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The purpose of this paper is to obtain new empirical evidence about the connections between equity trading activity and five possible liquidity determinants: market capitalisation, dividend yield, earnings yield, company growth and the distinction between recently listed firms as opposed to more established ones.

The authors use a sample of 172 stocks from four European markets and estimate models using the entire sample data and different sub-samples to check the relative importance of the above determinants. The authors also conduct a factor analysis to re-classify the variables into a more succinct framework.

The evidence suggests that market capitalisation is the most important trading activity determinant, and the number of years listed ranks thereafter.

The positive relation between trading activity and market capitalisation is in line with prior literature, while the findings relating to the other determinants offer further empirical evidence which is a worthy addition in view of the contradictory results in prior research.

This study is of relevance to practitioners who would like to understand the cross-sectional variation in stock liquidity at a more detailed level.

The originality of the paper rests on two important grounds: the authors focus on trading turnover rather than on other liquidity proxies, since the former is accepted as an important determinant of the liquidity-generation process, and the authors adopt a rigorous approach towards checking the robustness of the results by considering various sub-sample configurations.

People often face constraints such as a lack of time or information in taking decisions, which leads them to use heuristics. In these situations, fast and frugal rules may be useful for making adaptive decisions with fewer resources, even if it leads to suboptimal choices. When applied to financial markets, the recognition heuristic predicts that investors acquire the stocks that they are aware of, thereby inflating the price of the most recognized stocks. This paper aims to study the profitability against the market of the most recognized stocks in Europe.

In this paper, the authors perform a survey and use Google Trends to study the profitability against the market of the most recognized stocks in Europe.

The authors conclude that a recognition heuristic portfolio yields poorer returns than a market portfolio. In contrast, from the data collected on Google Trends, weak evidence was found that strong increases in companies monthly search volumes may lead to abnormal returns in the following month.

The applied investment strategy does not account for transaction costs, which may jeopardize its profitability given the fact that it is necessary to revise the portfolio on a monthly basis. Despite the results obtained, they are useful to understanding the performance of recognition heuristic strategies over a comprehensive time horizon, and it would be interesting to depict its viability during different market conditions. This analysis could provide additional information about a preferable scenario for employing our strategies and, ultimately, enhance the profitability of recognition heuristic strategies.

Through the exhaustive analysis performed here on the recognition heuristic in the European stock market, it is possible to conclude that no evidence was found for the viability of exploring this type of strategy. In fact, the investors would always gain better returns when adopting a passive investment strategy. Therefore, it would be wise to assume that the European market presents at least a degree of efficiency where no investment would yield abnormal returns following the recognition heuristic.

The main objective of this paper is to study the performance of the recognition heuristic in the financial markets and to contribute to the knowledge in this field. Although many authors have already studied this heuristic when applied to financial markets, there is a lack of consensus in the literature.

This study focuses to develop a common humanitarian supply chain process model (HSCPM) that enables effective enterprise resource planning (ERP) systems for NGOs, and the study also investigates the role of modularity as a dynamic capability that supports creating such model.

A multifocus group study was performed as part of a larger project, the Frontline Humanitarian Logistics Initiative, aiming to establish a common data model that would serve as the backbone of humanitarian ERP systems. Fourteen international humanitarian organizations (IHOs) participated in the process, reaching a consensus on the structure of the process model.

An HSCPM was proposed based on the consensus reached across IHOs. Four degrees of customization differentiating between “generic,” “tailored,” “specific,” and “unique” processes are presented and discussed.

The findings show modularity applied to process as a mean to create dynamic efficiencies and position the modular process model within the dynamic capabilities framework, supporting supply chain responsiveness and expanding the literature on supply chain management (SCM), dynamic capabilities, and humanitarian logistics.

This research proposes a consensus-based data model, facilitating the advancement of ERP systems in the humanitarian context and lays a foundation for interoperability among ERP systems across diverse IHOs.

First attempt to elucidate the specific characteristics and unique processes defining an HSCPM, this study reached an unprecedented consensus for the humanitarian sector, setting the base toward an industry standard.

This paper aims to examine lean thinking in medical commodities supply chains by considering its applications and success factors. It determines the drivers and wastes of medical commodity supply chain, and the existing lean tools and practices together with their application in the supply chain processes. The paper also examined the challenges and success factors for effective lean application in the medical commodities supply chains.

The study used qualitative approaches, in-depth interviews and focus group discussions with key informants to form the basis for data collection. Through thematic analysis, the collected data were analyzed by developing themes reflecting the objectives of the study.

The main drivers for waste associated with the supply chain were demand management, supplier development, institutional framework and governance. The wastes were observed at the level of inventory, operation costs, transaction costs, delays in terms of service, commodity delivery time and quality. Digitalization, information technology and standardization were the tools for medical supply chain. Poor infrastructure, unreliable internet supply, environmental uncertainty and poor management support were challenges to realizing an effective supply chain.

