Search results

This study aims to review the literature on sharing economy logistics and crowd logistics to answer the three following questions: How is the literature on sharing economy logistics structured? What are the main trends in sharing economy logistics and crowd logistics? What are the future research options?

Bibliometric analysis is used to evaluate 85 articles published over the past 12 years; it identifies the top academic journals, authors and research topics contributing to the field.

The sharing economy logistics and crowd logistics literature is structured around several disciplines and highlights that some are more scientifically advanced than others in their subject definitions, designs, modelling and innovative solutions. The main trends are organized around three clusters: Cluster 1 refers to the optimal allocation of costs, prices, distribution and supplier relationships; Cluster 2 corresponds to business related crowdsourcing and international industry practices; and Cluster 3 includes the impact of transport on last-mile delivery, crowd shipping and the environment.

The study is based on data from peer-reviewed scientific journals and conferences. A broader overview could include other data sources such as books, book chapters, working papers, etc.

Future research directions are discussed in the context of the evolution from crowd logistics to crowd intelligence, and the complexities of crowd logistics such as understanding how the social crowd can be integrated into the logistics process. Our results are part of the crowd science and engineering concept and provide some answers about crowd cyber-system questions regarding crowd intelligence in logistic sector.

The purpose of this article was to provide an outline of the citation practices of “Evolving to a new dominant logic for marketing” by Vargo and Lusch (2004) to identify and discuss the most prominent research topics in which citations were used and to suggest future research based on the results of the analysis.

The authors used a comprehensive framework of citation practices based on iterations of previous literature to analyze the relevant literature, which they identified by accessing, systematically and rigorously, every available contribution matching a set of criteria. The authors then categorized these contributions and highlighted the main topics of research interest in each category.

The findings identify some of the factors in the continuous development of SDL, the way this new marketing logic permeated the scientific debate, the infusion of Vargo and Lusch (2004) into several contributions framed in the new logic or justified through it, and a general perception of a default reference. Additionally, the findings highlight the main topics of research interest in each category.

The analysis enabled the detection of the original paper's influence through advances in service studies, pollination into other fields of research and continuous scientific debate. The authors have highlighted several avenues for research and proposed future research directions.

This research analyzed the effects of the spread of the SDL cornerstone article and emphasized the advantage of using an in-depth approach to the analysis of studies through a framework applied to more than 4,600 studies.

The rapid advancement of digital technologies has fundamentally changed the competitive dynamics of the logistics service industry and forced incumbent logistics service providers (LSPs) to digitalize. As many LSPs still struggle in advancing their digital transformation (DT), the purpose of this study is to discover barriers and identify organizational elements and associated leading practices for DT success at LSPs.

This study utilizes a two-stage approach. Stage 1 is devoted to a literature review. Stage 2, based on multiple case studies, analyzes information collected across nine international and global LSPs.

This research derives a practice-based definition of DT in the logistics service industry, and it has identified five barriers, eight success factors and associated leading practices for DT. The main obstacles LSPs struggle with, are the complexity of the logistics network and lack of resources, while the main success factor is a leader having and executing a DT vision, and creating a supportive organizational culture.

The results contribute to the emerging field of DT within the logistics and supply chain management literature and provide insights for practitioners regarding how to effectively implement it in a complex industry.

The authors analyze DT from the perspective of LSPs, traditionally not viewed as innovative companies. This study compares their DT with that of other companies.

In the COVID-19 pandemic, consumers increasingly opt for, or are forced to, use home delivery services. The authors study retailers' decisions regarding “delivery mode”, which is about outsourcing (vs. insourcing) the delivery service to a traditional delivery company or an unbranded carrier and its effects on consumers' perceived overall quality, perceived hygienic quality, and subsequently, willingness to stay with the firm beyond the pandemic.

A pre-test, an experiment and a post-test were conducted with participants from the UK (Total N = 380).

The results of this study show that (1) in a pandemic, perceived hygienic quality overshadows perceived service quality as a key determinant of consumers' choices, and (2) while consumers have a relatively negative view of the hygienic level of unbranded carriers, they do not differentiate between traditional delivery carriers and retailer-branded carriers. Thus, they are equally interested in using the services of the latter ones.

