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The purpose of this study is to present a system dynamic (SD)-based remanufacturing economic analysis model of used automobile engine under two recycling modes. The authors will compare the remanufacturing cost, sales profit and sales revenue from time and space dimensions incurred in different recycling modes in the long run.

The remanufacturing economic analysis model is based on SD methodology. The authors can simulate the relations of impact factors on automobile engine recycling and remanufacturing and further analyze and compare the cost, sales profit and sales revenue incurred in different recycling modes in the long term.

Sinotruk Steyr engine remanufacturing in Shandong province is taken as the research case subject. The revenue, cost and profit under the two recycling modes from 2015 to 2035 are analyzed and compared. The results show that different recycling modes have significant varying influence on the economy of engine remanufacturing.

This economic analysis model can provide a method reference to decide the recycling mode for auto components and other product remanufacturing. Moreover, this model can guide and support the sustainable development of remanufacturing industry.

The extensive use of mobile money (MM) has been widely recognized as a digital engine of socioeconomic development in sub-Saharan Africa (SSA). This paper aims to focus on the effects of MM use and stockouts on informal microenterprise performance and investigate whether MM use mitigates the relationship between stockouts and firm performance.

This study utilizes firm-level data from the latest World Bank Informal Sector Enterprise Surveys across six SSA countries. We employ instrumental variable-adjusted and propensity score-weighted regressions to investigate the buffering effect of MM use.

We find a significantly positive effect of MM use and a significantly negative impact of stockouts on informal microenterprise performance. Importantly, we establish that MM use attenuates the negative impact of stockouts on firm performance. We further document that the attenuating effect of MM use is more profound for firms using MM for transactions with supply chain partners, located in communities with high MM use rates, and operating in the retail industry.

Our research generates important managerial and policy implications. Future policies should capitalize on MM to foster an effective financial ecosystem in which informal microenterprises can survive and grow, thereby deepening their contributions to sustainable development.

Whereas the business benefits of MM among small, medium and large firms are well-documented, the role of MM use on informal microenterprise performance is less understood. This study fills the research gap in the literature by focusing on the influence of MM use on the relationships between informal microenterprise operations and performance.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This paper analyses the role of intellectual capital (IC) as a factor of the financial performance of entrepreneurial firms, which are recognized as the main drivers of economic growth and employment.

The sample consists of 188 business owners from Serbia. The primary data are collected using the questionnaire, while the secondary data come from the annual financial statements of their companies. The elements of IC as independent variables are grouped into three components: human, structural and relational capital; sales revenue and operating profit CAGR (5y) are used as dependent variables, while company size and industry type are used as control variables. Statistical analysis involves factor and regression analyses.

The results reveal that IC components contribute to the long-term financial performance of entrepreneurial firms. Specifically, the following elements have positive effects on financial performance: knowledge of the entrepreneur, process improvement and organisational culture. On the other hand, entrepreneurs’ social skills and tenacity were found to have a negative impact on revenue and operating profit growth, while support from informal networks had a negative effect on the growth of sales revenue.

This study aims to fill a gap in the literature on the impact of IC on the financial performance of entrepreneurial firms.

Gillette’s historically successful “razor and blade” business model (RBM) has been a promising benchmark for multiple businesses across diverse industries worldwide in the past several decades. The extant literature deals with very few nuances of this business model notwithstanding the fact that there are several variants of this business model being put to practical use by firms in diverse industries in grossly metaphorically equivalent situations.

This study adopts the 2 × 2 truth table framework from the domains of mathematical logic and combinatorics in fleshing out all possible (four logical possibilities) variants of the razor and blade business model for further analysis. This application presents four mutually exclusive yet collectively exhaustive possibilities on any chosen dimension. Two major dimensions (viz., provision of subsidy and intra- or extra-firm involvement in the making of razors or blades or both) form part of the discussion in this paper. In addition, this study synthesizes and streamlines entrepreneurial wisdom from multiple intra-industry and inter-industry benchmarks in terms of real-time firms explicitly or implicitly adopting several variants of the RBM that suit their unique context and idiosyncratic trajectory of evolution in situations that are grossly reflective of the metaphorically equivalent scenario of razor and recurrent blades. Inductive method of research is carried out with real-time cases from diverse industries with a pivotally common pattern of razor and blade model in some form or the other.

