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This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

The purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.

The definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.

(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.

The grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.

The new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.

Aim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle cosine relational degree model from the perspective of proximity and similarity.

Firstly, the algorithms of the generalized greyness of interval grey number and interval grey number vector are given, and its properties are analyzed. Then, based on the grey relational theory, the grey angle cosine relational model is proposed based on the generalized greyness of interval grey number, and the relationship between the classical cosine similarity model and the grey angle cosine relational model is analyzed. Finally, the validity of the model in this paper is illustrated by the calculation examples and an application example of related factor analysis of maize yield.

The results show that the grey angle cosine relational degree model has strict theoretical basis, convenient calculation and is easy to program, which can not only fully utilize the information of interval grey numbers but also overcome the shortcomings of greyness relational degree model. The grey angle cosine relational degree is an extended form of cosine similarity degree of real numbers. The calculation examples and the related factor analysis of maize yield show that the model proposed in this paper is feasible and valid.

The research results not only further enrich the grey system theory and method but also provide a basis for the grey relational analysis of the sequences in which the interval grey numbers coexist with the real numbers.

The paper succeeds in realizing the algorithms of the generalized greyness of interval grey number and interval grey number vector, and the grey angle cosine relational degree, which provide a new method for grey relational analysis.

With the use of the grey incidence analysis (GIA), indicators such as the absolute degree of grey incidence (ε_ij), relative degree of grey incidence (r_ij) or synthetic degree of grey incidence (ρ_ij) are calculated. However, it seems that some assumptions made to calculate them are arguable, which may also have a material impact on the reliability of test results. In this paper, the authors analyse one of the indicators of the GIA, namely the relative degree of grey incidence. The aim of the article was to verify the hypothesis: in determining the relative degree of grey incidence, the method of standardisation of elements in a series significantly affects the test results.

To achieve the purpose of the article, the authors used the numerical simulation method and the logical analysis method (in order to draw conclusions from our tests).

It turned out that the applied method of standardising elements in series when calculating the relative degree of grey incidence significantly affects the test results. Moreover, the manner of standardisation used in the original method (which involves dividing all elements by the first element) is not the best. Much more reliable results are obtained by a standardisation that involves dividing all elements by their arithmetic mean.

Limitations of the conducted evaluation involve in particular the limited scope of inference. This is since the obtained results referred to only one of the indicators classified into the GIA.

In this article, the authors have evaluated the model of GIA in which the relative degree of grey incidence is determined. As a result of the research, the authors have proposed a recommendation regarding a change in the method of standardising variables, which will contribute to obtaining more reliable results in relational tests using the grey system theory.

This paper examines the relationship between the extent and quality of the four dimensions of corporate social responsibility disclosure (CSRD) namely community, environment, workplace and marketplace with the long-run share price performance of Malaysian initial public offering (IPO) companies.

This study utilised secondary data by the content analysis of the annual reports and Datastream of 115 IPOs listed from 2007 to 2015 in Malaysia. The IPO’s performance was determined by calculating the return measures under the equally weighted and value-weighted schemes of the mean abnormal returns and buy-and-hold abnormal returns covering the three years post-listing using the event-time approach.

The findings demonstrate that Malaysian IPOs experience substantial overperformance and underperformance when both the IPO performance measures are benchmarked against the matched companies and market. The results indicated that the extent and quality of the community and environment CSRD dimensions are positively and significantly correlated to the IPO’s performance. On the other hand, the extent and quality of the workplace and marketplace CSRD dimensions are negatively and significantly correlated to the IPO performance.

Malaysian regulators could benefit from these findings in their endeavour to carry out a reform process on CSRD to improve its quality. The results of this study are important to investors, regulators, non-government organisations, communities and policymakers. They also enhance the understanding of companies about the importance of disclosing greater CSR information to improve their performance and profitability.

To the researchers' best knowledge, this study provides new insights into the association between CSRD and the performance of Malaysian IPO companies, which is considered important.

Supplier selection along with continuous evaluation of their performance is a crucial activity in healthcare supply chain management for effective utilization of scarce resources while providing quality service at an affordable price, and minimizing chances of stock-out, avoiding serious consequences on the illness or fatality of the patients. Presence of both qualitative and quantitative evaluation criteria, set of potential suppliers and participation of different stakeholders with varying interest make healthcare supplier selection a challenging task which can be effectively solved using any of the multi-criteria decision making (MCDM) methods.

To deal with various qualitative criteria, like cost, quality, delivery performance, reliability, responsiveness and flexibility, this paper proposes integration of grey system theory with a newly developed MCDM tool, i.e. mixed aggregation by comprehensive normalization technique (MACONT) to identify the best performing supplier for pharmaceutical items in a healthcare unit from a pool of six competing alternatives based on the opinions of three healthcare professionals.

While assessing importance of the six evaluation criteria and performance of the alternative healthcare suppliers against those criteria using grey numbers, and exploring use of three normalization procedures and two aggregation operations of MACONT method, this integrated approach singles out S5 as the most compromised healthcare supplier for the considered problem. A sensitivity analysis of its ranking performance against varying values of both balance parameters and preference parameters also validates its solution accuracy and robustness.