Although the qualitative approach used in the study provides detailed information, a quantitative study covers a larger sample for generalization.

Capacity building and professionalism should be given a priority because the philosophy of lean focuses on waste removal and continuous improvement, which highly depends on the quality of human resource (Brito et al., 2020). Limited human resource capabilities in supply chain management will, therefore, result into poor operational efficiency, which are wasted. Moreover, systems interoperability is key waste minimization and, therefore, demands interventions.

The government under the Ministry of Health and other key sector ministries such as local and regional governments should better understand the role of the waste drivers and adopt system-wide reforms to support improvements to remove waste in the medical supply chain. For example, the current institutional framework creates an administrative block and hence leads to wastes. This bureaucratic procedure should be removed to minimize wastes along the chain.

This study is among the first studies to determine applicability and implementation of lean in a resource-constrained context. The paper identifies contextual factors for lean implementation. This paper focused on a holistic view of the entire supply chains to enhance a well-functioning supply chain in delivering health commodities.

The paper presents a research methodology that could be used to carry out a systematic literature review on the current state of the art of the technological development in the field of the digitalization and unphysicalization of supply chains (SCs). A three-dimensional conceptual framework focusing on the relationship between Digital Technologies (DTs), business processes and SC performance is presented. The study identifies the emerging practices and areas of SC management that could be positively affected by the implementation of DTs. With this in mind, the emerging practices have a high probability to be considered future best practices.

A systematic literature review was conducted on DTs in SC management. The methodology used aims to algorithmically and objectively standardize the information incorporated into thousands of scientific documents. Selected papers were analyzed to investigate the recent literature on SC digitalization and unphysicalization. A total of 87 DTs were selected to be analyzed and subsequently grouped into 11 macro-categories. 17 business processes linked to SC management are taken into account and 17 different impacts on SC management are presented. From a set of 1,585 papers, 5,060 emerging practices were collected and singularly summarized combining DT, business process and impact on SC performance.

A unique analytical perspective provided represents an important evolution when trying to organize the current literature on SC management. The widely used DTs in the practices and the most considered business processes and impacts are highlighted and described. The three-dimensional conceptual framework is graphically represented to allow for the emergence of the best combinations of DT, business process and impact on SC performance. These combinations suggest the most promising areas for the implementation of the emerging practices for SC digitalization and unphysicalization. Additional findings identify and define the most important contexts in which Big Data contributes to SC performance.

The research methodology used is offering progress through which to systemize the current practices as well as detect the potential of digitalization and unphysicalization under the three-dimensional conceptual framework. The paper provides a structured proposal for promising future research directions, assuming that the five research gaps as findings of this research could be the basis for prescriptions, as well as a future research agenda and theory development. Moreover, this research contributes to current managerial issues concerning SC management, referred to data and information management, efficiency and productivity of SC processes, market performance, SC relationship management and risk management in SC.

This paper aims to determine what the brand performance consequences of corporate social responsibility (CSR) activities would be during times of recession for well-known brands.

Based on signaling theory, this paper investigates if CSR activities serve to signal higher brand value for consumers via perceptions of better quality and greater differentiation, specifically during recessions. This study incorporates a representative longitudinal sample of known US firms for the analyses, which is accomplished through generalized method of moments estimations.

The findings empirically demonstrate that CSR initiatives during recessions are actually associated with increased perceptions of brand value. More specifically, during recessions, CSR initiatives such as charitable contributions provide a signal to customers of higher brand quality.

This study did not control for the costs of doing specific CSR activities that may be less visible to consumers.

While individual firms or managers may not be able to prevent recessions from happening, they can limit the negative impact of recessions on their performance by engaging in CSR activities (or refrain from cutting back) during these times.

Because CSR initiatives during recessions result in more favorable consumer perceptions of the brand, engaging in CSR aligns both social and managerial interests, owing to the economic gains from CSR investments.

During times of recession, some critics indicate that CSR may be an unaffordable luxury. On the contrary, this research shows that managers may want to consider CSR activities as a means of increasing the value of their brands, especially during economic recessions.

The metaverse is a virtual world where users can communicate with each other in a computer-generated environment. The use of metaverse technology has the potential to revolutionize the way businesses operate, interact with customers, and collaborate with employees. However, several obstacles must be addressed and overcome to ensure the successful implementation of metaverse technology. This study aims to examine the implementation of metaverse technology in the management of an organization's supply chain, with a focus on predicting potential barriers to provide suitable strategies.

Covariance-based structural equation modeling (CB-SEM) was used to test the model. In addition, artificial neural network modeling (ANN) was also performed.

The CB-SEM results revealed that a firm's technological limitations are among the most significant barriers to implementing metaverse technology in the supply chain management (SCM). The ANN results further highlighted that the firm's technological limitations are the most crucial input factors, followed by a lack of governance and standardization, integration challenges, poor diffusion through the network, traditional organizational culture, lack of stakeholder commitment, lack of collaboration and low perception of value by customers.

