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This study examines whether corporate social responsibility (CSR) initiatives positively impact customers’ selection of retail banks in Hong Kong (HK) and identifies which CSR domains affect customers’ selection of banks.

This study adopted a quantitative approach. Primary data were collected from 416 customers of 22 retail banks in HK. The theoretical framework of this study was developed from a literature review, prior studies by Oberseder et al. (2013 and 2014), and CSR initiatives implemented by leading retail banks in HK. Descriptive statistics and statistical tests were used to analyze the data.

The study found that CSR initiatives positively affect customers’ bank selection. CSR initiatives related to the customer and environment domains are likely to have a greater impact on customers than those related to the society domain and are not likely to significantly impact customers’ bank selection.

This study contributes to the CSR literature by offering enhanced insight into the dynamics of CSR and its effects on customer bank selection. Furthermore, this study tests consumers’ perceptions of CSR initiatives in each CSR domain in the banking sector in Hong Kong – a novel approach that has not been previously explored in existing studies. These findings can help banks review the effectiveness of their CSR initiatives and make informed decisions on which initiatives should pursue improved CSR performance and efficient resource allocation.

The study assess the selection ability and market timing skills of mutual fund and unit trust managers in Ghana.

The study uses an improved survivorship bias-free dataset of yearly after-fee returns of all mutual funds and unit trusts operating in Ghana from January 2011 to December 2019, cumulating in nine years of quantitative fund data. The authors assess Mutual funds and Unit trusts that ever existed, “alive” or “dead,” over the sample period in the study. The authors construct factor loadings to enable the application of multifactor models in the analysis. The authors apply the unconditional versions of the Jensen alpha, Fama-French three-factor, and Carhart four-factor models to determine the selection ability and market timing skills of 32 mutual funds and 17 unit trusts. The authors deploy HAC-consistent robust standard errors to the OLS estimations to subdue the effect of heterogeneity and autocorrelation.

The results indicate that, on average, mutual funds and unit trust managers possess market timing skills but no selection ability. When the results are decomposed into fund types, fixed-income and balanced mutual fund managers possess selection ability and market timing skills.

To the authors' best knowledge, this study is the earliest to examine the selection ability and market timing skills of both mutual fund and unit trust managers in Sub-Saharan Africa (SSA). It is also the earliest to construct factor loadings for the Ghana stock market.

This paper aims to analyse the valuation of cultural products and explores what this process means for organizations involved in their production and marketing.

The authors develop the arguments using a number of mini-cases and industry examples.

The main thesis is that the meaningfulness and value ambiguity of cultural products shift the focus of valuation away from the products themselves towards how certain agents in the socio-cultural environment identify and certify these products. This paper discuss how valuation takes place via selection systems and how the nature of cultural products drives the dominance of one selection system over others.

Theories on value creation needs to take consideration of the critical role played by the selection system instead of just the firms that produce these products.

Organizations engaged in producing highly symbolic products need to manage selection systems and related industry dynamics to establish an enduring competitive advantage.

Value creation is a collective social efforts. Every member of the society can play a central role in this process. Better engaging various member of the society to enable them actively participate in the value creation process is what organizations today need to consider, instead of just treating individuals in the society as a “customer” who only passively consume. This research calls for the true empowerment of every member of the society to facilitate collective creativity and participation in the value creation endeavour that benefits the entire society as a whole.

It is the first paper that has created a conceptual link between the type of selection system and product categories. In other words, it takes existing literature on value creation and selection system one step further by creating the alignment or match between types of selection system and types of product categories. Therefore, it offers academics and practitioners a much detailed understanding on how value creation is conducted across different product categories.

This study explores the main factors considered when shippers and container shipping lines select their import/export and transshipment ports. In the present study, 38 container port selection indices were chosen from the previous research and field interviews. The scores of the 38 items were collected via survey to the three major maritime/port market players: shippers, shipping lines, and container terminal operators. In order to analyze the different priorities imposed on the port selection factors by the three market players, the ANOVA method has been employed. The empirical test shows the different perceptions about port selection attributes among service suppliers and demanders. In addition, the 38 items have been categorized into seven key factors through an exploratory factor analysis. The ANOVA technique was employed again to analyze the perspective differences for the port selection factors among the market players. The results show that there are significant differences among the players assessing the importance of the three port choice factors: liners and terminal operators give more weight to ‘hinterland and terminal basic conditions’ than shippers; terminal operators do not take ‘line operation’ as seriously as carriers and shippers; the factor of ‘terminal operation’ is more significantly considered by liners and terminal operators than by shippers.

