Search results

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.

There is evidence that spirituality at the workplace has positive effects on work outcomes, and there are different models conceptualizing the construct. To date, there is no discussion highlighting how digitalization is affecting workplace spirituality and vice versa. The present review tries to close this gap by discussing the psychological dynamics in light of digitalization and spirituality in the context of work.

This is a conceptual discussion based on an extensive narrative review. The conceptual design is further tested with a real-life case study.

The result is a model that may guide future research, which consists of the four highly interdependent domains, namely psychology (with the dimensions of emotion, cognition and behavior), digitalization (with the dimensions of platforms, data and algorithms), spirituality (with the dimensions of meaning, self-transcendation and belonging), as well as the workplace (with the dimensions of work tasks, location, community and culture and values). The discussion includes implications for the future of work, suggestions for management decisions and potential future research directions.

To date, there are many discussions about digital transformation and a limited amount of them have invested in analyzing psychological dimensions. The application to spirituality and the workplace – especially when the two are combined – is almost wholly absent, which makes the present discussion both innovative and original.

This study aims to provide a response to the commentary by Yuan on the paper “Marketing or Methodology” in this issue of EJM.

Conceptual argument and statistical discussion.

The authors find that some of Yuan’s arguments are incorrect, or unclear. Further, rather than contradicting the authors’ conclusions, the material provided by Yuan in his commentary actually provides additional reasons to avoid partial least squares (PLS) in marketing research. As such, Yuan’s commentary is best understood as additional evidence speaking against the use of PLS in real-world research.

This rejoinder, coupled with Yuan’s comment, continues to support the strong implication that researchers should avoid using PLS in marketing and related research.

Marketing researchers should avoid using PLS in their work.

This rejoinder supports the earlier conclusions of “Marketing or Methodology,” with additional argumentation and evidence.

Despite retail digitisation and research efforts focussed on online and omnichannel shopping, there is insufficient knowledge regarding retail patronage formation in the grocery category, where in-store sales dominate. This study analyses the retail patronage formation in grocery in-store fill-in shopping.

The authors designed a questionnaire to measure retail patronage behaviour, consumer satisfaction (CS), store attributes evaluation and e-retail usage. Then, the authors analysed the path structure for retail patronage behaviour formation using structural equation modelling. Additionally, they performed a mediation analysis using the bootstrap method and a moderation analysis based on a chi-square difference test.

This study provides three main findings. First, the authors' model has two ways to increase Share-of-Wallet (SOW). One is to increase Share-of-Visits (SOV) and another is to increase CS amongst non-users of e-retailing. Second, the results of the moderation analysis suggest the influence of customers' use or non-use of e-retailing on SOW formation. Third, service evaluation plays an interesting role in the overall model: the lower the assessment of service, the higher the SOV; the higher the evaluation of service, the greater the CS; the greater the CS, the higher the SOV.

The authors proposed the framework for the relative retail patronage formation in grocery fill-in shopping to examine the relationship between two relative patronage indicators (SOW and SOV) in the path structure and the mediating effect of CS and the moderating effect of e-retailing usage on retail patronage formation.

To determine how to best deploy the Triple-A supply chain (SC) capabilities (AAA-agility, adaptability and alignment) to improve competitive advantage (CA) by identifying the Triple-A SC model with the highest CA predictive capability.

Assessment of in-sample and out-of-sample predictive capacity of Triple-A-CA models (considering AAA as individual constructs) to find which has the highest CA predictive capacity. BIC, BIC-Akaike weights and PLSpredict are used in a multi-country, multi-informant, multi-sector 304 plant sample.

Greater direct relationship model (DRM) in-sample and out-of-sample CA predictive capacity suggests DRM's greater likelihood of achieving a higher CA predictive capacity than mediated relationship model (MRM). So, DRM can be considered a benchmark for research/practice and the Triple-A SC capabilities as independent levers of performance/CA.

DRM emerges as a reference for analysing how to trigger the three Triple-A SC levers for better performance/CA predictive capacity. Therefore, MRM proposals should be compared to DRM to determine whether their performance is significantly better considering the study's aim.

