Search results

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

This study aims to analyze the factors that drive or prevent interorganizational data sharing in the context of digital transformation (DT). Data sharing appears as a precondition for companies to capture emerging opportunities in supply chain management and for product-related servitization; however, there are ongoing concerns, and data are often perceived as the “new oil.” It is thus important to gain a better understanding of the determinants of firms’ decisions.

The authors develop an embedded case study analysis involving 16 firms within an extended supply network in the automotive industry. The authors focus on the peculiarities of the new context, as opposed to elements highlighted by research prior to the advent of the latest technologies. Abductive reasoning is applied to the theoretical foundations of the resource-based view, resource dependence theory and the complex adaptive systems perspective.

Data sharing is largely underpinned by factors identified prior to DT, such as data specificity, dependence dynamics and protection mechanisms and the dynamism of the business context. DT, however, can influence the extent of data sharing. New factors concern complementarities whenever data are pooled from different sources and digital platforms, as well as different forms of data ownership protection.

This study stresses that data sharing in the context of DT can be explained through established theoretical lenses, providing the integration of elements accounting for new technological opportunities.

This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19).

A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities.

Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed.

The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample.

Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach.

This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation.

The aim of this paper is to analyse the role of communities of practice (CoP) as knowledge-sharing tools in family small and medium-sized enterprises (SMEs). In this context, CoPs that jointly involve family and non-family members are expected to act as knowledge-sharing tools.

This paper employs a multiple case study methodology, analysing the cases of six small companies in different sectors and countries over a period of 8 years. Both primary and secondary data are used.

The results show the role CoPs play in involving family and non-family members in empowering knowledge-sharing initiatives. A CoP's role in knowledge sharing depends on the presence (or lack) of a family leader, the leadership approach, the degree of cohesion around shared approaches and values within the CoP, and the presence of multiple generations at work.

This paper contributes to the literature on knowledge sharing in family businesses, by exploring for the first time the role of the CoP as a knowledge-sharing tool, depending on families' involvement in the CoP.

This paper aims to use Bayesian network (BN) methodology complemented by machine learning (ML) and what-if analysis to investigate the impact of digital technologies (DT) on logistics service quality (LSQ), employing the service quality (SERVQUAL) framework.

Using a sample of 244 Italian firms, this study estimates the probability distributions associated with both DT and SERVQUAL logistics, as well as their interrelationships. Additionally, BN technique enables the application of ML techniques to uncover hidden relationships, as well as a series of what-if analyses to extract more knowledge.

This study was funded by the European Union—NextGenerationEU, in the framework of the GRINS-Growing Resilient, INclusive and Sustainable project (GRINS PE00000018—CUP B43C22000760006). The views and opinions expressed are solely those of the authors and do not necessarily reflect those of the European Union, nor can the European Union be held responsible for them.

This research delves into the influence of DTIE and DTA on SERVQUAL logistics, thereby filling a gap in the existing literature in which no study has explored the intricate relationships between these technologies and SERVQUAL dimensions. Methodologically, we pioneer the integration of BN with ML techniques and what-if analysis, thus exploring innovative techniques to be used in logistics and supply-chain studies.

The purpose of this paper is to fill a gap in the literature regarding the open innovation management approaches that small and medium-sized enterprises (SMEs) can use to access digital technologies and incorporate them into their organizational processes. The research question is: What organizational and process-level managerial actions do SMEs take to successfully access and implement digital technologies within their organizational processes?

Using Guertler et al.'s (2020) Action Innovation Management Research (AIM-R) framework, this study examines the digital transformation experiences of 10 European SMEs to gain insights into the managerial actions that foster successful digital transformation.

The findings of the paper reveal two major contributions. First, a digital transformation roadmap for SMEs is proposed, with a focus on accessing external resources and reconfiguring internal ones to ease their digital transformation journey. Second, three distinct paradoxes that influence the digital transformation process in SMEs are highlighted, providing useful insights into the challenges and tensions SMEs face during this journey.

This paper provides a unique perspective on the digital transformation of SMEs by examining the managerial actions required for successful technology adoption and revealing the paradoxes that may emerge during this transformative process. The findings form the basis for future research, deepening our understanding of digital transformation in SMEs and providing actionable advice to managers and practitioners navigating this journey.

The purpose of the study is to understand the relationship between family-driven innovation and the incorporation of corporate sustainability in German family firms.

The study conducted 26 interviews with 22 German family firms. Thematic analysis was undertaken on the collected data resulting in five major themes.

The study identified five main themes of corporate sustainability-oriented innovation in family firms, which include measuring corporate sustainability performances, building corporate sustainability-oriented infrastructure, stabilizing/optimizing operations, enhancing operational flexibility/independence and knowledge management and development. The study also provides an activity-based guide for family firms to use innovation to achieve corporate sustainability goals and present the findings’ implications for policymakers.

The present study is the first study to empirically investigate the relationship between family-driven innovation and the incorporation of corporate sustainability at each of the corporate sustainability maturity levels.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.