This integrated approach can thus efficiently solve healthcare supplier selection problems based on qualitative evaluation criteria in uncertain group decision making environment. It can also be deployed to deal with other decision making problems in the healthcare sector, like supplier selection for healthcare devices, performance evaluation of healthcare units, ranking of physicians etc.

Cotton fibre lots are graded and selected for yarn spinning based on their quality value which is a function of certain fibre properties. Cotton grading and selection has created a domain of emerging interest among the researchers. Several researchers have addressed the said issue using a few exponents of multi-criteria decision-making (MCDM) technique. The purpose of this study is to demonstrate a cotton selection problem using a recently developed measurement of alternatives and ranking according to compromise solution (MARCOS) method which can handle almost any decision problem involving a finite number of alternatives and multiple conflicting decision criteria.

The MARCOS method of the MCDM technique was deployed in this study to rank 17 cotton fibre lots based on their quality values. Six apposite fibre properties, namely, fibre bundle strength, elongation, fineness, upper half mean length, uniformity index and short fibre content are considered as the six decision criteria assigning weights previously determined by an earlier researcher using analytic hierarchy process.

Among the 17 alternatives, C9 secured rank 1 (the best lot) with the highest utility function (0.704) and C7 occupied rank 17 (the worst lot) with the lowest utility function (0.596). Ranking given by MARCOS method showed high degree of congruence with the earlier approaches, as evidenced by high rank correlation coefficients (Rs > 0.814). During sensitivity analyses, no occurrence of rank reversal is observed. The correlations between the quality value-based ranking and the yarn tenacity-based rankings are better than many of the traditional methods. The results can be improved further by adopting other efficient method of weighting the criteria.

The properties of raw cotton have significant impact on the quality of final yarn. Compared to the traditional methods, MCDM is reported as the most viable solution in which fibre parameters are given their due importance while formulating a single index known as quality value. The present study demonstrates the application of a recently developed exponent of MCDM in the name of MARCOS for the first time to address a cotton fibre selection problem for textile spinning mills. The same approach can also be extended to solve other decision problems of the textile industry, in general.

Novelty of the present study lies in the fact that the MARCOS is a very recently developed MCDM method, and this is a maiden application of the MARCOS method in the domain of textile, in general, and cotton industry, in particular. The approach is very simple, highly effective and quite flexible in terms of number of alternatives and decision criteria, although highly robust and stable.

In this study, we investigate what drives the MAX effect in the South Korean stock market. We find that the MAX effect is significant only for overpriced stocks categorized by the composite mispricing index. Our results suggest that investors' demand for the lottery and the arbitrage risk effect of MAX may overlap and negate each other. Furthermore, MAX itself has independent information apart from idiosyncratic volatility (IVOL), which assures that the high positive correlation between IVOL and MAX does not directly cause our empirical findings. Finally, by analyzing the direct trading behavior of investors, our results suggest that investors' buying pressure for lottery-like stocks is concentrated among overpriced stocks.

Although the phenomenon of the corporate social responsibility disclosure (CSRD) has derived the interest of several scholars, in recent years, the comparative studies between Islamic banks (IBs) regarding CSRD quantity versus quality have not been the subject matter of studies till now. In this perspective, this chapter aims to investigate the importance given by IBs to the quality and quantity disclosure of CSR. Moreover, it seeks to explore the impact of CSRD quality and quantity on the IBs' financial performance (FP). To meet these objectives, we used a sample of 59 IBs from 2011 to 2016 in the Arab world and non-Arab world. Then, by adopting the content analysis approach, the authors constructed two CSRD indexes (quality and quantity). The empirical results indicated that IBs give more importance to the qualitative disclosure than the quantitative. Our findings will be very helpful for the policymakers and the managers of IBs because maintaining a good CSRD policy increases the capacity of IBs to deal with possible reputational events, thus protecting their profits and financial results. As far as the comparison between the Arabian and non-Arabian IBs, based on financial reports and Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) governance standard N°7 is concerned, our study is among the first studies that provides two new CSRD indexes (quantity and quality).

Because of growing corporate tax scandals, there is an enhanced focus on corporate taxation by governments, institutions and the general public. Transparency in tax matters has been identified as critical for effectively managing and promoting socially responsible tax behaviour. This study aims to explore the impact of ownership structure, board and audit committee characteristics on corporate tax responsibility (CTR) disclosure.

This research collected data from the annual reports of Pakistani-listed firms over 12 years, from 2009 to 2020. Consequently, the data set encompasses a total of 1,800 firm-year observations. This study uses regression analysis to test the relationship between corporate governance and CTR disclosure.

The results show that board gender diversity, managerial ownership and audit committee independence promote tax responsibility disclosure. In contrast, family board membership, CEO duality, foreign ownership and family ownership negatively impact tax responsibility disclosure. Additional analyses reveal the specific information categories that produce the overall effects on tax responsibility disclosure and assess the moderating impact of family firms on the governance and CTR disclosure nexus.

Corporations can use the results to encourage practices that enhance transparency and improve the quality of disclosures. Regulatory authorities can use the findings to stipulate better protocols. Doing so will be vital for developing countries such as Pakistan to improve tax revenue and cultivate economic growth.

While this research represents, to the best of the authors’ knowledge, one of the first empirical investigations of the association between corporate governance and CTR, the results contribute to the corporate governance literature and offer fresh insights into CTR, an emerging dimension of corporate social responsibility.