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Predicting customers’ purchase behaviors is a challenging task. The literature has introduced the individual-level and the segment-based predictive modeling approaches for this purpose. Each method has its own advantages and drawbacks, and performs in certain cases. The purpose of this paper is to propose a hybrid approach which predicts customers’ individual purchase behaviors and reduces the limitations of these two methods by combining the advantages of them.

The proposed hybrid approach is established based on individual-level and segment-based approaches and utilizes the historical transactional data and predictive algorithms to generate predictions. The effectiveness of the proposed approach is experimentally evaluated in the domain of supermarket shopping by using real-world data and using five popular machine learning classification algorithms including logistic regression, decision trees, support vector machines, neural networks and random forests.

A comparison of results shows that the proposed hybrid approach substantially outperforms the individual-level and the segment-based approaches in terms of prediction coverage while maintaining roughly comparable prediction accuracy to the individual-level method. Moreover, the experimental results demonstrate that logistic regression performs better than the other classifiers in predicting customer purchase behavior.

The study concludes that the proposed approach would be beneficial for enterprises in terms of designing customized services and one-to-one marketing strategies.

This study is the first attempt to adopt a hybrid approach combining individual-level and segment-based approaches to predict customers’ individual purchase behaviors.

The purpose of this paper is to propose a new RFM model called length, recency, frequency, monetary and periodicity (LRFMP) for classifying customers in the grocery retail industry; and to identify different customer segments in this industry based on the proposed model.

This study combines the LRFMP model and clustering for customer segmentation. Real-life data from a grocery chain operating in Turkey is used. Three cluster validation indices are used for optimizing the number of groups of customers and K-means algorithm is employed to cluster customers. First, attributes of the LRFMP model are extracted for each customer, and then based on LRFMP model features, customers are segmented into different customer groups. Finally, identified customer segments are profiled based on LRFMP characteristics and for each customer profile, unique CRM and marketing strategies are recommended.

The results show that there are five different customer groups and based on LRFMP characteristics, they are profiled as: “high-contribution loyal customers,” “low-contribution loyal customers,” “uncertain customers,” “high-spending lost customers” and “low-spending lost customers.”

This research may provide researchers and practitioners with a systematic guideline for effectively identifying different customer profiles based on the LRFMP model, give grocery companies useful insights about different customer profiles, and assist decision makers in developing effective customer relationships and unique marketing strategies, and further allocating resources efficiently.

This study contributes to prior literature by proposing a new RFM model, called LRFMP for the customer segmentation and providing useful insights about behaviors of different customer types in the Turkish grocery industry. It is also precious from the point of view that it is one of the first attempts in the literature which investigates the customer segmentation in the grocery retail industry.

This paper presents the design and implementation of collaborative data science framework (CoDS), a knowledge management system for consolidating data science activities in an enterprise.

The development of the CoDS framework is grounded on the design science research methodology for information systems research. In our case study, we first designed the initial framework for CoDS based on a systematic literature review. Then, we collected the expert opinions of eight data scientists to validate the need for generic content for such a knowledge management system. In the second iteration, a portfolio prototype is developed by the same data scientists as a part of our technical action research. Finally, a survey is conducted with 57 data analyst candidates in the last iteration.

Using the CoDS portfolio strengthened the communication among data scientists and stakeholders to improve development and scaling activities. It eased the reuse or modification of existing analytical solutions in other company processes.

The CoDS presents a platform on which business details, data-related knowledge, modeling procedures and deployment steps are shared for (1) mediating and scaling ongoing projects, (2) enriching knowledge transfer among stakeholders, (3) facilitating ideation of new products and (4) supporting the onboarding of new employees and developers.

This study proposes a novel structure and a roadmap for creating a data science knowledge management system for the collaboration of all stakeholders in an enterprise.

The purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way.

This paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed.

It was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management.

This paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations.

Scholars and practitioners acknowledge that digital leadership can help organizations gain a competitive advantage. This article focuses on the characteristics, styles and skills needed for effective digital leadership. It looks at the role of digital leaders in innovating business models and introducing organizational change required for a successful digital transformation.

This paper is based on a comprehensive literature review of digital transformation, digital leadership, business model innovation, and organizational culture. It draws on institutional theory (INT) the neo-institutional theory (NIT). It draws from Science Direct, Web of Science and Google Scholar publications. A proposition and a conceptual framework are developed based on evaluating and synthesizing the literature.

