Impact of Lean and Sustainability Oriented Innovation on Sustainability Performance of Small and Medium Sized Enterprises: A Data Envelopment Analysis-based framework

Lean and Sustainability Oriented Innovation both enhance competitiveness of small and medium enterprises (SMEs) in a sustainable way. Lean is efficiency focused, whereas Sustainability Oriented Innovation emphasizes on responsiveness. Although lean and sustainability oriented innovation have been separately researched, there is a gap in knowledge on the combined effect of lean and sustainability oriented innovation (SOI) on SMEs Supply Chain sustainability. SMEs have limited resources and face numerous competition. Therefore, their supply chain sustainability can only be achieved through most appropriate trade-off between economic, environment and social aspects of business. The purpose of this paper is to understand the combined effect of sustainability oriented innovation and lean practices, on supply chain sustainability performance of SMEs. The study uses a Data Envelopment Analysis (DEA) based framework and applies this to a group of SMEs within the Eastern part of India. Lean and sustainability oriented innovation are considered as input criteria, and economic, operational, environmental and social aspects are considered as output criteria of the proposed framework. DEA segregates inefficient SMEs and suggests at least a SME to benchmark. Subsequently, the study undertakes qualitative approach to suggest improvement measures for the inefficient SMEs. The results reveal that combined lean and SOI helps achieve SMEs' supply chain sustainability. The findings are useful for policy makers and Individual SMEs' owners and managers to undertake measures for improving sustainability. Theoretically this research contributes a DEA-based framework to study the effect of combined lean and SOI on sustainability that helps improving SMEs’ sustainability performance

Does carrier localization affect the anomalous Hall effect?

The effect of carrier localization due to electron-electron interaction in anomalous Hall effect is elusive and there are contradictory results in the literature. To address the issue, we report here the detailed transport study including the Hall measurements on $\beta$-Mn type cubic compound Co$_7$Zn$_7$Mn$_6$ with chiral crystal structure, which lacks global mirror symmetry. The alloy orders magnetically below $T_c$ = 204 K, and reported to show spin glass state at low temperature. The longitudinal resistivity ($\rho_{xx}$) shows a pronounced upturn below $T_{min}$ = 75 K, which is found to be associated with carrier localization due to quantum interference effect. The upturn in $\rho_{xx}$ shows a $T^{1/2}$ dependence and it is practically insensitive to the externally applied magnetic field, which indicate that electron-electron interaction is primarily responsible for the low-$T$ upturn. The studied sample shows considerable value of anomalous Hall effect below $T_c$. We found that the localization effect is present in the ordinary Hall coefficient ($R_0$), but we failed to observe any signature of localization in the anomalous Hall resistivity or conductivity. The absence of localization effect in the anomalous Hall effect in Co$_7$Zn$_7$Mn$_6$ may be due to large carrier density, and it warrants further theoretical investigations, particularly with systems having broken mirror symmetry.Comment: 9 pages, 5 figure

Understanding Value of Social Media in Supply Chain Management

The purpose of the paper is to extend the social media analytics frameworks reported in the existing literature to create value from the data captured from heterogenous social media and IoT sources in a global supply-chain. The paper will aid the practitioners to understand the importance of focussed analysis and the key requirements for this analysis by offering a guide to organisational needs. The framework and requirements proposed in the paper will aid managers to capture value from the data (i.e. gain key insights) and augment human decision-making

Sustainability and intermodality in humanitarian logistics: A two-stage multi-objective programming formulation

