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This paper aims to explore the interdisciplinary nature of coordination challenges in the logistic response to food safety incidents while distinguishing the food supply chain positions involved.

This adopts an exploratory qualitative research approach over a period of 11 years. Multiple research periods generated 38 semi-structured interviews and 2 focus groups. All data is analysed by a thematic analysis.

The authors identified four key coordination challenges in the logistics response to food safety incidents: first, information quality (sharing information and the applied technology) appears to be seen as the biggest challenge for the response; second, more emphasis on external coordination focus is required; third, more extensive emphasis is needed on the proactive phase in the logistic response; fourth, a distinct difference exists in the position’s views on coordination in the food supply chain. Furthermore, the data supports the interdisciplinary nature as disciplines such as operations management, strategy and organisation but also food safety and risk management, have to work together to align a rapid response, depending on the incident’s specifics.

The paper shows the need for comprehensively reviewing and elaborating on the research gap in coordination decisions for the logistic response to food safety incidents while using the views of the different supply chain positions. The empirical data indicates the interdisciplinary nature of these coordination decisions, supporting the need for more attention to the interdisciplinary food research agenda. The findings also indicate the need for more attention to organisational learning, and an open and active debate on exploratory qualitative research approaches over a long period of time, as this is not widely used in supply chain management studies.

The results of this paper do not present a managerial blueprint but can be helpful for practitioners dealing with aspects of decision-making by the food supply chain positions. The findings help practitioners to systematically go through all phases of the decision-making process for designing an effective logistic response to food safety incidents. Furthermore, the results provide insight into the distinct differences in views of the supply chain positions on the coordination decision-making process, which is helpful for managers to better understand in what phase(s) and why other positions might make different decisions.

The findings add value for the general public, as an effective logistic response contributes to consumer’s trust in food safety by creating more transparency in the decisions made during a food safety incident. As food sources are and will remain essential for human existence, the need to contribute to knowledge related to aspects of food safety is evident because it will be impossible to prevent all food safety incidents.

As the main contribution, this study provides a systematic and interdisciplinary understanding of the coordination decision-making process for the logistic response to food safety incidents while distinguishing the views of the supply chain positions.

The tender documents, an essential data source for internet-based logistics tendering platforms, incorporate massive fine-grained data, ranging from information on tenderee, shipping location and shipping items. Automated information extraction in this area is, however, under-researched, making the extraction process a time- and effort-consuming one. For Chinese logistics tender entities, in particular, existing named entity recognition (NER) solutions are mostly unsuitable as they involve domain-specific terminologies and possess different semantic features.

To tackle this problem, a novel lattice long short-term memory (LSTM) model, combining a variant contextual feature representation and a conditional random field (CRF) layer, is proposed in this paper for identifying valuable entities from logistic tender documents. Instead of traditional word embedding, the proposed model uses the pretrained Bidirectional Encoder Representations from Transformers (BERT) model as input to augment the contextual feature representation. Subsequently, with the Lattice-LSTM model, the information of characters and words is effectively utilized to avoid error segmentation.

The proposed model is then verified by the Chinese logistic tender named entity corpus. Moreover, the results suggest that the proposed model excels in the logistics tender corpus over other mainstream NER models. The proposed model underpins the automatic extraction of logistics tender information, enabling logistic companies to perceive the ever-changing market trends and make far-sighted logistic decisions.

(1) A practical model for logistic tender NER is proposed in the manuscript. By employing and fine-tuning BERT into the downstream task with a small amount of data, the experiment results show that the model has a better performance than other existing models. This is the first study, to the best of the authors' knowledge, to extract named entities from Chinese logistic tender documents. (2) A real logistic tender corpus for practical use is constructed and a program of the model for online-processing real logistic tender documents is developed in this work. The authors believe that the model will facilitate logistic companies in converting unstructured documents to structured data and further perceive the ever-changing market trends to make far-sighted logistic decisions.

This paper aims to use artificial neural network (ANN) methods to predict stock price crashes in the Chinese equity market.