This paper aims to provide a systematic review of extant literature on supplier selection by identifying drivers of integration of Environmental Sustainability (ES) into supplier selection, with the aim of classifying them under broad categories for the development of a framework showing the interrelationships among them.

A search was ran for selected keyword themes using three powerful and rigorous search engines: Scopus, Google Scholar and Science Direct, to identify relevant articles from 12 peer-reviewed journals. These were desk reviewed through manual filtering to select drivers of integration of ES into supplier selection.

Thirty-one drivers identified from 41 relevant articles as propelling integration of ES into supplier selection were then classified into five categories: strong policy direction, high level of commitment, desire for high reputation, robust technology and availability of green products. This yielded a framework showing the interconnectedness among the drivers.

The study contributes to the body of knowledge by developing a classification of drivers of integration of ES into supplier selection. The interconnectedness brought to the fore a more subtle appreciation of the drivers of green integration, which could help expand current knowledge outside the narrow scope of isolated drivers. This study provides detailed analyses of drivers of green integration into supplier selection.

This paper provides a comprehensive review of drivers of integrating green into supplier selection, which is lacking in the body of knowledge on ES.

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.

Mutual funds are the second most preferred investment option in India and have garnered considerable research interest. The focus of Indian studies thus far has been restricted to the bottom-up approach of investing which rewards a fund manager for picking winner stocks and generates superior returns. While changing portfolio allocation as per varying macro-trends has been instrumental in generating superior returns, it has not been given the desired attention. This study addresses this important research gap.

The authors analyze the industry selection ability of the fund manager on a robust sample by decomposing alpha into alpha due to industry selection and alpha attributable to stock selection. Alpha estimates are computed on a robust sample of 34 open-ended Indian equity mutual funds for a 10-year duration 2011–2020 using three base models of asset pricing – single-factor, four-factor and five-factor alpha under panel data methodology.

The study leads us to four major findings. One, industry selection explains more than two-fifth of the alpha both in cross-section and time series of returns; two, industry selection exhibits persistence for more than four quarters across asset pricing model; third, younger funds have level playing when alpha from picking right industries is concerned; four, broad industry allocation continues to explain superior returns as sector allocation undergoes consolidation during ongoing COVID-19 pandemic and funds increase exposure to defensive stocks, consistent with folio allocations as per macroeconomic conditions.

The authors find strong evidence of persistence in the case of alpha attributable to the industry selection component, and the findings are consistent with the persistence results reported in the empirical literature. While some funds excel in stock-picking skills and others excel in picking the right industries, both skills together make for winner funds that attract larger investor flows as investors chase superior performance. The authors also find no evidence of diseconomies of scale in the case of industry allocation alpha generated by the fund managers.

The results suggest a fresh approach for investors while making mutual fund investment decisions; the investors can achieve superior returns by assessing industry selection skills as it tends to provide a more holistic picture concerning a perennial question – why some funds outperform and continue to contribute to investor's wealth?

Mutual funds have become a favored investment option for Indian investors more so as a disciplined investment option owing to dismal financial literacy rates. The study throws light on a relatively unaddressed dimension of choosing winner funds. The significance of right sector allocation assumed even more significance with the onset of the pandemic which lends further credence to the findings of the study.

Research has been conducted on secondary data extracted from a well-cited database for Indian mutual funds. Empirical analysis and conclusion drawn are based on authentic statistical analysis and adds to the existing literature.

