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To endure and cope in the rapidly changing environment, it is required of firms to gain a deeper acquisition of knowledge on market dynamics and subsequently concentrate on corporations' capacity to create, restructure and integrate their internal and external competences. Hence, the objective of this study is to investigate the influence of eco-dynamic capability (EDC) on the sustainability performance of small and medium-sized enterprises (SMEs).

Structured questionnaires were used to obtain primary data. The data were solicited from 500 employees and owner-managers of SMEs. The study’s hypotheses were tested using standard multiple regression through IBM SPSS Statistics (version 24).

The study revealed that EDC has a substantial positive effect on the economic, social and environmental sustainability performance dimensions.

The focus of this study is on EDC. Thus, although dynamic capability has been the subject of substantial study, little is known regarding the effect of EDC on the economic sustainability performance (ESP) (financial), environmental sustainability performance (ENSP) and social sustainability performance (SSP) of SMEs, predominantly amongst SMEs in emerging economies.

This study aims to examine the influence of two significant product labelling strategies (geographical indication [GI] vs country-of-origin [COO]) on shaping customer product attitude and purchase likelihood, considering consumers’ ethnocentric and cosmopolitan tendencies. The authors also investigate the boundary conditions and intervening mechanisms to manage the adverse consumer product evaluations and present mitigating procedures which reinstate favourable product evaluations and purchase likelihood.

The collected data from these all these studies were analysed using ANOVA and mediation anlaysis. The study tests the proposed hypotheses using three follow-up experimental investigations.

The study found that GI (vs COO) labels have a more significant impact on customers’ product evaluation and likelihood of purchase and supported the dispositional effect of ethnocentric and cosmopolitan inclinations. Further, the results indicated that self-product congruence can efficiently regulate consumer dispositions. Also, the results confirmed the significant impact of product identification on influencing consumer attitudes.

The above-said insights add practical insights, particularly concerning product labelling. Also, the insights on product attitudes and purchase likelihood intricacies in the context of product labelling enable companies to comprehend better the significance of GI labels, COO labels and self-product congruence.

To the best of the authors’ knowledge, this is the first time a study has compared the role of two significant product labelling strategies (GI vs COO) in shaping customer product evaluations, confirmed its boundary conditions and shown how to transform them into helpful customer product outcomes.

This study aims to explore the direct and indirect effects of sales opportunism and sales conflict as well as of non-economic and economic satisfaction in business-to-business (B2B) sales relationships. This understanding offers B2B buyers enhanced knowledge of sales business expectations towards sustainable business relationships in the future.

Through self-administered questionnaires, data were obtained from 237 sales or marketing managers/directors of small- and medium-sized companies across industries in Spain, who were randomly contacted via LinkedIn. The multivariate analysis of measurement and structural models was based on IBM SPSS Amos 27.

The study confirms that sales opportunism positively affects sales conflict. Moreover, sales opportunism is negatively associated with non-economic sales satisfaction, whereas non-economic sales satisfaction is positively associated with economic sales satisfaction. Consequently, if all associates are pleased with the relationship and the gains it can provide, a long-standing orientation can be achieved.

The study expands existing theory on seller–buyer relationships in a B2B context. It contextualises direct and indirect relationships between two antecedents (sales opportunism and sales conflict) and two postcedents (economic sales satisfaction and non-economic sales satisfaction) in sales business–buyer settings.

The study guides buyers in B2B relationships towards an improved understanding of how sales businesses perceive opportunism and conflict (as negative precursors) to impact non-economic satisfaction and how it can influence economic satisfaction.

Most studies explore B2B relationship building from the perspective of the buyer, thereby creating a shortfall in developing an understanding of all partner expectations in B2B relational intent. Moreover, the measurement of satisfaction as a multidimensional construct secured the integration of non-economic satisfaction and economic satisfaction within a single model allowing the constructs measured in this study to be holistically assessed.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.

Sustainable supply chain management (SSCM), driven by the downstream buyers' power, transfers sustainability responsibilities to the upstream supplier. In contrast to the heavily-focused buyers' perspective in the literature, the authors investigate how this buyer-driven SSCM influences suppliers' performance, using the measure of stock market reaction.

