their human resources analytics (HRA) capability, although interest in the subject is rapidly increasing. Furthermore, the academic literature on the subject is immature with limited practical guidance or comprehensive models that could support organisations in the development of their HRA capability. To address this issue, the aim of this paper is to provide a maturity model – i.e. HRAMM – and an interdependency matrix through which an organisation can (1) operationalise its HRA capability and assess its organisational maturity; (2) generate harmonious development roadmaps to improve its HRA capability; and (3) enable benchmarking and continuous improvement.

The research described in this paper is based on the popular methodology proposed by Becker et al. (2009) and the procedure for maturity evaluation developed by Gastaldi et al. (2018). This method combines academic rigour and field experience in analytics, in a process spanning eight main phases that involves literature reviews and knowledge creation techniques.

We define HRA maturity through four areas and 14 dimensions, providing a comprehensive model to operationalise HRA capability. Additionally, we argue that HRA maturity develops through an evolutionary path described in four discrete stages of maturity that go beyond traditional analytics sophistication. Lastly, the interdependency matrix reveals specific enablers for the development of HRA.

This paper provides practitioners with useful tools to monitor, evaluate and plan their HRA development path. Additionally, our research helps practitioners to prioritise their work and investment, generating an effective roadmap for developing and improving their HRA capability.

To the best of the authors’ knowledge, this study is the first to provide a model for evaluating the maturity of HRA capability plus an interdependency matrix to evaluate systematically the prerequisites and synergies among its constituting dimensions.

The surge of artificial intelligence (AI) applications and subsequent adoption by consumers and marketers has ignited substantial research exploring the benefits and opportunities of AI. Despite this, little attention has been given to its unintended negative consequences. In this paper, the authors examine both the practitioner and academic sides of ethical AI. In doing so, the authors conduct an extensive review of the AI literature to identify potential issues pertaining to three areas: individual consumers, societal and legal. The authors identify gaps and offer questions to drive future research.

The authors review recent academic literature on AI in marketing journals, and top ethical principles from three top technology developers (Google, IBM and Meta) in conjunction with media reports of negative AI incents. They also identify gaps and opportunities for future research based on this review.

The bibliographic review reveals a small number of academic papers in marketing that focus on ethical considerations for AI adoption. The authors highlight concerns for academic researchers, marketing practitioners and AI developers across three main areas and highlight important issues relating to interactive marketing.

This paper highlights the under-researched negative outcomes of AI adoption. Through an extensive literature review, coupled with current responsible AI principles adopted by major technology companies, this research provides a framework for examining the dark side of AI.

This study aims to investigate the impact of brain drain on government AI readiness in EU member countries, considering the distinctive governance characteristics, macroeconomic conditions and varying levels of ICT specialists.

The research employs a dynamic panel data model using the System Generalized Method of Moments (GMM) to analyze the relationship between brain drain and government AI readiness from 2018 to 2022. The study incorporates various control variables such as GDP per capita growth, government expenditure growth, employed ICT specialists and several governance indicators.

The results indicate that brain drain negatively affects government AI readiness. Additionally, the presence of ICT specialists, robust governance structures and positive macroeconomic indicators such as GDP per capita growth and government expenditure growth positively influence AI readiness.

Major limitations include the focus on a specific region of countries and the relatively short period analyzed. Future research could extend the analysis with more comprehensive datasets and consider additional variables that might influence AI readiness, such as the integration of AI with emerging quantum computing technologies and the impact of governance reforms and international collaborations on AI readiness.

The theoretical value of this study lies in providing a nuanced understanding of how brain drain impacts government AI readiness, emphasizing the critical roles of skilled human capital, effective governance and macroeconomic factors in enhancing AI capabilities, thereby filling a significant gap in the existing literature.

This research fills a significant gap in the existing literature by providing a comprehensive analysis of the interaction between brain drain and government AI readiness. It uses control variables such as ICT specialists, governance structures and macroeconomic factors within the context of the European Union. It offers novel insights for policymakers to enhance AI readiness through targeted interventions addressing brain drain and fostering a supportive environment for AI innovation.

Export market orientation can be broadly divided into intelligence (generation and dissemination) and responsiveness activities. Although previous studies assess intelligence and responsiveness activities, little is known about what type of international channel partner acts as an enabling condition for the impact of these activities on export venture performance. This study aims to examine the extent to which the selection of international channel partners through word-of-mouth referrals versus direct contacts affects the benefits of intelligence and responsiveness activities.

Data were collected from 246 exporting manufacturers in Japan. To test the hypotheses, we conducted regression analyses using a subjective performance measure at the venture level. We also performed a post hoc analysis using objective performance measure at the function level.

We find that the extent to which international channel partners are selected through word-of-mouth referrals has a moderating role in the export market-oriented activities–performance linkages. Specifically, it acts as an enabling condition for intelligence activity and a disenabling condition for responsiveness activity.

This study contributes to a better understanding of export market orientation by classifying it into intelligence and responsiveness activities and providing empirical evidence on their different interaction effects with partner selection. It also contributes to the elaboration of agency theory by offering insights into the fit between task characteristics and contract type. Our study is critical for business managers as it suggests guidelines for manufacturing exporters engaging in export market-oriented behaviors and export channel management.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

Although intellectual capital (IC) and human dynamic capabilities (HDCs) play a significant role in decarbonization processes, their measurement and reporting is under-researched. Hence, this study aims to identify the link between HDCs, carbon accounting and integrated reporting (IR) in the transition processes, investigating IC and HDCs in decarbonization processes to achieve net-zero business models (n-ZBMs).

