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Design Science (DS) is a relatively new paradigm for addressing complex real-world problems through the design and evaluation of artifacts. Its constituent methodologies are currently being discussed and established in numerous related research fields, such as information systems and management (Hevner et al., 2004). However, a DS methodology that describes the “how to” is largely lacking, not only in the field of OM but in general. The Theory of Constraints (TOC) and its underlying thinking processes (TP) have produced several novel artifacts for addressing ill-structured real-world operations problems (Dettmer, 1997; Goldratt, 1994), but they have not been analyzed from a DS research standpoint. The purpose of this research is to demonstrate how TOC’s thinking process methodology can be used for conducting exploratory DS research in Operations and Supply Chain Management (OSCM).

A case study of spare parts replenishment illustrates the use of TOC’s thinking processes in DS to structure an initially unstructured problem context and to facilitate the design of a novel solution.

TOC’s thinking processes are an effective methodology for problem-solving DS research, enabling the development of novel solutions in initially unstructured and wicked problem situations. Combined with structured CIMO design logic TOC’s thinking process offers a systematic method for exploring wicked problems, designing novel solutions, and demonstrating theoretical contributions.

The implication for research is that TOC’s thinking process methodology can provide important elements of the lacking “how to” methodology for DS research, not only for the field of OM but in general for the field of management.

The practical outcome of the research is a novel design for dynamic buffer-based replenishment that extends beyond organizational boundaries.

This work shows how the thinking processes can be used in DS research to develop rigorous design propositions for ill-structured problems.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

The study aims to discern the primary obstacles confronted by the implementing agencies in their efforts to foster financial inclusion through the “Pradhan Mantri MUDRA Yojana” (PMMY).

To collect primary data, a semi-structured questionnaire was developed. Around 120 loan officers from the implementing agencies (Scheduled Commercial Banks (SCBs), Regional Rural Banks (RRBs), Small Finance Banks (SFBs), Non-Banking Financial Companies (NBFCs) and Micro- Finance Institutions (MFIs)) of Haryana were randomly selected to fulfill the objectives. To categorize the perceived problems into discrete factors, the “factor analysis” technique was employed. The scales were then regressed on factors linked to the demographic characteristics of the loan officers to validate the hypotheses.

The study highlighted the primary obstacles impeding the advancement of financial inclusion, which encompass a range of factors. These include challenges in management, infrastructure, politics, finance and technology. Furthermore, the study established the association of the explanatory variables, namely gender, age, educational qualification, location and experience of the officers, with the extracted constraints. Notably, the experience of loan officers emerged as the most influential variable contributing to the promotion of financial inclusion through the scheme.

The current body of literature lacks any empirical investigation focusing on the perspectives of the implementing agencies regarding the challenges they encounter in advancing FI. Given the significance of FI in India, where access to formal financial services remains a critical issue, this research adds value by addressing the gaps in understanding the problems encountered.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0462

Alexa’s integration in rural primary schools has improved the pedagogy and has created an engaging and objective learning environment. This study investigates the integration, with a specific focus on exploring its various aspects. The impact of Alexa’s on students' English vocabulary, comprehension and public speaking are examined. This study aims to provide insights the teachers and highlight the potential of artificial intelligence (AI) in rural education.

This content analysis study explores the use of Alexa in primary education in rural areas of India. The study focuses on the types of the questions asked by the students and examines the pedagogical implications of these interactions. By analyzing the use of Alexa in rural educational settings, this study aims to contribute to our understanding of how voice assistants are utilized as educational tools in underprivileged areas.

Alexa significantly improved students' English vocabulary, comprehension and public speaking confidence. Alexa increased school enrollment and retention. Virtual voice assistants like Alexa may improve pedagogy and help India’s rural education. This study shows AI improves rural education.

The study only covers rural India. Self-reported data and observations may bias the study. The small sample size may underrepresent rural educational institutions in India.

Alexa is used to study rural India’s primary education. Voice assistants in rural education are understudied. The study examines Alexa’s classroom use, student questions, and policy and teacher education implications. AI’s education transformation potential addresses UNESCO’s teacher shortage. This novel study examines how AI can improve rural education outcomes and access.

Social media played an irreplaceable role in young people’s online social life and information consumption during the COVID-19 pandemic. This research focuses on the impact of excessive information on social media about COVID-19 vaccines on Generation Z's (Gen Z) associated psychological states and long-term vaccine advocacy.

The research conducted structural equation modeling analysis with online survey data from 409 Gen Z citizens in the UK.

The findings suggest that excessive information increased Gen Z social media users' ambivalence and conspiracy beliefs around COVID-19 vaccines, which, in turn, reduced their long-term vaccine advocacy in terms of vaccine acceptance, vaccination intention and vaccine promotion. Importantly, Gen Z’s confidence in government and in the healthcare systems during COVID-19 was effective in helping them overcome the detrimental effects of conspiracy beliefs and ambivalence about long-term vaccine advocacy, respectively.

This research reveals the “dark side” of social media use in the post-pandemic period and highlights the significant roles played by social institutions in mitigating the detrimental effects of Gen Z’s support in social decisions. Beyond the context of COVID-19, this research has important implications for facilitating the civic engagement of Gen Z and boosting their confidence in social institutions in terms of social cohesion.

Pakistan ranks third globally in terms of newborn deaths occuring within the first 24 hours of life. With a neonatal mortality rate of 42.0%, it carries the highest burden compared to neighboring countries such as Bangladesh (17%), India (22.7%) and Afghanistan (37%). While there has been a decline in neonatal mortality rates in Pakistan, the pace of this decline is slower than that of other countries in the region. Hence, it is crucial to conduct a comprehensive examination of the risk factors contributing to neonatal mortality in Pakistan over an extended period. This study aims to analyze the trends and determinants of neonatal mortality in Pakistan over three decades, providing valuable insights into this persistent issue.

The study focused on neonatal mortality as the response variable, which is defined as the death of a live-born child within 28 days of birth. Neonates who passed away during this period were categorized as “cases,” while those who survived beyond a specific timeframe were referred to as “noncases.” To conduct a pooled analysis of neonatal mortality, birth records of 39,976 children born in the five years preceding the survey were extracted from four waves (1990–2018) of the Pakistan Demographic and Household Survey. The relationship between risk factors and the response variable was examined using the Cox Proportional Hazard Model. Neonatal mortality rates were calculated through the direct method using the “syncmrates” package in Stata 15.

During the extended period in Pakistan, several critical protective factors against neonatal mortality were identified, including a large family size, improved toilet facilities, middle-aged and educated mothers, female children, singleton live births, large size at birth and longer birth intervals. These factors were found to reduce the risk of neonatal mortality significantly.

This study makes the first attempt to analyze the trends and patterns of potential risk factors associated with neonatal mortality in Pakistan. By examining a large dataset spanning several years, the study provides valuable insights into the factors influencing neonatal mortality.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2022-0604