Grounded by the resource dependence theory (RDT), the authors empirically analyze the power effect on suppliers. Event study methodology and regression analysis are used, based on a sample of 1977 paired supplier observations from 1990 to 2016.

The result suggests that although a negative stock market reaction for suppliers in SSCM exists, the effect is less negative at a high level of buyer and supplier dependence. For the investigation of the “consolidated SSCM initiative,” where buyers acquire exogenous power by collaboratively managing SSCM with their peers, the authors uncover that the negative impact of this consolidated SSCM initiative can be mitigated by the high interdependence that generates relational norms in the dyads.

The authors focus on dyadic relationships. Future research can use the study's findings to study the SSCM diffusion to lower-tier suppliers.

This paper has good managerial implications for both suppliers and buyers. The authors propose dependence-based strategies for supplier managers to reduce uncertainty in SSCM. Moreover, buyer managers can use the study's findings to strengthen suppliers' commitment.

The novelty of examining the suppliers' perspective contributes to exploring the supply chain impact of SSCM. The authors extend RDT and show that high dependence is not necessarily detrimental to suppliers in this buyer-driven SSCM context. The interesting finding of interdependence in the context of the consolidated SSCM initiative brings new insights that relational norms constrain the leverage of power in the dyads and are beneficial to the power-disadvantageous suppliers.

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

This paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work.

This paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA.

This paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning.

The resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters.

This paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.

The current study seeks to investigate, why and how faculty members of Indian central universities are using academic social networking sites (ASNs) for research communication and information dissemination, as well as validate and update the results of previous scholarship in this area. To achieve this, the paper uses structural equation model (SEM).

A simple random sampling method was adopted. Online survey was conducted using a well-designed questionnaire circulated via email id among 3384 faculty members of Indian Central Universities. A SEM was designed and tested with International Business Machines (IBM) Amos. Apart from this, Statistical Package for Social Sciences (SPSS) 22 and Microsoft Excel 2010 were also used for data screening and analysis.

The study explores that most of the respondents are in favour of using the ASNs/tools for their professional activities. The study also found that a large chunk of the respondents used ASNs tools during day time. Apart from it, more number of faculty members used ASNs in research work than general purpose. No significant differences were found among the disciplines in use behaviour of ASNs in scholarly communication. Three hypotheses have been accepted while two were rejected in this study.

The study was confined to the twelve central universities, and only 312 valid responses were taken into consideration in this study.

The paper demonstrates the faculty members’ use behaviour of ASNs in their research communication. The study also contributes new knowledge to methodological discussions as it is the first known study to employ SEM to interpret scholarly use of ASNs by faculty members of Indian central universities.

This study aims to examine the impact of socioeconomic factors on electricity usage and assess the feasibility of implementing a mini-grid system in Kasangazi, Malawi. The primary aim is to understand the community’s current and potential utilisation of electrical equipment.

A mixed-methods approach was used to collect quantitative and qualitative data. Information was gathered through structured questionnaires, and energy audits were conducted among 87 randomly selected households from 28 Kasangazi communities. Data analysis relied on descriptive statistics using IBM SPSS version 28.

The study indicates that every household in Kasangazi uses non-renewable energy sources: 60 households use disposable batteries for lighting, 20 for radios and all use firewood, freely sourced from local forests, for cooking and heating water. The study shows that firewood is the community’s preferred energy source, illustrating the challenges faced in the fight against deforestation. Most household income comes from farming, with smaller contributions from businesses, employment and family remittances. Access to higher education is scarce, with only one out of 349 family members receiving tertiary education. Despite the constraints of low education levels and income, there is a demand for larger electrical appliances such as stoves and refrigerators. This underscores the need for mini-grid solutions, even in less technologically advanced, agriculture-dependent communities.

This study underscores that in Sub-Saharan Africa, factors like household size, income and education levels do not significantly influence the electricity demand but should be taken as part of the fundamental human rights. Rural populations express a desire for electricity due to the convenience it offers, particularly for appliances like refrigerators and stoves. Mini-grids emerge as a viable alternative in regions where grid electricity provision is challenging. It is concluded from this paper that the issue of using renewable energy should not only be taken for environmental preservation but also to promote energy access, augmenting efforts in supplying electricity to the remotest parts of the country.