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To date there is a dearth of research on strategic decision‐making in manufacturing small and medium sized firms. This research paper focuses on the key drivers of decision‐making and organisational performance: ownership, decision‐making (type and level) and employee deployment. These drivers are defined and operationalised.

The validity of the constructs used and their relevance was tested through the qualitative phase of the research – in‐depth interviews with chief executives and employer representative bodies. The survey instrument was also pilot tested.

The findings indicate that the sample can be divided into performance leaders and laggards. Over 75 per cent of the leaders are independently owned and over 66 per cent of the laggards are subsidiary firms. Accordingly, ownership is a key factor in overall performance. This is confirmed by the degree of profitability of both classifications.

The practical implications of the study are outlined in the paper. Chief executives are encouraged to focus on the aspects of ownership, decision making, and employee deployment that are associated with enhanced performance.

This study focuses on decision making and employee deployment according to ownership and overall performance – factors hitherto under researched in SMEs.

The purpose of this paper is to identify which factors influence mobile banking adoption and examine those factors for segmentation, using a sample of Indian consumers.

In total, 59 statements were identified based on a literature review, focus group discussions and personal interviews. Exploratory factor analysis was conducted to identify the relevant factors. An online survey of 367 mobile phone users in India was conducted. Confirmatory factor analysis was conducted using structural equation modeling. Appropriate statistical techniques (hierarchical cluster analysis, k-means cluster analysis) were used to segment the users. A profile of each segment was developed based on demographics, mobile banking services used, and attitude and intentions toward mobile banking. Further, a post hoc test was used to test the variation between the obtained clusters and user attitudes and intentions toward mobile banking. The demographic characteristics of users within each cluster were also examined.

Mobile users were segmented into three clusters based on their perceptions of various factors influencing mobile banking. These segments were labeled as technology adoption (TA) leaders, TA followers and TA laggards. The results show that both attitude and intentions toward mobile banking significantly differs across the three segments. In terms of relative positioning, TA leaders have the most favorable attitudes and intentions followed by TA followers, and TA laggards. Age was found to significantly influence TA and usage.

The findings of the study are based on responses from young, educated and salaried Indian consumers from large metro cities. Therefore, it is important to include respondents from smaller cities and towns to be able to generalize the findings. The sample is skewed toward users having accounts with private banks and hence, a balanced representation of respondents from public and private sector banks would help in identifying gaps pertaining to each sector. In future research, attempting to compare the results with other developing and developed countries may be beneficial.

The results offer service providers better knowledge about typical mobile banking user segments, providing banks with ideas for customizing their services to meet customer expectations.

This paper provides insights into factors that influence mobile banking adoption in India, which has not been investigated. In contrast to earlier studies conducted on internet banking, this study attempts to examine the perceptions, attitudes and intentions of mobile users. Although traditional TA models and theories of technology diffusion have been used, this study attempts to tailor the model specifically for mobile banking.

The purpose of this paper is to explore how the shopping mall environment impacts on hedonic and utilitarian shopping experiences, and approach behaviour of fashion leaders and followers.

Fashion shoppers' response and behaviour has been modelled in an invariant multigroup latent structural path analysis. Paths were initially constrained and then released as required. More than 300 usable questionnaires were acquired from a mall intercept in a regional urban middleclass shopping centre. Participants were probed on their attitude about fashion, perception of the shopping mall, present mood, shopping value and approach behaviour toward the mall.

The mall environment directly influences fashion leaders' hedonic shopping experience and approach behaviour. Fashion followers' hedonic shopping experience may be mood driven, while that of fashion leaders' is triggered by higher involvement cognitive processing.

This study was carried out in one fashion‐oriented urban mall in Montreal, and should be replicated to other locations and markets. A larger sample would allow the inclusion of additional constructs.

Mall owners and developers might appeal to fashion leaders through offering services that will speed up their shopping trip, using high‐tech methods to convey fashion information and by branding the mall. Fashion followers and laggards are likely to respond to experience‐oriented strategies that make their shopping trip more pleasurable.

Although fashion consumer groups have been studied from various perspectives, no research was found that investigates the integrated shopping experience of fashion shoppers in a shopping mall setting. This study fills the void.

