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For this study, the authors document day-of-the-week trading patterns of individual investors using a unique data set of NYSE-listed firms and discuss their influence on the Monday effect. It is found that Monday stock returns are generally lower than those of other weekdays and, on average, negative. Unlike previous researchers, the authors use actual trading data for individual investors rather than proxies to measure individual investor activity, such as the percentage of odd-lot trading. The results demonstrate that the trading activity of individual investors on Mondays is lower than previously documented. This finding contradicts the long-held belief that individual investors are most active on Mondays. In addition, the authors find that individual investors’ trading activity during the week broadly follows corporate announcement patterns. The least amount of firm-specific information is released on Friday, followed by Monday, Tuesday, Thursday and Wednesday. Accordingly, individual investors trade the least number of shares on Friday, followed by Monday, Tuesday, Thursday and Wednesday, strengthening the argument that individual investors trade on attention-grabbing stocks. Taken together, the authors’ findings challenge those of previous studies that hold individual investors responsible for the Monday effect. The paper aims to discuss this issue.

The authors use actual trading data for individual investors rather than proxies to measure individual investor activity, such as the percentage of odd-lot trading, to study the existence of Monday effect in stock prices.

The results show that the trading activity of individual investors on Mondays is lower than previously documented. This finding contradicts the long-held belief that individual investors are most active on Mondays. In addition, the authors find that individual investors’ trading activity during the week broadly follows corporate announcement patterns.

The authors find that individual investors’ trading activity during the week broadly follows corporate announcement patterns. The least amount of firm-specific information is released on Friday, followed by Monday. Accordingly, individual investors trade the least number of shares on Friday, followed by Monday, strengthening the argument that individual investors trade on attention-grabbing stocks. Taken together, the authors’ findings challenge those of previous studies that hold individual investors responsible for the Monday effect.

Financial advisors.

The authors find that individual investors’ trading activity during the week broadly follows corporate announcement patterns. The authors challenge the commonly hold view that individuals often make trading decisions during weekends and thus trade on Mondays, and find that the least amount of firm-specific information is released on Friday, followed by Monday. Accordingly, individual investors trade the least number of shares on Friday, followed by Monday. Taken together, the authors’ findings challenge those of previous studies that hold individual investors responsible for the Monday effect.

This study aims to examine investors’ herd behaviour for various calendar events and size-based stock portfolios in Pakistan. The authors consider three calendar effects, crisis (COVID-19 and financial crisis 2018–19), announcement of political news and popular calendar anomalies (month-of-the-year and day-of-the-week), and investigate the impact of stock size on calendar effect in terms of investors’ herd behaviour.

The study uses non-linear specification to capture herd behaviour using firm-level daily data for 496 stocks listed on Pakistan Stock Exchange over the period 2001–2020.

The results indicate herd formation during periods of COVID-19, financial crisis, political news announcements and January (month-of-the-year). The authors also observe significant herding for the biggest and smallest size stocks over complete period. However, the authors find more pronounced herding in big stocks during January as compared to the more noticeable herding in small stocks over complete period. The findings suggest that herding in small stocks is not the main cause of January herding and hint on the prevalence of significant institutional herding during January.

The stock prices destabilize because of the mimicking behaviour during crisis periods, days of political announcements and month of January. Implementation of insider trading laws and transparent information environment can help in reducing these effects and increasing market efficiency.

The authors consider the recent COVID period in our analysis. In addition, we provide new evidence on the possible impact of stock size on calendar effect in terms of herd behaviour, which, to the best of the authors’ knowledge, has not yet been documented in literature.

Presently, Indian sectors are manifesting a higher level of interdependency and making the economy more vulnerable to human-caused and natural disasters. COVID-19 pandemic creates a devastating effect on the world economy. The Indian economy was expected to lose around ₹ 32,000 crores every day during the first 21 days of complete lockdown. This motivates to conduct the research on how the COVID-19 pandemic affects the port logistics sector and how the effects of COVID-19 on port logistics propagate to other sectors owing to its interconnectedness and affect the economy of the country.

The purpose of the study is analyze how perturbation in one sector can affect the system of interdependent sectors and it is done with interdependency analysis. It uses Wassily Leontief’s inoperability input-output model (IIM) and interval programming (IP) to develop a framework. IP is used to address situations where assumptions are not valid because of uncertainties associated with disruptive events.

The model helps in describing how the effect of the COVID-19 pandemic in port logistics can propagate owing to the interconnectedness across other sectors. The model uses the latest five-year data available on the Organisation for Economic Co-operation and Development database. It uses metrics like inoperability and economic loss to study the consequences of COVID-19 pandemic on various sectors. This study also presents the ranking of the affected sectors based on their inoperability and economic loss

In the future study, other techniques like dynamic evolution, multiplex network analysis, analytical hierarchy process, pinch analysis, stochastic evolution and pinch graph could be integrated with input-output (I-O) modelling. Integrated stochastic evolution with an I-O model allows capturing the likelihood of the events; it includes probability distributions instead of point estimates for scenario parameters. Methods like dynamic evolution and multiplex network analysis can be introduced in future work to shed lights on interdependency among the sector, which could potentially provide additional insights for transport policy formulations.

