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Previous literature on sustainable supply chain management has largely adopted an instrumental view of stakeholder management and has focused on understanding the effect of powerful stakeholders who have a more decisive influence on an organization's supply chain decisions. Social enterprises have emerged as organizations that often aim to create impact by integrating marginalized stakeholders into their operations and supply chains. This study examines the trade-offs that social enterprises experience due to their moral stance toward stakeholder engagement, evidenced in their commitment to serving marginalized stakeholders, as well as the responses adopted to these trade-offs.

The study follows a theory elaboration approach through a multiple case study design. The authors draw on insights from stakeholder theory and use the empirical insights to expand current constructs and relationships in a novel empirical context. Based on an in-depth analysis of primary and secondary qualitative data on ten social enterprises, the authors examine how these organizations integrate marginalized stakeholders into various roles in their operations.

When integrating marginalized customers, suppliers and employees, social enterprises face affordability, reliability and efficiency trade-offs. Each trade-off represents conflicts between the organization's needs and the needs of marginalized stakeholders. In response to these trade-offs, social enterprises choose to internalize the costs through slack creation or vertical integration or externalize the costs to stakeholders. The ability to externalize is contingent on the growth orientation of the organization and the presence of like-minded B2B (Business-to-Business) customers. These responses reflect whether organizations accept the trade-offs at the expense of one or more stakeholders or if they avoid the trade-offs and find mutually beneficial solutions.

Building on the empirical insights, the authors elaborate on stakeholder theory with a focus on the integration of marginalized stakeholders by emphasizing a moral justification for stakeholder engagement, identifying the nature of the underlying trade-offs which can arise when various stakeholder needs are in conflict and examining the contingencies affecting organizational responses to these trade-offs.

This paper tests whether Bayesian A/B testing yields better decisions that traditional Neyman-Pearson hypothesis testing. It proposes a model and tests it using a large, multiyear Google Analytics (GA) dataset.

This paper is an empirical study. Competing A/B testing models were used to analyze a large, multiyear dataset of GA dataset for a firm that relies entirely on their website and online transactions for customer engagement and sales.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the intellectual property fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits. Frequentist A/B testing identified fraud in bounce rate at 5% significance, and bounces at 10% significance, but was unable to ascertain fraud at the standard significance cutoffs for scientific studies.

None within the scope of the research plan.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the IP fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits.

Bayesian A/B testing can derive economically meaningful statistics, whereas frequentist A/B testing only provide p-value’s whose meaning may be hard to grasp, and where misuse is widespread and has been a major topic in metascience. While misuse of p-values in scholarly articles may simply be grist for academic debate, the uncertainty surrounding the meaning of p-values in business analytics actually can cost firms money.

There is very little empirical research in e-commerce that uses Bayesian A/B testing. Almost all corporate testing is done via frequentist Neyman-Pearson methods.

East Asia's emergence as the forthcoming center of global manufacturing is leading nations in the region to reconsider their current and future role in the world economy. For its part, the Republic of Korea is pursuing a strategy to transform the country into the business hub of Northeast Asia. A central aspect in this strategy is the development of a regional “Pentaport” in the city of lncheon, which will consist of five components: an airport, a seaport, a business port, a technoport, and a leisure port. Hopefully, the Port of lncheon will be able to specialize in moving both coastal cargoes and transshipment cargoes to and from China and Japan using Short Sea Shipping (SSS). The first section of this paper provides an analysis of recent trade patterns between the Asian countries of the Asian-Pacific Economic Cooperation (APEC) agreement and the United States, with an emphasis on the Republic of Korea. The second part of the paper discusses current intermodal coastal shipping practices in the United States along the Gulf of Mexico and offers suggestions to the developers of Korea s Pentaport initiative; based upon observations in the United States, to maximize the likelihood of their success.

The purpose of this paper is to develop arguments for a public policy of requiring all large companies to make their tax returns publicly available. It is argued that such a policy would help to check tax avoidance, strengthen public accountability and secure fair competition.

The policy proposal rests on notions of transparency and public accountability.

The paper argues that the proposed policy is feasible.

The paper hopes to stimulate debates about the value of public filing of corporate returns and limits of public accountability.

The paper extends the range of public policies which might be able to check organised tax avoidance.

It is one of the few papers to call for public filings of large company tax returns.

Failed deliveries (i.e. deliveries not accomplished due to the absence of customers) represent a critical issue in B2C (Business-to-consumer) e-commerce last-mile deliveries, implying high costs for e-commerce players and negatively affecting customer satisfaction. A promising option to reduce them would be scheduling deliveries based on the probability to find customers at home. This work proposes a solution based on presence data (gathered through Internet of Things [IoT] devices) to organise the delivery tours, which aims to both minimise the travelled distance and maximise the probability to find customers at home.