This study shows that during a health crisis, consumers change their hierarchy of motivations to reflect the new protection motivations. The authors usher perceived hygienic quality as a variable that should be seriously considered as both a tactical and a strategic variable affecting the attractiveness of alternative home delivery methods and consumers' intentions to continue using them after the pandemic.

Digital freight forwarder (DFF) start-ups and their associated business models have gained increasing attention within both academia and industry. However, there is a lack of empirical research investigating the differences between DFFs and traditional freight forwarders (TFF) and the impact of digital start-ups on incumbents' companies. In response, this study aims to examine the key business model characteristics that determine DFFs and TFFs and propose a framework illustrating the extent to which digital logistics start-ups influence incumbent logistics companies.

Based on the primary data gathered from eight interviews with experts from start-ups' and incumbents' logistics companies, as well as secondary data, the authors identify the main factors of DFFs start-ups that have an impact on TFFs and analyze the similarities and differences in regard to the business model components' value proposition, value creation, value delivery and value capture.

The results show that differences between DFFs and TFFs appear in all four business models' components: value proposition, value creation, value delivery and value capture. In particular, the authors identify three main factors that need to be considered when assessing the impact of DFFs on TFFs: (1) the company size, (2) the market cultivation strategy and (3) the transport mode.

This is one of the first studies to specifically examine the key business model differences between DFFs and TFFs and to propose a conceptual framework for understanding the impact of digital logistics start-ups on incumbent companies.

The literature on warehouse performance assessments is mainly focussed on the efficiency and effectiveness of an action or activity due to customer demand and tailored fulfilment, with less attention being given to the performance measurement of each function of the warehouse and its overall productivity. Therefore, this study was aimed at revising the key warehouse performance metrics to a set of productivity measurement indicators that can be adopted internationally for benchmarking productivity performance.

A literature review and semi-structured survey questionnaire were used for this study. The importance of warehouse productivity performance was reviewed to revamp the measurement indicators. Through the use of a directed content analysis and descriptive analysis, an extensive study was carried out to analyze existing warehouse productivity indicators.

The findings of this study provide comprehensive references for practitioners and academicians for improving the classification of productivity measurements from existing key performance metrics for warehousing. Also, this paper highlights the warehouse resources related to the respective warehouse operation activities.

The study was limited to productivity performance indicators adapted from Staudt et al. (2015). Furthermore, the samples for this study comprised Malaysian academicians and practitioners in the related field. The findings can be adapted on a global scale as this study implemented general warehouse operation processes.

Consequently, the contributions of this study are that it provides relevant benchmarks for key productivity performance indicators in the warehousing sector that has worldwide applicability and the developed model provides a conceptual platform from which further theoretical and empirical developments can be carried out.

Intelligence level of a crowd network is defined as the expected reward of the network when completing the latest tasks (e.g. last N tasks). The purpose of this paper is to improve the intelligence level of a crowd network by optimizing the profession distribution of the crowd network.

Based on the concept of information entropy, this paper introduces the concept of business entropy and puts forward several factors affecting business entropy to analyze the relationship between the intelligence level and the profession distribution of the crowd network. This paper introduced Profession Distribution Deviation and Subject Interaction Pattern as the two factors which affect business entropy. By quantifying and combining the two factors, a Multi-Factor Business Entropy Quantitative (MFBEQ) model is proposed to calculate the business entropy of a crowd network. Finally, the differential evolution model and k-means clustering are applied to crowd intelligence network, and the species distribution of intelligent subjects is found, so as to achieve quantitative analysis of business entropy.

By establishing the MFBEQ model, this paper found that when the profession distribution of a crowd network is deviate less to the expected distribution, the intelligence level of a crowd network will be higher. Moreover, when subjects within the crowd network interact with each other more actively, the intelligence level of a crowd network becomes higher.

This paper aims to build the MFBEQ model according to factors that are related to business entropy and then uses the model to evaluate the intelligence level of a number of crowd networks.