This study discusses the theory, methodology and application of the IIM-IP model in the domain of port logistics. The developed IIM-IP model helps decision-makers to manage risk in port logistics. Firstly, it studies how different sectors are interconnected with each other. Secondly, it helps in identifying the most vulnerable sectors based on economic loss and inoperability. Thirdly, it provides the ranking of the sectors based on their economic losses.

Global trade depends on more complex, prolonged and larger port systems, where port logistics is a backbone for such operations. Ports are responsible for transferring more than 80 percent of the global trade. Port logistics are prone to being risk-oriented. The study proposes a model to study various port logistics barriers and their associated risks for emerging economies in the Industry 4.0 era.

The study develops a framework by integrating the fuzzy set theory, the evidential reasoning approach and the expected utility theorem for identifying the severity value of port logistics barriers under the Industry 4.0 era for emerging economies and prioritize them based on various perspectives. The study identifies multiple risks associated with the barriers, and intensity-based categorization of the risks is performed for risk profiling.

The study reveals that poor infrastructure, nonsupportive policy ecosystem, and lack of research and development are the top barriers that need immediate attention. A new approach has been proposed that changes the importance of perspectives, and 192 analytical experiments were done to study the changing behavior of barriers. The study also presents various types of risks associated with the selected barriers.

In future studies, other barriers can be discovered and studied to develop such models. To cover the entire spectrum of possibilities, belief degrees of the barriers could be used to study the barriers instead of changing the weights.

This study presents a quantification model to prioritize the barriers based on environmental, economic and operational perspectives. Further, the model helps create scenarios for decision-makers to improve port logistics performance and achieve sustainability. The study identifies various risks associated with port logistics barriers and allows decision-makers to take proactive actions.

This study contributes significantly to the literature on port logistics by developing a framework for determining the severity of the barriers in the Industry 4.0 era for emerging economies. Further, the study pinpoints various risks associated with port logistics, and risk profiling is carried out.

This paper aims to investigate whether the non-generally accepted accounting principles (GAAP) performance measures (NGMs) disclosure by high-tech initial public offering (IPO) firms signal firms’ efforts to maintain relatively high stock price levels before the expiration of the lock-up period to benefit insider selling.

The authors perform ordinary least squares and logit regressions using financial statement data and hand collected data on NGM disclosures for high-tech firms during the IPO process.

The authors find that the top executives of high-tech IPO firms with NGM disclosures are significantly more likely to sell and sell significantly more insider shares at the lock-up expiration than those of high-tech IPO firms without NGM disclosures. At the same time, while high-tech NGM firms have stock returns similar to their counterparts without NGMs for the period before the lock-up expiration, their stock returns are substantially lower after insider selling following the lock-up expiration.

By documenting the negative association between NGM disclosures and post-lockup expiration stock performance, the study highlights managerial deliberate optimism about the firm’s prospects which may not materialize. Hence, investors should take the NGM disclosures with a grain of salt.

This paper fills a notable void in the non-GAAP reporting literature by documenting a statistically and economically significant positive association between managerial equity trading incentives and NGM disclosures by high-tech IPO firms.

The disconnect between producers and consumers is a fundamental issue causing irregularities, inefficiencies and leakages in the agricultural sector, leading to detrimental impacts on all stakeholders, particularly farmers. Despite the potential benefits of Metaverse technology, including enhanced virtual representations of physical reality and more efficient and sustainable crop and livestock management, research on its impact in agriculture remains scarce. This study aims to address this gap by identifying the critical success factors (CSFs) for adopting Metaverse technology in agriculture, thereby paving the way for further exploration and implementation of innovative technologies in the agricultural sector.

The research employed integrated methodology to identify and prioritise critical success criteria for Metaverse adoption in the agricultural sector. By adopting a mixed-method technique, the study identified a total of 15 CSFs through a literature survey and expert consultation, focusing on agricultural and technological professionals and categorising them into three categories, namely “Technological”, “User Experience” and “Intrinsic” using Kappa statistics. Further, the study uses grey systems theory and the Ordinal Priority Approach to prioritise the CSFs based on their weights.

The study identifies 15 CSFs essential for adopting Metaverse technology in the agricultural sector. These factors are categorised into Technological, User Experience-related and Intrinsic. The findings reveal that the most important CSFs for Metaverse adoption include market accessibility, monetisation support and integration with existing systems and processes.

Identifying CSFs is essential for successful implementation as a business strategy, and it requires a collaborative effort from all stakeholders in the agriculture sector. The study identifies and prioritises CSFs for Metaverse adoption in the agricultural sector. Therefore, this study would be helpful to practitioners in Metaverse adoption decision-making through a prioritised list of CSFs in the agricultural sector.