When managing crises and disasters, decision-makers face high uncertainty levels, disrupted supply chains, and damaged infrastructure. This complicates delivering resources that are essential for the survival of the victims. Flexible and adaptable supply networks are needed to ensure a consistent flow of relief to the areas affected by disasters. Intermodality is a valuable approach when infrastructure is damaged, as it allows the use of different delivery modes to reach demand areas. Nevertheless, involving different transportation modes has an impact on the environment. Looking at the importance of helping victims and considering the environmental impact of humanitarian operations for long-term sustainability, intermodality and carbon emission reduction measures can be an interesting combination. This area, however, is currently understudied. This article introduces a two-stage stochastic formulation to fill that gap. The model addresses facility location, resource allocation, and intermodal relief distribution considering carbon emission reduction in facilities, intermodal activities, and distribution. The formulation minimises costs and the level of shortage of relief. The model is tested using a case study in Sinaloa, Mexico, to investigate the impact of intermodality and carbon emission reduction measures on costs and shortage of relief for disaster victims. The findings confirm that the model proposed allows for the diversification of transportation modes and reduces carbon emissions whilst achieving a good level of performance in both metrics. The comparison with a benchmark model without intermodality and carbon reduction measures suggests that the formulation can increase flexibility and reduce the level of CO2 emissions whilst maintaining high satisfaction rates

Embedding transparency in artificial intelligence machine learning models: managerial implications on predicting and explaining employee turnover

Employee turnover (ET) is a major issue faced by firms in all business sectors. Artificial intelligence (AI) machine learning (ML) prediction models can help to classify the likelihood of employees voluntarily departing from employment using historical employee datasets. However, output responses generated by these AI-based ML models lack transparency and interpretability, making it difficult for HR managers to understand the rationale behind the AI predictions. If managers do not understand how and why responses are generated by AI models based on the input datasets, it is unlikely to augment data-driven decision-making and bring value to the organisations. The main purpose of this article is to demonstrate the capability of Local Interpretable Model-Agnostic Explanations (LIME) technique to intuitively explain the ET predictions generated by AI-based ML models for a given employee dataset to HR managers. From a theoretical perspective, we contribute to the International Human Resource Management literature by presenting a conceptual review of AI algorithmic transparency and then discussing its significance to sustain competitive advantage by using the principles of resource-based view theory. We also offer a transparent AI implementation framework using LIME which will provide a useful guide for HR managers to increase the explainability of the AI-based ML models, and therefore mitigate trust issues in data-driven decision-making

Linking HRM with Sustainability Performance Through Sustainability Practices:Unlocking the Black Box

In response to the triple‐bottom‐line sustainability challenges facing today's world, organizations are increasingly incorporating sustainability principles into their strategies; however, this is challenging for small and medium‐sized enterprises (SMEs) with limited resources. While sustainable human resource management (HRM) has recently gained scholarly prominence, the impact of sustainable HRM practices on an organization's sustainability performance remains under‐explored. Anchored on signalling theory, we address this gap by examining the impact of sustainable HRM on an organization's sustainability performance. Drawing upon survey data collected from 254 employees across SMEs in Vietnam, we found that sustainable HRM practices and managerial competency positively impact sustainability practices, which in turn predict sustainability performance, while sustainability practices mediate these relationships. Employee sustainability participation and long‐term orientation strengthen the relationship between sustainability practices and sustainability performance. Further, co‐worker sustainability support moderates the relationship between sustainable HRM practices and managerial competency and sustainability practices. We discuss the contributions to theory and practice and provide future research directions

Adoption of circular economy practices in small and medium-sized enterprises: Evidence from Europe and the UK

Circular Economy (CE) practices have the potential to enhance sustainability performance of organisations and thus help respond to United Nations Sustainability Development Goals. The aim of this research is to examine the adoption of CE in small and medium sized enterprises (SMEs) and its impact on sustainability performance. We analyze the current state of CE practices and its impact on sustainability performance across key CE fields of action (design, procurement, production, distribution, consumption and recover) of SMEs in France, Greece, Spain and the UK A mixed-methods approach (survey, interviews, case studies) is adopted to collect data from around 100 SMEs in each country, employing resource-based view as the theoretical lens. Our findings reveal that CE adoption can result into superior environmental performance compared to economic and social performance. Moreover, the ‘design’ function contributes the most towards the adoption of CE in SMEs, whereas the ‘recover’ function contributes the least, considering the current state-of-practices. From a theoretical perspective, we outline the issues and challenges, impact of support from customers and policymakers, and self-motivation of SMEs to adopt CE. Based on the findings, we propose an implementation framework for SMEs to develop organisation wide strategic initiatives for CE adoption in business operations