A systematic literature review with a concise bibliometric analysis is conducted on 229 articles, published from 1990 to 2023 in Scopus database and Google Scholar. Reviewing data on publications, journals, authors and citations and analysing the article content, this study identifies the main search trends, providing a new conceptual model and future research propositions.

The results reveal that the literature has rarely focussed on carbon accounting in terms of IC and HDCs. Additionally, firms face pressure from institutions and stakeholders regarding legitimacy and transparency, necessitating a response considering IR and requiring n-ZBMs to be developed through IC and HDCs to meet social and environmental requirements.

Not only does this study link IC with HDCs to address carbon emissions through decarbonization practices, which has never been addressed in the literature to date, but also provides novel recommendations and propositions through which firms can sustainably transition to being net-zero emission firms, thereby gaining competitive advantage and contributing to the nation’s sustainability goals.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

There is a knowledge gap regarding the determinants of open innovation processes and outcomes in a joint value creation context, as well as what role artificial intelligence (AI) and data management play in facilitating open innovation processes. One strategy to better understand joint value creation through open innovation, supported by AI and data management, is to conduct studies on the digital business ecosystem (DBE). The purpose of this paper is to improve our current knowledge of this urgent issue in contemporary management through the lens of an ecosystem-based theory by conducting an empirical study on two DBEs (called ecosystem micro-communities (EMCs)), developed by Haier, as well as multiple literature reviews on the key concepts “Haier EMC” and “digital business ecosystem”.

By building on multiple literature reviews and empirical data from a multi-year and ongoing research program driven by Haier, this study examines Haier’s EMC model for AI-driven DBEs. Secondary data were collected through iterative literature reviews on DBEs, the EMC concept and the two selected EMC cases. The empirical data were collected through a qualitative study of two Haier EMCs in China.

Haier's ecosystem micro-community concept represents a radical shift towards a more flexible, responsive and innovative cross-industry organizational structure, offering valuable lessons for business leaders and scholars. Haier’s ecosystem micro-community model, part of their RenDanHeYi philosophy and here viewed as a DBE, is a pioneering management concept that not only redefines the management of the firm and the traditional corporate structure, but also the traditional view on innovation management, business strategy, human resource management and marketing (customer centricity). The concept has therefore an important and big impact on traditional management. For scholars, the gap in understanding innovation processes in open business ecosystems is addressed by the concept. However, the concept also opens new areas for academic research, particularly in innovation management, business strategy, human resource management and marketing. The concepts further encourage more interdisciplinary research.

The DBE is a relatively new research area that will need more research. While the EMC model is promising as an effective version of a DBE, its effectiveness across different industries and organizational cultures needs to be explored further. Future research should investigate its applicability and impact in diverse business environments. To understand the EMC’s long-term impact, longitudinal studies are needed. These should focus on the sustained competitive advantages, potential market disruptions and the evolution of customer value propositions over time. Finally, considering increasing concerns about data privacy and security, future research should also explore how DBEs solve the issue of data protection and IP while promoting open innovation and value sharing.

For managers and practitioners, the EMC concept could inspire leaders to learn how to foster innovation by creating smaller, autonomous teams that can respond quickly to market changes in the form of a DBE. The concepts exemplify how value creation and capture could be enhanced for any company and even could be a new strategy in the company’s digital transformation and repositioning into a more competitive, high-end player on the market. The concept also emphasizes employee empowerment and ownership, which can lead to higher job satisfaction and retention rates. The concept can further improve companies’ adaptability and resilience by decentralizing decision-making. Finally, the micro-communities allow businesses to be more customer-centric, developing products and services that better meet specific customer needs.

The social implications could be positive, as complex social problems commonly need an ecosystem approach to develop and deliver impactful solutions. In addition, Haier’s ecosystem micro-community model seems inherently scalable and culturally adaptable.

Haier’s EMC model is well-known in the research literature and is a novel approach to DBEs, which has been proven successful and replicable in different countries and industries. Providing insights from multiple literature reviews and two unique Haier EMC cases will contribute to a better understanding of highly effective data- and AI-driven business ecosystems, as well as of determinants of open innovation processes and outcomes in a joint value creation context, as well as what role AI and data management play in facilitating open innovation processes.

Artificial intelligence (AI) technologies have led to significant transformations across industries and society, including the field of education. The integration of AI in educational settings has the potential to improve students' learning experience and support their individual competencies when paired with non-AI methods. Despite the growing importance of AI in modern education, there remains a noticeable research gap regarding its use in entrepreneurship education and the effects of Chatbots on students' entrepreneurial competencies. To address this gap, an exploratory study was conducted on undergraduate students who were tasked with using ChatGPT to improve their business model canvas.

The chosen methodology aligned with the research purpose, aiming to explore the relationship between Generative AI and competencies. Due to the novel nature of the research problem, an exploratory study was conducted using a mixed methods approach. A survey with open- and closed-ended questions was designed, and statistical and text analyses were performed to interpret data and test identified propositions.

The findings of this study indicate that ChatGPT can enhance the types of students' entrepreneurial competencies considered in this study: spotting opportunities, creativity, vision, valuing ideas and ethical and sustainable thinking. The results show that ChatGPT can be particularly helpful to improve the ability of students of valuing ideas.

Overall, this study highlights the potential of adopting ChatGPT in experiential learning methodologies for enhancing students' entrepreneurial competencies and improving their learning outcomes.