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The purpose of this paper is to explore and propose how product-in-use data can be used in, and improve the performance of, the demand planning process for automotive aftermarket services.

A literature review and a single case study investigate the underlying reasons for the demand for spare parts by conducting in-depth interviews, observing actual demand-generating activities, and studying the demand planning process.

This study identifies the relevant product-in-use data and divides them into five main categories. The authors have analysed how product-in-use data are best utilised in planning spare parts with different attributes, e.g. different life cycle phases and demand frequencies. Furthermore, the authors identify eight potentially relevant areas of application of product-in-use data in the demand planning process, and elaborate on their performance effects.

This study details the understanding of what impact context has on the potential performance effects of using product-in-use data in aftermarket demand planning. Propositions generate several strands for future research.

This study shows the potential impact of using product-in-use data, using eight different types of interventions for spare parts, in the aftermarket demand planning.

The literature focusses on single applications of product-in-use data, but would benefit from considering the context of application. This study presents interventions and explores how these enable improved demand planning by analysing usage and effects.

The ambiguous response of business schools to responsible management education (RME) is part of a larger problem which is rooted in how science evolves. The purpose of this paper is to explain how the consolidation of causal-based research in management will produce much more effective theories and teaching materials. Therefore business schools will then have to seriously engage in strong RME practices to prevent scandals from happening and to consolidate management as a profession.

In total, two models are introduced and explained. First, a comprehensive methodology for theory building that controls for causality and delivers clear and actionable results. Second, a model that depicts the three stages of interactions between variables (relationship, contingency and causality).

New causal-based research methods are starting to emerge that explain when a particular event will occur and how to “cause” for it to happen.

Business schools are not responsible of the current economic crisis. But if they do not find a way to introduce RME practices before the advent of causal-based research their reputation might be severely damaged during the next one.

As many other business school innovations, the first group of business schools that pioneer causal-based research will not only deliver on the promise of RME but also introduce the new generation of successful, relevant and actionable new managerial practices.

Over the last decade or so there has been an increased interest in combining the forecasts from different models. Pooling the forecast outcomes from different models has been shown to improve out‐of‐sample forecast test statistics beyond any of the individual component techniques. The discussion and practice of forecast combination has revolved around the pooling of results from individual forecasting methodologies. A different approach to forecast combination is followed in this paper. A method is used in which negatively correlated forecasts are combined to see if this offers improved out‐of‐sample forecasting performance in property markets. This is compared with the outcome from both the original model and with benchmark naïve forecasts over three 12‐month out‐of‐sample periods. The study will look at securitised property in three international property markets – the USA, the UK and Australia.

Circular supply chain management (CSCM) is proving to be a strong contributor towards sustainable development. The purpose of this study is to analyse the circular practices in CSCM implementation towards sustainability in the Indian auto sector; these practices are at the initial stage in a developing economy.

In this research work, a framework has been developed to enrich understanding of various CSCM practices for sustainability. To develop a causal framework, a Fuzzy Decision-Making Trial and Evaluation Laboratory (F-DEMATEL) methodology has been adopted.

The results produce 32 practices with cause and effect groups and their importance/priorities. Based on F-DEMATEL, these 32 practices are grouped into cause and effect groups based on R + C and R − C values.

The findings will help managers and decision-makers to gain a deeper understanding of the practices and their nature; the governmental and financial practices are identified as the most critical factors that need more attention. The results will help strategy makers to plan accordingly.

This research has identified and analysed 32 basic practices and their interrelationships to achieve sustainability in CSCM.

This study aims to focus on a key unanswered question about how digitalization and the knowledge processes it enables affect firms’ strategies in the international arena.

The authors conduct a systematic literature review of relevant theoretical and empirical studies covering over 20 years of research (from 2000 to 2023) and including 73 journal papers.

This review allows us to highlight a relationship between firms’ international strategies and the knowledge processes enabled by applying digital technologies. Specifically, the authors discuss the characteristics of patterns of knowledge flows and knowledge processes (their origin, the type of knowledge they carry on and their directionality) as determinants for the emergence of diverse international strategies embraced by single firms or by populations of firms within ecosystems, networks, global value chains or alliances.

Despite digital technologies constituting important antecedents and critical factors for the internationalization process, and international businesses in general, and operating cross borders implies the enactment of highly knowledge-intensive processes, current literature still fails to provide a holistic picture of how firms strategically use what they know and seek out what they do not know in the international environment, using the affordances of digital technologies.