Nowadays businesses tend to compete with rivals by improving capability to meet customer demands. One of the key to improve logistics efficiency of a firm is to select appropriate supplier. In the past, to select the most suitable supplier, most people evaluated performance by using average performance or variance from historical data but did not mentioned skewness. In other words, skewness impact on supplier performance is ignored by researchers and buyers. In fact, supplier with greatest average performance does not confirm to be the most suitable one because of uncertainties which make its performance skew either to the left or right, i.e., lower or higher than expectation. Therefore, this empirical study aims to discover and determine the important role of skewness on supplier selection problem. After identifying influential criteria on supplier selection, we analyze skewness effect on suppliers’ performance in each criterion by surveying real data of suppliers’ performances. Skewness effect can be rated in 3 levels; no effect, moderately effect, and highly effect. The results show that, there is only one criterion with no skewness effect, which is price. Criteria which have high skewed performance, for both of medium-sized and large-sized buyers, are lead time, product quality and reliability, and on-time delivery. Also, skewness has higher effect on suppliers’ performance of medium-sized buyers than large-sized buyers. The conclusion surprisingly shows that, skewness is the best index to distinguish between good and bad suppliers, while mean is the worst index.

This study aims to examine the role of supplier selection and supplier monitoring in public procurement efficiency in terms of cost reduction in Tanzania.

A structured questionnaire was used to collect cross-sectional survey data from 179 public procuring entities in Tanzania. Structural equation modelling (SEM) was used to analyse the collected data.

The findings revealed that supplier selection and supplier monitoring are positive and significant predictors of public procurement efficiency in terms of cost reduction.

This study was conducted in Tanzanian public procurement contexts, so generalisations should be made with caution. Also, this study collected cross-sectional data; other studies may consider longitudinal data.

This study provides procurement practitioners with insights into selecting the proper suppliers and embracing supplier monitoring to achieve procurement efficiency in terms of cost reduction.

This study examines the effects of supplier selection and supplier monitoring on procurement cost reduction as a measure of public procurement efficiency in the Tanzanian context. Consequently, it provides empirical evidence of supplier management practices in the public procurement context.

This paper aims to examine the interrelation between two innovating strategies (product and process) on total factor productivity (TFP) growth and the dynamic linkages between these strategies, for Colombia. The authors first explore whether ex ante more productive firms are those that introduce innovations (the self-selection hypothesis) and if the introduction of innovations boosts TFP growth (the returns-to-innovation hypothesis). Second, the authors study the firm’s joint dynamic decision to implement process and/or product innovations. The authors use Colombian manufacturing data from the Annual Manufacturing and the Technological Development and Innovation Surveys.

This study uses a four-stage procedure. First, the authors estimate TFP using a modified version of Olley and Pakes (1996) and Levinsohn and Petrin (2003), proposed by De Loecker (2010), that implements an endogenous Markov process where past firm innovations are endogenized. This TFP would be estimated by GMM, Wooldridge (2009). Second, the authors use multivariate discrete choice models to test the self-selection hypothesis. Third, the authors explore, using multi-value treatment evaluation techniques, the life span of the impact of innovations on productivity growth (returns to innovation hypothesis). Fourth, the authors analyse the joint likelihood of implementing process and product innovations using dynamic panel data bivariate probit models.

The investigation reveals that the self-selection effect is notably more pronounced in the adoption of process innovations only, as opposed to the adoption of product innovations only or the simultaneous adoption of both process and product innovations. Moreover, our results uncover distinct temporal patterns concerning innovation returns. Specifically, process innovations yield immediate benefits, whereas implementing both product innovations only and jointly process and product innovations exhibit significant, albeit delayed, advantages. Finally, the analysis confirms the existence of dynamic interconnections between the adoption of process and product innovations.

The contribution of this work to the literature is manifold. First, the authors thoroughly investigate the relationship between the implementation of process and product innovations and productivity for Colombian manufacturing explicitly recognising that firms’ decisions of adopting product and process innovations are very likely interrelated. Therefore, the authors start exploring the self-selection and the returns to innovation hypotheses accounting for the fact that firms might implement process innovations only, product innovations only and both process and product innovations. In the analysis of the returns of innovation, the fact that firms may choose among a menu of three innovation strategies implies the use of evaluation methods for multi-value treatments. Second, the authors study the dynamic inter-linkages between the decisions to implement process and/or product innovations, that remains under studied, at least for emerging economies. Third, the estimation of TFP is performed using an endogenous Markov process, where past firms’ innovations are endogenized.