We find that specific leader characteristics (agility, participative, innovativeness and openness), styles (democratic and transformational) and skills (cognitive, social, technological and digital) enable successful business model innovation and organizational change, all of which allow successful digital transformation of firms.

The literature on digital transformation has not been well integrated with the leadership literature. This is particularly true in terms of the role digital leaders play in the successful digital transformation of firms. The conceptual framework and a way forward proposed in this paper introduce future research directions on the topic.

Although digital transformation (DT) has emerged as an important phenomenon for both research and practices, the influences remain inconclusive and inadequate. The emerging artificial intelligence (AI) technologies further complicate the understanding and practices of DT while understudied yet. To address these concerns, this study takes a process perspective to empirically investigate when and how digital-intelligence transformation can improve firm performance, aiming to enrich the literature on digital-intelligence transformation and strategic information systems (IS) field.

Drawing on the dynamic capability view and business agility, we took a process perspective to conceptualize and empirically examine the influence of digital-intelligence transformation and the process characteristics. Taking a continuous panel dataset of listed Chinese firms covering 2007 to 2020, we investigated digital-intelligence transformation’s effect on firm performance and the moderating roles of three strategic aspects: pace, scope and rhythm.

This study found that digital-intelligence transformation positively affects firm performance and is moderated by the characteristics of transformation processes (i.e. pace, scope and rhythm). Specifically, the high-paced and rhythmic transformation processes facilitate the positive relationship, while the large scope undermines the benefits of transformation. These relationships hold across various endogeneity and heterogeneity analyses.

Our findings provide valuable implications for digital-intelligence transformation and strategic IS field. First, this study enriches existing literature on digital-intelligence transformation by empirically investigating the influence from a process perspective. Moreover, this study provides insights into a comprehensive understanding of the complexity of digital-intelligence transformation and the influences of AI. Finally, this study provides practical implications on how to make digital-intelligence transformation to benefit firm performance.

This paper aims to point to the shift in the temporal orientation, going from reporting on the past to creating insights about the future, which might be suggestive of perennial managerial attempts to push the boundaries of bounded rationality.

In this essay, the authors want to critically engage with the concept of “data-driven management” in the context of digitalization. To do so, they sketch the edges of current discourses around the emerging idea of data-driven management and its relationship with the inner workings of organizations from an accounting perspective. They question the often-times supposed objectivity and increased rationality of the concept and instead introduce the idea of becoming “data-curious” (before being data-driven).

The authors observe that this push also seems to be accompanied by trends of individualized decision-making and prevailing hopes of technology to solve organizational problems. They therefore suggest that it is valuable for current debates to take a moment to give attention, in practice and in research, to the role of temporality, benefits of collective decision-making and changes in professions (of accountants).

The aim of this paper is to spark curiosity and engagement with the phenomenon of data-driven management by outlining a novel set of potential future pathways of research and point towards methods that might help studying the questions arising for a data-curious approach.

As the global financial ecosystem grapples with the complexities of modernization, blockchain technology emerges as a pivotal catalyst, offering the banking, financial services, and insurance (BFSI) industry unprecedented opportunities for secured digital transformation and enhanced customer trust. To gain a comprehensive understanding of blockchain technology adoption, this study aims to identify the factors and establish the contextual interrelationships among them.

The authors have identified the factors affecting blockchain technology adoption in BFSI industry through extensive literature review and experts’ interviews. After identification of factors, contextual relationship has been established based on experts’ opinion and total interpretive structural modeling (TISM) approach. Furthermore, factors are categorized into autonomous, dependent, linkage and driving variables using cross-impact matrix multiplication applied to classification analysis.

The TISM-based structural model is divided into eight different hierarchal levels in which Government support is placed on the lower most layer (level 8) which indicates that this is the most crucial factor in blockchain adoption. Further social influence and security are placed on seventh and sixth level in the hierarchy.

The results of this study will help the policymakers to direct the resources from the most crucial factor to other factors in the hierarchy as per their relevance. In essence, this study serves as a guiding compass, steering the course of blockchain technology adoption in the BFSI sector toward a more secure and digitally transformed future.