Projects have high stakes in how they are categorized. The final place of a project within a classification scheme depends on the inclusion or exclusion of certain classification criteria. So far, many researchers and organizations have used a variety classification criteria to construct different project classification schemes. However, most of these classification criteria have been taken for granted and the process of selecting them to categorize projects still remains a black box. The purpose of this paper is to open the black box of classification process and explain how it is reflected in picking the classification criteria.

Drawing on insights from cognitive psychology’s literature, the authors examine the main views of classification process to provide insight into the unknown or implicit reasons that one might have to pick particular attributes as project classification criteria.

The authors argue that classification occurs in the eye of the beholder; it is not only the project’s features per se but also the classifier’s “goals, ideal and preference” or “knowledge of causal relations” that are reflected in the classification criteria.

By elaborating the classification process, the authors brought the project context into the big picture of classification and provide a more rational, and coherent picture of how project classification works. This contributes to a theoretical blind spot, raised by prior researchers, related to the selection of project classification criteria.

Understanding classification processes will reduce the ambiguities, inconsistencies and multiple interpretations of project categories and help practitioners increase their projects’ visibility and legitimacy within an already established classification scheme. These implications help organizations in addressing some of the main obstacles to using categorization in project management practice.

The review of prior work in the category research literature and the insights from this paper will provide project management scholars with a useful toolbox for future research on project classification, which has long been understudied.

This study aims to explore how entrepreneurial firms' networking logics may change under different types of perceived uncertainty. The arrival of new knowledge from the entrepreneurial firm's network may alter the perceived technology and market uncertainty that in turn determines how the firm adopts or combines the two opposing logics of causation and effectuation. Focusing on the roles of external advisors recruited by the firms, the study probes the details of the cyclical process and the mechanism through which networking logics are altered.

In this study the authors conducted a 3-year longitudinal multiple case study of 12 United Kingdom (UK) high-tech start-ups from prefounding to A-round funding with 54 semistructured interviews and meeting observations.

The knowledge of external advisors with distinct experience often reshapes the entrepreneurial firm's perceptions of uncertainty, leading to logics change in network development. The authors identify two types of knowledge brought by external advisors and discover how these can influence three networking logic pathways under different levels of technology and market uncertainty.

The study is one of the first to map the paths of changing logics along with different types of uncertainty in the context of entrepreneurial network development. The study unpacks one of the key mechanisms of networking logic changes: the knowledge and expertise of those advisors recruited by the entrepreneurial firms. The process model of changing logics contributes to the effectuation literature and entrepreneurial network research.

Bitcoin is difficult to categorize and indeed has been associated with 112 different labels in the British media (e.g., “private money,” “commodity”) – most of which poorly describe bitcoin. Specifically, our analyses of 674 media articles, focusing on the relationship between labeling and categorization, identify classification inconsistencies at three levels: within clusters of labels, between labels and categories, and between category attributes. These inconsistencies hamper categorization based on attribute similarity, audience goals, and causal models, respectively. We identify four factors that nurture this categorical anarchy and conclude with a call for research on the socioeconomic revolution heralded by blockchain technology.

Structural equation modelling (SEM) is a method that is very frequently applied by marketing and business researchers to assess empirically new theoretical proposals articulated by means of complex models. It is, therefore, a logical thought that the quality of the new advances in marketing and business theory depends, in part, on how well SEM is applied. This study aims to conduct an extensive review and empirical analysis of a broad variety of classic and recent controversies and issues related with the use of SEM, in order to identify problematic questions and prescribe a compendium of solutions for its suitable application.

The main analyses were conducted on a sample of 191 SEM‐based papers and 472 applications, i.e. all the SEM‐based studies published in four leading marketing journals during the period 1995‐2007.

Despite the maturity of SEM, its application in marketing research still has notable room for improvement. This is a general conclusion based on numerous problems detected and discussed here.

The study provides plausible solutions to the problems identified, a useful guide that is easy to follow and to apply adequately to present SEM issues in marketing and business studies.

The sample of SEM‐based papers and applications is limited to four publication outlets. A wider set or/and other journals different to those analyzed here may be preferred.

This is a valuable and timely study of the application of SEM in marketing and business research, and is also useful as a guiding framework for good practice. Likewise, as the problems discussed here presumably occur in other areas of social science, this paper should be welcome beyond the borders of the business disciplines.