Results with our sample justify how managers can suitably deploy the Triple-A SC capabilities to improve CA by implementing AAA as independent levers. Single capability deployment does not require levels to be reached in others.

First research considering Triple-A SC capability deployment to better improve performance/CA focusing on model's predictive capability (essential for decision-making), further highlighting the lack of theory and contrasted models for Lee's Triple-A framework.

This study aims to determine the factors and dynamic systems behaviour of essential medicine stockout in public health-care supply chains. The authors examine the constraints and effects of mental models on medicine stockout to develop a dynamic theory of medicine availability towards saving patients’ lives.

This study uses a mixed-method approach. Starting with a survey method, followed by in-depth interviews with stakeholders within five health-care supply chains to determine the dynamic feedback leading to stockout and conclude by developing a network mental model for medicines availability.

The authors identified five constraints and developed five case mental models. The authors develop a dynamic theory of medicine availability across cases and identify feedback loops and variables leading to medicine availability.

The need to include mental models of stakeholders like manufacturers and distributors of medicines to understand the system completely. Group surveys are prone to power dynamics and bias from group thinking. This survey’s quantitative output could minimize the bias.

This study uniquely uses a mixed-method of survey method and in-depth interviews of experts to assess the essential medicine stockout in Nigeria. To improve medicine availability, the authors develop a dynamic network mental model to understand the system structure, feedback and behaviour driving stockouts. This research will benefit public policymakers and hospital managers in designing policies that reduce medicine stockout.

Several studies have shown that the mechanism of labour-intensive construction (LIC) projects can mitigate high unemployment and create skilled development, especially in developing nations. The guidelines and practices for implementation may have faced some encumbrances in some countries. Whether the current guidelines and practices for municipal infrastructure support agent (MISA) to execute LIC projects face hindrances in South Africa has yet to receive in-depth studies. Thus, this study attempts to proffer policy solutions to improve the proposed revised guidelines and practices for MISA in LIC project execution in South Africa.

The study's objectives were accomplished via a combination of 16 virtual interviews of built environment professionals and government officials involved in LIC project execution in South Africa and supported by the analysed documents. A thematic approach was used to analyse the data and presented two main themes.

Findings show lax enforcement of discretionary funds, lax institutional capacity and inadequate individual skills, among others, as the gaps in existing South Africa's LIC guidelines and practices. Also, policy solutions to address the gaps were proffered.

The suggested feasible policies will improve the proposed revised guidelines and practices for MISA in LIC project execution in South Africa. This guide will promote the development of individual skills, institutional capacities and increase employment across South Africa.

This study promotes the use of LIC to create employment and contribute to proffering measures that will improve the proposed revised third edition of the guidelines and practices for MISA to execute LIC.

The purpose of this study was to examine how well a strength-based program grounded in positive psychology principles can advance the practical critical thinking skills of those pursuing the teacher training course.

This study used a single-group pre-test post-test design with 35 teacher-trainees from the Bachelor of Education course. The two-and-a-half-week strength-based program used the values in action survey to identify strengths. Pre- and post-test scores, measured with the Cornell Critical Thinking Test – Level Z, underwent Statistical Package for Social Sciences analysis including paired samples t-test for subcomponent and overall composite analysis.

Analysis of the pre- and post-test scores demonstrated a statistical significance in the critical thinking scores obtained by the teacher-trainees. Post-test scores were consistently significant. Out of the elements of critical thinking, induction, meaning, observation and credibility were more prominent. Deduction and assumption identification were also having a significant effect.

Most critical thinking programs focus on evaluating specific teaching methods for improving critical thinking skills. In education, positive psychology studies often center on students’ well-being, attention spans and academic success, aligning with wellness programs. Despite the importance of strengths in positive psychology, there is a lack of research on using a strength-based approach to boost critical thinking skills. This study aims to enhance teacher-trainees’ critical thinking by leveraging their individual strengths, moving away from traditional instructional strategies.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.