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Market orientation (MO) (intelligence generation, intelligence dissemination and responsiveness) is known as one of the key concepts in marketing literature. Although prior research has widely focused on the meaning and application of MO, few attempts have been made to explore how market-oriented firms lead to innovation and market performance and what factors actually moderate this relationship. To fill this gap, the present study aims to explore the relationship between MO, innovation and market performance. This study also attempts to examine the intervening role of IT infrastructure, business analytics (BA) capabilities and market turbulence in the proposed model.

In this study, a questionnaire-based survey was undertaken to test the proposed hypotheses. To verify the proposed theoretical model, partial least squares (PLS)/structured equation modeling (SEM) was performed with 114 valid survey data.

Despite prior studies which postulated innovation performance as the final outcome of MO (Han et al., 1998; Song et al., 2015), this study focused on innovation performance as a mediating outcome which finally leads to market performance. The statistical results approve the putative relationship which means managers would be able to realize the paramount role of innovation as an integral part of achieving higher market performance. In addition, no support was found for the relationship between intelligence generation and responsiveness. This finding shows that not all obtained information can help managers in the decision-making process.

This study aims to enrich literature by developing a conceptual model to test the link between MO, innovation and market performance. The value of this study is to investigate the roles of flexible IT infrastructure, BA capabilities and market turbulence as the potential moderators in the proposed model. The results advance the understanding of the influence of BA capabilities on the link between intelligence dissemination and responsiveness. Findings also show innovation performance as remarkable and deemed valuable capability, leading to higher performance in marketing-related activities, particularly in highly turbulent markets.

Decision support (DS), as a traditional management concept, have had a remarkable role in competitiveness or survival of organizations and nowadays, business intelligence (BI), as a brand modern impression, has various contributions in supporting decision-making process. Although, a variety of benefits are expected to arise from BI functions, researches, and models that determining the effect of BI functions on the decisional and organizational benefits are rare. The purpose of this paper is to study the relationship between BI functions, DS benefits, and organizational benefits in context of decision environment.

This research conducts a quantitative survey-based study to represent the relationship between BI capabilities, decision support benefits, and organizational benefits in context of decision environment. On this basis, the partial least squares (PLS) technique employs a sample of 228 firms from different industries located in Middle-East countries.

The findings confirm the existence of meaningful relationship between BI functions, DS benefits, and organizational benefits by supporting 15 out of 16 main hypotheses. Essentially, this research provides an insightful understanding about which capabilities of BI have strongest impact on the outcome benefits.

The results can provide effective and useful insights for investors and business owners to utilize more appropriate BI tools and functions to reach more idealistic organizational advantages. Also it enables managers to better understand the application of BI functions in the process of achieving the specified managerial support benefits.

The purpose of this research is to explore the relationship between corporate social responsibility (CSR), brand image (BI) and brand equity (BE) in the banking industry of the Islamic Republic of Iran.

Using the deductive approach as the methodology and 213 valid questionnaires returned by customers of Iranian banks in four big cities of Iran, this study tests the relationship between CSR, BI and BE in eight hypotheses. The data were analyzed by the partial least squares method.

The results of this research show that CSR has a significant direct effect on BI. Also, BI has a significant direct effect on BE.

This research provides valuable insight for studying the relationship between CSR, BI and BE. The results of this study provide a better understanding of the role of CSR in customers’ attitudes and behaviors in the banking industry.

This paper aims to urge the new culture of assessment of the quality of library services among Iran academic libraries and to assess the overall services quality of libraries from the users' perspectives based on the LibQUAL model.

In this survey researchers used the LibQUAL model to assess service quality in the central libraries in engineering and technical governmental universities in Tehran city. For gathering data of libraries users, researchers used the LibQUAL questionnaire in a non‐electronic format and translated into Farsi.

It was found that library users were dissatisfied with their library building. But these libraries performed very well in the information control dimension. Furthermore, the examination of users' expectations showed that the proposition “Employees who are consistently courteous” was most important, and “Employees who instill confidence in users” had less importance.

Using the LibQUAL Survey has helped the university libraries in Iran to better serve their main user groups, and for the first time researchers used the non‐electronic format of LibQUAL questionnaire in the survey.

Playing video gaming is one of the most popular forms of leisure activities. This study looks into a specific video game genre, under the category of Multiplayer Online Battle Arena (MOBA) games. The purpose of this research is to investigate the factors that lead to the consumption of MOBA games. Three factors, imaginal, emotional and sensory experiences, are investigated through an integration of the hedonic consumption model, Technology Acceptance Model (TAM) and Uses and Gratification Theory (UGT).

The study analyses a sample of 292 MOBA game players using the PLS-SEM model. The study comprises two stages; in the first stage, an estimation model was used to test the constructs' quality and legitimacy. In the second stage, we assessed the theoretical model to test the relationship between the principle constructs.

The study found that factors related to emotional experiences, namely enjoyment, emotional involvement, and arousal, led to greater intention to play MOBA games. Similarly, two factors related to imaginal experiences, escapism and role projection, also positively impacted while fantasy carried a negative impact. The study also found that sensory experiences had a significant positive impact on the intention to play MOBA games. Lastly, a positive correlation was also found between the intention to use and usage behavior in MOBA games.