Because metaverse technology has the potential to provide organizations with a competitive advantage, increase productivity, improve customer experience and stimulate creativity, it is crucial to discuss and develop solutions to implementation challenges in the business world. Companies can position themselves for success in this fascinating and quickly changing technological landscape by conquering these challenges.

This study provides insights to metaverse technology developers and supply chain practitioners for successful implementation in SCM, as well as theoretical contributions for supply chain managers aiming to implement such environments.

Improvement of reverse supply chains requires accurate and timely information about the patterns of consumption. In the consumer context, the ways to generate and access such use-visibility data are in their infancy. The purpose of this study is to demonstrate how the Internet of Things (IoT) may be operationalised in the domestic setting to capture data on a consumer’s use of products and the implications for reverse supply chains.

This study uses an explorative case approach drawing on data from studies of six UK households. “Horizontal” data, which reveals patterns in consumers’ use processes, is generated by combining “vertical” data from multiple sources. Use processes in the homes are mapped using IDEF0 and illustrated with the data. The quantitative data are generated using wireless sensors in the home, and qualitative data are drawn from online calendars, social media, interviews and ethnography.

The study proposes four generic measurement categories for operationalising the concept of use-visibility: experience, consumption, interaction and depletion, which together address the use of different household resources. The explorative case demonstrates how these measures can be operationalised to achieve visibility of the context of use in the home. The potential of such use-visibility for reverse supply chains is discussed.

This explorative case study is based on an in-depth study of the bathroom which illustrates the application of use-visibility measures (UVMs) but provides a limited use context. Further research is needed from a wider set of homes and a wider set of use processes and contexts.

The case demonstrates the operationalisation of the combination of data from different sources and helps answer questions of “why?”, “how?”, “when?” and “how much?”, which can inform reverse supply chains. The four UVMs can be operationalised in a way that can contribute to supply chain visibility, providing accurate and timely information of consumption, optimising resource use and eliminating waste.

IDEF0 framework and case analysis is used to identify and validate four UVMs available through IoT data – that of experience, consumption, interaction and depletion. The UVMs characterise IoT data generated from a given process and inform the primary reverse flow in the future supply chain. They provide the basis for future data collection and development of theory around their effect on reverse supply chain efficiency.

This paper examines the financial ratios that may have a significant effect on the efficiency in Malaysian listed companies. Nine financial ratios measure seven variables which are firm visibility, tangibility, working capital, leverage, liquidity, productivity and profitability.

Data are collected from 108 public listed companies in Malaysia. The data extracted from companies' annual reports for three years 2012–2014. STATA software analysis is used to examine these relationships.

The results show each of tangibility and liquidity have negative relationships with efficiency ratio. In against of that, profitability, working capital and productively positively link to efficiency. Leverage which is measured by two ratios – Debt ratio and Debt equity ratio – shows mix results. Debt ratio shows a positive but not significant relationship with efficiency ratio and Debt equity ratio shows a negative significant relationship with efficiency ratio.

The results benefit companies, investors, economists and governments regulators in Malaysia-to understand the efficiency determinants, so help to make the right decision to enhance the efficiency level in companies which leads to enhance the amount of investments which in turn, enhance the country's economy in general.

This study differs than previous studies number of aspects: first the study covers a three years' period between 2012 and 2014, this period presents the movement of Malaysian current into depreciation with more than 45 percent of its value. Second, in the Malaysia context, this study examines new variables such as firm visibility, tangibility, and productivity. Third, the results of this study will help managers, shareholders, investors, regulators and other parties to make right decisions that will enhance the level of firm efficiency which enhances the investments and the economy of Malaysia.

In the recent dynamic market, supply chain disruptions are rapidly increasing with varied customer demand, technological changes, uncertain pandemic events etc. To overcome the unexpected disruptions, supply chain of each business should be resilient and pharmaceutical supply chain (PSC) is no exception. Motivated by the challenges and unexpected pandemic disruptions, this paper aims to examine the performance indicators (PIs) of the resilient PSC and to predict the resilience level for a certain time period in the context of Bangladesh.

The aim of this paper presents a structured framework based on the Delphi method, fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) and system dynamics (SD). The proposed methodology was validated using expert's inputs from the relevant field in Bangladesh. This study reveals the influential relationships of indicators and resilience level using fuzzy DEMATEL and SD to improve the resiliency.

Findings revealed that “Supply chain risk orientation”, “Visibility”, “Flexibility”, “Agility in supply chain” and “Collaboration” are the top five influential performance indicators for resilient PSC. The cause and effect relationship found that “IT capability”, “Flexibility”, “Supply chain network design”, “Resource availability”, “Supply chain risk orientation” and “Velocity” were in cause category which play a vital role for establishing resilient supply chain. SD approach has developed a model for predicting the resilience level of the supply chain.

This work is one of the initial contributions in the literature that has targeted on the identification and analysis of the significant PIs and predicting the resilience level of the PSC.