Healthcare networks are becoming ubiquitous, yet it is unclear how hospitals with varying quality capabilities would fare by being affiliated with large healthcare networks. The purpose of this paper is to first consider the deductive configuration perspective and distinguish high and low quality hospitals by using clinical and experiential quality as two dimensions of quality capability. Next, it examines the impact of healthcare network size on operating costs of hospitals. Additionally, the paper investigates the interaction effect of hospital demand and healthcare network size on operating costs.

The paper uses a dataset that was created by combining five separate sources. Cluster analysis technique is used to classify hospitals into four groups – holistic quality leaders (high clinical and experiential quality capability), experiential quality focusers (low clinical quality capability and high experiential quality capability), clinical quality focusers (high clinical capability and low experiential quality capability), and quality laggards (low clinical and experiential quality capability). The authors test the research hypotheses by means of regression analyses after controlling for several contextual characteristics.

The results show that affiliation with large healthcare networks reduces operating costs for quality laggards, but increases these costs for experiential quality focusers and clinical quality focusers. The hypothesized positive relationship between healthcare network size and costs is not supported for holistic quality leaders. The authors find that clinical quality focusers and holistic quality leaders can complement higher utilization levels in their operations due to increased demand and healthcare network size to reduce their operating costs per day.

There has been increasing evidence suggesting that hospitals must carefully manage both clinical and experiential quality. By focusing on both clinical and experiential quality, unlike experiential quality focusers and clinical quality focusers, holistic quality leaders are not adversely affected by the size of their network. The results suggest that experiential quality focusers and clinical quality focusers should either embrace holistic quality management or restrict the size of their networks to maintain their quality level and to reduce coordination costs.

This study investigates the relationship between environmental disclosures made within the various sections of the 1992 annual reports of the 20 U.S. firms named by Fortune (Rice, 1993) as the ten leaders and ten laggards in environmental performance. We compared the disclosures made by the companies identified as leaders with the disclosures made by the companies identified as laggards, to determine if there was a relationship between companies' environmental performances and their environmental disclosures. We found that laggards made significantly more mandatory disclosures than did the leaders; however, there was little difference in the voluntary disclosures of the two groups. The leaders' disclosures were positively correlated between the mandatory and voluntary sections; there was virtually no correlation within the laggards. Leaders and laggards could be properly classified on the basis of their environmental disclosures.

In the current business environment, more uncertain than ever before, understanding consumer behavior is an integral part of an organization's strategic planning and execution process. It is the key driver for becoming a market leader. Therefore, it is important that all processes in business are customer centric. Marketers need to harness big data by engaging in data driven-marketing (DDM) to help organizations choose the “right” customers, to “keep” and “grow” them and to sustain “growth” and “profitability”. This research examines DDM adoption practices and how companies can aim to enhance shareholder value by bringing about “customer centricity”.

An online survey conducted in 2016 received 180 responses from junior, middle and senior executives. Of the total responses, 26% were from senior management, 39% from middle management and the remaining 35% from junior management. Industries represented in the survey included retail, BFSI, healthcare and government, automobile, telecommunication, transport and logistics and IT. Other industries represented were aviation, marketing research and consulting, hospitality, advertising and media and human resource.

Success of DDM depends upon how well an organization embraces the practice. The first and foremost indicator of an organization's commitment is the extent of resources invested for DDM. Respondents were divided into four categories; Laggards, Dabblers, Contenders and Leaders based on their “current level of investments” and “willingness to enhance investments” soon.

With storming digital age and the development of analytics, the process of decision-making has gained significant importance. Judgment and intuition too are critical to the process. Choosing an appropriate action cannot be done strictly on a rational basis.

The results of the study offer interesting implications for managing the growing sea of data. An iterative and incremental approach is the need of the hour, even if it has to start with baby steps, to invest in and reap the fruits of DDM. The intention to use any system is always dependent on two primary belief factors: perceived usefulness and perceived ease of use; however, attitudes and social factors are equally important.

There is a dearth of knowledge with regards to who is and is not adopting DDM, and how best big data can be harnessed for enhancing effectiveness and efficiency of marketing budget. It is, therefore, imperative to build a knowledge base on DDM practices, challenges and opportunities. Better use of data can help companies enhance shareholder value by bringing about “customer centricity”.