This study contributes to the theoretical understanding of the playful-consumption experiences of pleasure-oriented information systems (I.S.) that is MOBA games that derive behavioral intention to play MOBA games, which in turn determines the usage behavior of MOBA players. The study also incorporates the uses and gratification theory to uncover the needs and experiences that trigger MOBA games' behavioral intention and its further impact on gamers' usage behavior. The study also presents useful insights for game developers and other relevant stakeholders in game development.

The present study aims to specify, estimate, and validate the composite formative model of consumer eSports engagement (CeSE) through utilizing the composite confirmatory analysis (CCA) methodological approach.

To validate the composite model of CeSE, we collected the data using the Mturk online tool from eSports gamers located in European countries. The partial least squares based structural equation modelling (PLS-SEM) approach was employed using SmartPLS to conduct CCA analyses.

Using CCA, the authors first evaluated the reflective constructs (three first-order dimensions comprising cognitive, affective, and activation) and found reliable and valid reflective constructs. The authors then proceeded to assess the composite formative model of CeSE at higher-order level and resulted that CeSE is a valid composite model. Besides, the nomological network of CeSE with technology acceptance variables (e.g. behavioral intention and usage behavioral were estimated and found significant results).

This study contributes to consumer engagement literature in many directions. First, this study applies consumer engagement scale in the eSports context (i.e. eSports game brands). Second, this study notably specifies, estimates, and validates CeSE as composite model comprising cognitive, affective, and activation factors as first-order reflective constructs and CeSE as second/higher-order formative construct. Third, this study applies CCA guidelines to establish the CeSE as a composite formative measurement model and make significant methodology contribution. Fourth, this study extends the TAM model via studying CeSE as an important determinant of behavioral intention to play eSports games, which in turn impacts on eSports gaming/usage behavior.

The purpose of this paper is to propose the application of artificial neural networks (ANN) to predict overall bank customer satisfaction and to prioritize influencing factors on customer satisfaction.

Data are collected from 436 randomly selected customers at ten different branches of an Iranian bank using a questionnaire consisting of 51 questions. An exploratory factor analysis (EFA) is done on the collected data to determine those factors that influence customer satisfaction. A multilayer perceptron ANN model is developed using the factor scores from the EFA. The ANN model is trained and validated to predict overall bank customer satisfaction. In addition, a linear regression model is developed to predict customer satisfaction. Prediction accuracy of the ANN model is compared with that of the linear regression model. The developed ANN is then used to compare sensitivity of customer satisfaction to each influencing factor.

Nine different influencing factors are extracted by EFA. The factors include Fees and Loans, Prompt Service, Appearance, Technological Service, Responsiveness, Reliability and Trustworthiness, Employees’ Attitudes and Behaviors, Accessibility to Bank and Availability of Service, and Interest Rates. Training and validation results show that the ANN model has 73 percent higher accuracy compared to the linear regression model in predicting overall bank customer satisfaction. Factor prioritization results show that Fees and Loans, Appearance, and Prompt Service have the highest impact on customer satisfaction, respectively; interest rate and accessibility to bank and availability of service are the least dominant factors influencing overall bank customer satisfaction.

This study proposes a more reliable and accurate methodology to predict customer satisfaction when compared with regression-based methods. ANN can also be utilized by bank management systems to prioritize different influencing factors that affect the satisfaction level of bank customers.

This paper advances the knowledge on bank customer satisfaction by proposing application of artificial intelligence methods. A case study is discussed and results of the application of an ANN are compared with those of a commonly used statistical regression model.

The turbulent phase of COVID-19 has caused uncertainty as governments fail to develop coherent strategies for cutting emissions and are struggling to match the rhetoric of sustainable activities with actions (Barbier & Burgess, 2020; Cawthorn, Kennaugh, & Ferreira, 2021). In the recent past, firms have failed in their plans to decarbonise their key sectors such as the retail sector in the United Kingdom So far, retailers' commitment to achieving net zero emissions has been an important pledge but delivery is nowhere closer to their promises (Henriques, 2020). The firms' climate targets are not going to be met by magic as serious action is needed to fulfil the promises.

Fossil fuels have led to a drastic increase in carbon emissions in the world over the last decade. Firms championing cleaner energy and low carbon technologies are needed to cut emissions. Renewable energy sources such as wind energy can help reducing the dependency of fossil fuels (Boretti, 2020; Ebhota & Jen, 2020). Wind is an indirect form of solar energy which can provide environment-friendly option in uncertain times and can provide long-term sustainability of global economy. Solar energy technologies have the potential to decrease climate change through energy-related emissions (Li, Dai, & Cui, 2020). Increasing energy demand has initiated a focus on using hydrogen from water as a substitute for oil and fossil fuels (Boretti, 2020).

The first part of the chapter discusses theoretical perspectives of sustainable development and environmental performance with regards to three main issues: energy, water and carbon emissions, whereas the later part highlights the importance of solar technology as a low-polluted alternative to fossil fuels in the retail sector. Sustainable development of energy, water and environmental precautions such as reducing carbon emissions are of interest to wider branches of industries including retail, energy and water sector, governmental policymakers, researchers, educators and society. The purpose of this chapter is to increase the debate of the key issues of sustainable development regarding environment, energy and water in the modern times.

With big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).

The factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).

A total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.

This research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.

This research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.

The result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.

This research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.