The adopted methodology is a multi-method approach, based on interviews with practitioners. A model is developed and applied to Milan (Italy) to compare the performance of the proposed innovative solution with traditional home deliveries (both in terms of cost and delivery success rate).

The proposed solution implies a significant reduction of missed deliveries if compared to the traditional operating mode. Accordingly, even if allocating the customers to time windows based on their availability profiles (APs) entails an increase in the total travel time, the average delivery cost per parcel decreases.

On the academic side, this work proposes and evaluates an innovative last-mile delivery (LMD) solution that exploits new AI (Artificial Intelligence)-based technological trends. On the managerial side, it proposes an efficient and effective novel option for scheduling last-mile deliveries based on the use of smart home devices, which has a significant impact in reducing costs and increasing the service level.

The purpose of this study is to systematically review available state-of-the-art literature on comparative studies on Quick Commerce (Q-commerce) and E-commerce and their greenhouse gas (GHG) emissions.

The literature survey methodology is based on the funneling approach of Kitchenham (2004), where results are obtained according to inclusion and exclusion criteria. The literature review methodology used for this study covers the period from 2016 to 2022. The areas considered for the survey are operations, logistics and supply chain network design for the distribution of goods in e-business. After deciding on the criteria, a total of 140 articles were extracted from 9 journal articles that study e-commerce and environmental emissions.

The result of this study reveals that GHG emissions from both modes of shopping depend on various parameters such as speed of delivery, last-mile depot locations, logistics and vehicle efficiency, customers’ order patterns and average basket size. Furthermore, the findings also highlight the difference between Q-commerce and E-commerce supply chain networks.

This study only accounts for GHG emissions from logistics activities, but there are other sources of GHG emissions in the overall supply chain that are not taken into consideration. Supply chain/business analysts in Q-commerce companies might refer the findings from this study to measure GHG emissions from their operations.

This is the first study in the Q-commerce field that uses a structured approach to find relevant literature from the years 2016 to 2022 and focuses on GHG emission measurement.

Recent advancements in information and communication technologies have led to the rapid growth of electronic commerce market. In the United States, e-commerce retail sales for 2002 reached $45.6 billion, indicating an increase of 26.9% from 2001 while total retail sales increased 3.1% during the same period. Although e-commerce sales account for only 1.4% of total sales in this country, forecasts show that the magnitude of digital economy will continue to expand. The logistical requirements of e-commerce goods that extend to each customer's address stimulate greater complexity in traditional supply chain management, potentially causing higher costs for freight supply chain participants. To harness the economic potential of e-commerce, it is important to encourage the development of a freight transportation system that will support its steady growth, while avoiding the possible negative effects from the changes in freight transportation. Due to the intrinsic nature of e-commerce goods, advances in home delivery have the potential to promote the growth of e-commerce as well as to create sustainable urban freight transportation systems. Based on the case study of the United States, this paper presents an in-depth discussion of the key challenges arising in home delivery operations, and proposes potential solution strategies that will lead to more efficient and reliable home delivery systems.

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s absence causes significant loss of logistics efficiency, especially for perishable food. The purpose of this paper is to propose an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation.

The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation.

Computational experiments reveal that the proposed approach could reduce the total travel distance by 3-20 percent, and theoretically increase the success rate of first-round delivery approximately by18-26 percent.

The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics.

This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency.

The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers’ experience on online shopping.

Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper also provides a methodological approach to this line of research.

This paper proposes an approach to solve the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) in the context of the Physical Internet (PI) supply chain. The main objective is to minimize the total distribution costs (transportation cost and holding cost) to supply retailers from PI hubs.

Mixed integer programming (MIP) is proposed to solve the problem in smaller instances. A random local search (RLS) algorithm and a simulated annealing (SA) metaheuristic are proposed to solve larger instances of the problem.

The results show that SA provides the best solution in terms of total distribution cost and provides a good result regarding holding cost and transportation cost compared to other heuristic methods. Moreover, in terms of total carbon emissions, the PI concept proposed a better solution than the classical supply chain.

The sustainability of the route construction applied to the PI is validated through carbon emissions.

This approach also relates to the main objectives of transportation in the PI context: reduce empty trips and share transportation resources between PI-hubs and retailers. The proposed approaches are then validated through a case study of agricultural products in Thailand.

This approach is also relevant with the reduction of driving hours on the road because of share transportation results and shorter distance than the classical route planning.

This paper addresses the VRPSPD problem in the PI context, which is based on sharing transportation and storage resources while considering sustainability.