The purpose of this article is to evaluate revenue efficiency performance of life insurance companies in India. The study also compares if private or public insurance sector is more “revenue efficient”. Furthermore, the study determines the nature of return to scale (RTS) and identifies the leaders and laggards amongst insurance companies operating in India.

Revenue efficiency is calculated by employing data envelopment analysis – a non-parametric approach, on a data set of 24 insurance companies over the period 2013–2014 to 2017–2018.

The empirical results suggest that life insurance companies in India could generate only 34.4% of revenue, which is very less than what these are expected to generate from the same inputs. Majority of life insurance companies operating in India are operating at decreasing return to scale (DRS). There is a reduction in leaders and the highest proportion of companies is falling in the category of laggards.

As per the best knowledge of researchers, no empirical work has been carried out with respect to measuring the revenue efficiency of Indian insurance companies. The current study appropriately fills the gap by not only calculating the revenue efficiency scores of insurance companies in India but also provides insights into the causes of revenue inefficiencies. It also gives implications for efficient and effective management of insurance companies.

The purpose of this study is to explore, develop, and evaluate a new sustainable development goals (SDG) index that quantifies corporate social responsibility (CSR). By providing a granular perspective with clear justification for methods, this index is more applicable to academic research in comparison with the CSR indices published by private companies.

Focusing on the Fortune 500 companies in 2017, this study uses data from Bloomberg, ASSET4, and the Carbon Disclosure Project. A z-score was calculated for each variable, which was then aggregated according to the SDG indicator list to calculate each SDG score. Various robust analyses were conducted.

The SDG index shows that companies tend to score worse on environment-related goals compared with social goals. Furthermore, for each SDG, there are differences across industrial sectors, a finding that is enabled by the more granular approach of this index. Additionally, the leaders and laggards are identified for each of the SDGs.

This study identifies the methodological weaknesses of the existing CSR indices and introduces and evaluates an alternative index based on the SDGs. This alterative index provides methodological clarity and granularity of data, which were lacking in previously established indices.

This study explores the nature of critical success factors (CSFs) in UK budget hotel operations through a questionnaire survey designed to ascertain the relevance and importance of a range of factors referred to as critical in the extant academic and trade literature. From a usable sample of 239 questionnaires the results show that budget hotel unit managers from the leading brands largely agree with the criticality of the factors stated in the literature, this being verified via the independent t‐test and reliability analysis results that show most of the CSFs to be highly statistically significant (p<0.001) and the set of CSF items to be consistent (α=0.9360). The data analysis, via principal components analysis, also facilitates the creation of a parsimonious model that identifies two dimensions and five CSFs as being the “most critical”.

This paper aims to endeavour to assess revenue efficiency (RE) scores of Scheduled Commercial Banks operating in India. Differences in RE are studied across varying ownership as well. The study also determines the nature of return to scale of Indian SCBs as whole as well as classified across ownership. Number of banks operating as leaders and laggards has also been calculated.

RE of banks is calculated by using the non-parametric approach, namely, data envelopment analysis (DEA). Further, the differences in the efficiency scores are examined by applying Panel Tobit Regression.

The results of DEA suggest that none of the banks has ever achieved full RE score of 1 in any of the years under study. An inconsistent pattern of RE is seen. Private sector banks have performed better than their counterparts in public and foreign sector. Maximum number of banks operating on decreasing return to scale are from public sector, and the highest number of banks operating on constant return to scale belong to Foreign Sector. More number of banks operates as laggards in the Indian financial system. Thus, there still exists room for improvement for banks in all sectors.

With specific reference to India, less empirical work has been carried out with respect to RE. As only two studies so far from the literature are available that consider RE exclusively, namely, Ram Mohan and Ray (2004) and Bhatia and Mahendru (2015). However, Ram Mohan and Ray (2004) considered only the reformatory phase, whereas Bhatia and Mahendru (2015) analyzed the performance for specific points of time only. None of the study has been able to give any concrete findings according to sector-wise performance of banks in terms of RE parameters.