Several new variants of the razor and blade model (much beyond what the extant literature explicitly projects) have been discovered from the multiple metaphorically equivalent cases of RBM across industries. All of these expand the portfolio of options that relevant entrepreneurial firms can explore and exploit the best possible option chosen from them, given their unique context and idiosyncratic trajectory of growth.

This study has enriched the literature by presenting and analyzing a more inclusive or perhaps comprehensive palette of explicit choices in the form of several variants of the RBM for the relevant entrepreneurial firms to choose from. Future research can undertake the task of comparing these variants of RBM with those of upcoming servitization business models such as guaranteed availability, subscription and performance-based contracting and exploring the prospects of diverse combinations.

Smart entrepreneurial firms identify and adopt inspiring benchmarks (like razor and blade model whenever appropriate) duly tweaked and blended into a gestalt benchmark for optimal profits and attractive market shares. They target diverse market segments for tied-goods with different variants or combinations of the relevant benchmarks in the form of variegated customer value propositions (CVPs) that have unique and enticing appeal to the respective market segments.

Value-sensitive customers on the rise globally choose the option that best suits them from among multiple alternatives offered by competing firms in the market. As long as the ratio of utility to price of such an offer is among the highest, even a no-frills CVP may be most appealing to one market segment while a plush CVP may be tempting to yet another market segment simultaneously. While professional business firms embrace resource leverage practices consciously, amateur customers do so subconsciously. Each party subliminally desires to have the maximum bang-to-buck ratio as the optimal return on investment, given their priorities ceteris paribus.

Prior studies on the RBM have explicitly captured only a few variants of the razor and blade model. This study is perhaps the first of its kind that ferrets out many other variants (more than ten) of the razor and blade model with due simplification and exemplification, justification and demystification.

Responding to calls for deeper analysis of the conceptual foundations of service infusion in manufacturing, this paper examines the underlying assumptions that: (i) manufacturing firms incorporating services follow a pathway, moving from pure-product to pure-service offerings, and (ii) profits increase linearly with this process. We propose that these assumptions are inconsistent with the premises of behavioural and learning theories.

Machine learning algorithms are applied to test whether a successive process, from a basic to a more advanced offering, creates optimal performance. The data were gathered through two surveys administered to USA manufacturing firms in 2021 and 2023. The first included a training sample comprising 225 firms, whilst the second encompassed a testing sample of 105 firms.

Analysis shows that following the base-intermediate-advanced services pathway is not the best predictor of optimal performance. Developing advanced services and then later adding less complex offerings supports better performance.

Manufacturing firms follow heterogeneous pathways in their service development journey. Non-servitised firms need to carefully consider their contextual conditions when selecting their initial service offering. Starting with a single service offering appears to be a superior strategy over providing multiple services.

The machine learning approach is novel to the field and captures the key conditions for manufacturers to successfully servitise. Insight is derived from the adoption and implementation year datasets for 17 types of services described in previous qualitative studies. The methods proposed can be extended to assess other process-based models in related management fields (e.g., sand cone).

This paper aims to identify and report the differential effects of activity control and capability control on role stressors, which subsequently affect salespeople’s job satisfaction and sales performance.

Drawing on job demands-resources (JD-R) theory, the authors defined active control and customer demandingness as the job demands and capability control as the job resource, and designed their relationship with role stressors, which are indicated as role ambiguity, role conflict and role overload. The authors enrolled a sample of 223 industrial salespeople from pharmaceutical companies. After collecting the data, the authors used structural equation modeling using AMOS to test and estimate causal relationships along with a two-step approach to examine the interaction effect. The authors have also tested the simple slope of two-way interactions. All of the measured variables were identical to those used in previous studies.