In the current landscape, blockchain technology remains in its nascent stage, leaving ample room for exploration and innovation. This study stands as the pioneering effort to comprehensively identify and establish the contextual relationships among the adoption factors of blockchain technology within BFSI industry. Through rigorous TISM analysis, this paper enriches the existing body of knowledge on blockchain technology adoption.

Industry 4.0 is one of the most emergent research topics attracting significant interest by researchers as well as practitioners. Many articles have been published with regards Industry 4.0; however, there is no research that clearly conceptualizes Industry 4.0 in the context of supply chain. This paper aims to propose the term “Supply Chain 4.0” together with a novel conceptual framework that captures the essence of Industry 4.0 within the supply chain context. As Industry 4.0 is inherently a revolution, and as revolutions are evolutionary, this research also aims to capture the evolution of Supply Chain 4.0 from maturity levels perspective to facilitate the formulation and development of Supply Chain 4.0 strategy.

Following a deductive research approach and a qualitative strategy, a systematic literature review (SLR) was adopted as the research method seeking to understand the relationships among supply chain, Industry 4.0 and maturity levels research. The three phases of the SLR process utilized are: planning, conducting and reporting. A concept-oriented technique was applied to the outputs of the SLR to obtain the key constructs that would facilitate the development of the conceptual Supply Chain 4.0 framework.

The SLR showed that there is limited research linking Industry 4.0 to supply chain. Nevertheless, it was possible to extract a set of thematic categories from the analysis of the articles which are referred to as constructs as they form the core of the conceptual Supply Chain 4.0 framework. These constructs are managerial and capability supporters, technology levers, processes performance requirements and strategic outcomes. Each of these constructs consists of a number of elements which are referred to as “dimensions” in this research and a total of 21 dimensions were identified during the SLR. The SLR also demonstrated that maturity propositions for Industry 4.0 are still embrionary and entirely missing in the context of supply chain. Hence, this research develops and proposes a maturity levels framework that is underpinned by the core constructs of Supply Chain 4.0 and the corresponding dimensions. As these proposed frameworks are conceptual, this research also identifies and proposes several research directions to help fortify the Supply Chain 4.0 concept.

This research argues that the frameworks are robust because the constructs and dimensions are grounded in the literature, thus demonstrating both theoretical and practical relevance and value. As Supply Chain 4.0 research is still in infancy, there is a range of open research questions suggested based on the frameworks that could serve as guides for researchers to further develop the Supply Chain 4.0 concept. Also, practitioners can use this framework to develop better understanding of Supply Chain 4.0 and be able to evaluate the maturity of their organizations. As the proposed frameworks are conceptual, they require further empirical research to validate them and obtain new insights.

The SLR demonstrated a clear gap in literature with regards to Industry 4.0 in the context of supply chain, and also in the context of Industry 4.0 maturity levels for supply chain. This research is unique as it formulates and introduces novel frameworks that close these gaps in literature. The value of this research lies in the fact that it makes significant contribution in terms of understanding of Supply Chain 4.0 with a clear set of constructs and dimensions that form Supply Chain 4.0, which provides the foundation for further work in this area.

The purpose of this study is to examine the implementation of Industry 4.0 (I4.0) through the lens of the dynamic capabilities framework. Contrary to most existing research, this study chooses a cross-national viewpoint, exploring companies operating in two emerging economies.

Semi-structured interviews were conducted with 80 company managers operating in eight industries in Vietnam and Bolivia. The chosen inductive analysis was supported by qualitative content analysis and data structure.

The analysis reveals 13 conceptual dimensions. For instance, sensing opportunities underlines tangible and intangible “direct prospects”, such as enhanced accuracy, speed and cost effectiveness, whereas “operational management pressures” (sensing threats) identify the dilemma of changing individuals’ mindset, recruitment and addressing financial needs. While there is an overall agreement in key dimensions, differences between managers from both countries also arise, including staff’s adaptation and constant upskilling.

Empirically, this study responds to calls for cross-national studies investigating I4.0 initiatives. In doing so, the data gathered from company managers engaged in business in emerging economies afford new perspectives, with practitioner value. Theoretically, the numerous dimensions emerging from the data analysis provide useful conceptual insights to understand managerial aspects in considering and adapting to I4.0 expectations and requirements. These insights are reinforced by the development of a conceptual model that illuminates the initiatives, efforts and challenges of embracing this phenomenon.