The study contributes to the theory by integrating two established theories to identify critical factors for sustainable agriculture through Metaverse adoption. It enriches existing literature with empirical evidence specific to agriculture, particularly in emerging economies and reveals three key factor categories: technological, user experience-related and intrinsic. These categories provide a foundational lens for exploring the impact, relevance and integration of emerging technologies in the agricultural sector. The findings of this research can help policymakers, farmers and technology providers encourage adopting Metaverse technology in agriculture, ultimately contributing to the development of environment-friendly agriculture practices.

Continuous benchmarking of a closed-loop supply chain (CLSC) network is required to achieve circular economic viability for end-of-life vehicle (ELV) recovery programs for original equipment manufacturers (OEMs). This study develops a framework to assess and benchmark CLSC networks in ELV recovery programs, addressing the absence of a standard index and enabling circular economic viability for OEMs.

The study uses a Bayesian evidential reasoning approach (BERA) that helps decision-makers develop a reintegration index (RI) for the automobile CLSC network. To develop the index, a total of 15 factors related to the automobile CLSC are identified from the literature. Bayesian belief network (BBN) is used on those factors to generate conditional probabilities for different nodes of the BBN. With the opinion of 12 domain experts, the ERA is used to generate a score for each node. Finally, the Markovian tree is used on the scores to generate the RI for a particular CLSC network.

The analysis demonstrates that both operational and strategic actions aimed at ensuring customer satisfaction and retaining core components are quantified using a standardized index value for each alternative amidst uncertainty. Leveraging the BERA model, decision-makers can calculate RI values, providing them with the means to assess and regulate ratings for CLSC networks. This capability serves to bolster circular economic sustainability by facilitating informed decision-making within the automotive industry.

This framework offers a structured approach for decision-makers to evaluate CLSC networks in ELV recovery programs, allowing for adaptability to specific organizational objectives and facilitating informed decision-making in the automotive industry.

The study’s integration of expert insights and probabilistic modeling fills the gap in the literature by providing a comprehensive framework for assessing CLSC networks in ELV recovery programs, contributing to circular economic viability and strategic decision-making for OEMs.

The conflict in Russian Ukraine is a problem for the world economy because it hinders growth and drives up inflation when it is already high. The trade route between India and Russia is also impacted by the Russia-Ukraine crisis. This study aims to compile the most recent data on how the present global economic crisis is affecting it, with particular emphasis on the Indian economy.

This research develops a mathematical forecasting model to evaluate how the Russia-Ukraine crisis would affect the Indian economy when perturbations are applied to the major transport sectors. Input-output modeling (I-O model) and interval programing (IP) are the two precise methods used in the model. The inoperability I-O model developed by Wassily Leontief examines how disruption in one sector of the economy spreads to the other. To capture data uncertainties, IP has been added to IIM.

This study uses the forecasted inoperability value to analyze how the sectors are interconnected. Economic loss is used to determine the lowest and highest priority sectors due to the Russia-Ukraine crisis on the Indian economy. Furthermore, this study provides a decision-support conclusion for studying the sectors under various scenarios.

In future studies, other sectors could be added to study the Russian-Ukrainian crises’ effects on the Indian economy. Perturbation is only applied to transport sectors and could be applied to other sectors for studying the effects of the crisis. The availability of incomplete data is a significant concern in this study.

Russia-Ukraine conflict is a significant blow to the global economy and affects the global transportation network. This study discusses the application of the IIM-IP model to the Russia-Ukraine conflict. It also forecasts the values to examine how the crisis affected the Indian economy. This study uses a variety of scenarios to create a decision-support conclusion table that aids decision-makers in analyzing the Indian economy’s lowest and most affected sectors as a result of the crisis.

After completion of the case study, students will be able to understand the concept of clustering and identify clusters for improving capacity utilization, analyse transport routes to optimize logistics resources, analyse the impact of a full truckload on resource optimization, evaluate unused capacity and ascertain the impact of reverse milk run to reduce the same and apply clustering and reverse milk run to optimize the logistics resources.

The case study is about a freight forwarding company that offered end-to-end logistics solutions for the exporters based in India. Within a short time span, the company became one of the sought-after service providers for its clients. However, when the company planned to expand its business by expanding its client base, the efficiencies reduced and hurt the profitability of the company. It was all excellent with the limited number of clients, but as the number of distantly located clients surged, the operating costs increased. Trucks were running with partial loads, thus reducing efficiency. The rate of increase in cost surpassed the rate of revenue every time. The cost per mile of transportation was on the rise. The surging fuel prices were adding to the heat. In spite of being one of the first choices for clients, the company could not generate good profit margins. If they chose to increase prices, the company would have lost customers to the cheaper unorganized players in the market. It was time to choose between growth and survival. The company could not sustain itself without devising a mechanism to reduce costs. The company would not have sustained itself without devising a mechanism to reduce costs. To sustain in the business, the company had to device a mechanism to reduce costs. Whether to continue operating the conventional way or to transform? Was there a logistics strategy that would have improved transportation efficiency and reduced the costs for the company?

The case study is suitable for teaching post-graduate management courses in operations and logistics, supply chain management and supply chain analytics, as well as entrepreneurship-related courses.

Teaching notes are available for educators only.

CCS 9: Operations and logistics.