The study findings indicate that behavior-based control can be counterproductive. Reducing activity control can decrease role stress, increase job satisfaction and improve job performance; increasing capability control, however, can reduce role stress and increase job satisfaction and performance. It is also important to acknowledge the external environment of the sales context in which behavior-based control is most effective: whereas high customer demandingness and capability control are related to reduced role stress, high customer demandingness and activity control are related to increased role stress.

Sales managers should recognize that different control management regimes reinforce or mitigate salespeople’s job stressors and outcomes under specific conditions (i.e. work environments marked by higher or lower customer demandingness).

Drawing on JD-R theory, the research shows that a behavior control (i.e. activity control and capability control) has differential, and even opposite, psychological consequences.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

Opinions drafted by the European Economic and Social Committee (EESC), and reports and studies by the European Parliament and 2014 and 2018 Resolutions on employee financial participation (EFP) emphasised the growing importance of EFP, particularly with respect to small and medium-sized enterprises. Against this background, the commission included the promotion of employee share ownership in its Action Plan to reform European company law and corporate governance and embarked on the 2013/14 Pilot Project. Based on the most recent data on the scope and impact of various EFP schemes in EU companies and the legal and regulatory changes in individual Member States, the aim of this article is to provide an overview of policymaking and the main trends of the dynamics of EFP over the last three decades.

We give an overview (1) of the analysis of the most recent rounds of different large-scale cross-country surveys (2015 European Working Conditions Survey, 2021 CRANET [Cranfield Network on International Human Resource Management], 2019 European Company Survey [ECS]) and (2) of the EU policy documents and legislative acts over the last three decades. We provide a tabular overview of the status quo in all 29 countries under consideration as of January 2024 to provide the reader with an Alta vista summary view of all forms of EFP practised.

The European Commission’s interest in EFP has grown substantially since the publication of the first PEPPER Report in 1991 and the Council Recommendation on EFP of 27 July 1992. Since then, the European Union has not only extended from 12 Members States to currently 27 but has also faced many complex and urgent challenges. Financial and other crises have endangered prosperity and cohesion on the continent and both, the financial crisis of 2008/09, and the coronavirus disease 2019 (COVID-19) pandemic 2020/21 have left their marks on “Social Europe”.

The European Commission’s interest in EFP has grown substantially since the publication of the first PEPPER (Promotion of Employee Participation in Profits and Enterprise Results) Report in 1991 and the Council Recommendation on EFP on 27 July 1992. Since then, the European Union has not only extended from 12 Members States to currently 27 but has also faced many complex and urgent challenges. Financial and other crises have endangered prosperity and cohesion on the continent and both, the financial crisis of 2008/09, and the COVID-19 pandemic 2020/21 have left their marks on “Social Europe”.

This study identifies benefits and challenges of PL partnerships, and recommendations to improve the PL partnership process.

Qualitative data was collected via semi-structured interviews (n = 8) with Irish PL retail buyers (n = 4) and producers (n = 4). Data was coded and thematically analysed.

Three key themes were identified. Theme 1 provides an overview of the benefits of PL partnerships for producers (e.g. volume driven orders, increased efficiencies) and for retailers (e.g. unique products, meeting consumer demand). Theme 2 presents challenges of PL partnerships specific to small and large producers (e.g. small producers may need significant investment to upgrade facilities, while larger producers may require significant volume to justify adaptation of production lines). Challenges common to both (e.g. risks related to short-term contracts, concerns about brand identity) are also discussed. Theme 3 summarised recommendations for successful PL partnerships generally (e.g. setting clear expectations and goals, building rapport and trust), and recommendations specific to producers and buyers specifically (e.g. producers should diversify customers to reduce risk, and retailers should communicate needs and direction).

There is currently limited research on PL partnerships between producers and retailers. This study addresses this gap by identifying key aspects for producers to consider when entering PL partnerships and key aspects for retailers to be aware of to help improve the attractiveness and success of these partnerships.