Three ANN models are developed and compared with the logistic regression model.

Results from this study conclude that the ANN approaches outperform the traditional logistic regression model, with fewer hidden layers in the ANN model having superior performance compared to the ANNs with multiple hidden layers. Results from the ANN approach also reveal that foreign institutional ownership, financial leverage, weekly average return and market-to-book ratio are the important variables when predicting stock price crashes, consistent with results from the traditional logistic model.

First, the ANN framework has been used in this study to forecast the stock price crashes and compared to the traditional logistic model in the world’s largest emerging market China. Second, the receiver operating characteristics curves and the area under the ROC curve have been used to evaluate the forecasting performance between the ANNs and the traditional approaches, in addition to some traditional performance evaluation methods.

This study aims to identify the main challenges to achieving humanitarian logistics in the context of United Nations peace missions in sub-Saharan Africa and to present suggestions for overcoming the logistical gaps encountered.

The methodological approach of the work focuses on the comparative case study of the United Nations Mission in South Sudan, the United Nations Multidimensional Integrated Stabilisation Mission in the Central African Republic and The United Nations Organisation Stabilisation Mission in the Democratic Republic of Congo from 2014 to 2021. The approach combined a systematic literature review with the authors’ empirical experience as participant observers in each mission, combining theory and practice.

As a result, six common challenges were identified for carrying out humanitarian logistics in the three peace missions. Each challenge revealed a logistical gap for which an appropriate solution was suggested based on the best practices found in the case study of each mission.

This paper presents limitations when addressing the logistical analysis based on only three countries under the UN mission as a case study, as well as conceiving that certain flaws in the system, in the observed period, are already in the process of correction with the adoption of the 2016–2021 strategy by the UN Global Logistic Cluster. The authors suggest that further studies can be carried out by expanding the number of cases or using countries where other bodies (AU, NATO or EU) work.

To the best of the authors’ knowledge, this study is the first comparative case study of humanitarian logistics on the three principal missions of the UN conducted by academics and practitioners.

Digital technology has been widely applied in zakat collection. Millennials, who are now dominating the productive phase and at their peak carrier path, are the potential target for zakat collection as their number reached 31.3% of the Indonesian population. On the other hand, public and private zakat institutions have attempted to optimize the country’s zakat potential, reaching 233.6tn rupiahs, through development of a digital platform for zakat collection. However, the gap between the actual collection of zakat with its potential is still large. This study aims to analyse the factors affecting millennials in paying zakat through direct payment or through digital platform of private or public zakat institutions.

Multinomial logistic regression method, which signifies the contribution of this study, is used to analyse factors influencing millennials in their zakat payment. In addition, cross-tabulation is used to classify the characteristics of respondents. Respondents are selected conveniently through a digital questionnaire distributed in February–March 2021. Respondents are also selected purposively based on their experience in paying zakat through direct, private or public zakat institutions, which are consisted of 50 respondents per each category; thus, the total becomes 150 respondents.

Based on the results, three variables, namely, education, accessibility and age, are found to have a significant influence on zakat payment through online platforms provided by private zakat institutions. Meanwhile, variables that influence zakat payment through online platforms provided by public zakat institutions are education, accessibility and income. This study also finds that millennials have the highest probability to select online platforms provided by private zakat institutions as a channel of their zakat payment. However, the overall result shows that millennials tend to pay directly to the mustahik (zakat recipients) rather than via online platforms, presumably because of their limited zakat literacy.

The purposive sampling technique used to determine the research samples limits the generalization of the study.

This paper establishes a new approach in analysing millennials preference in their zakat payment with digital inclusiveness. The use of a multinomial logistic approach, which has not been widely applied in such research, strengthens the analysis that is relevant to the need of both private and public zakat institutions to analyse determinants of millennials in paying their zakat through online platform. This study can be used as a reference to formulate a more effective marketing strategy for zakat collection. This paper also serves as an estimate of the preference with some selected typical characteristics of millennials by using a multinomial logistic approach.

Formal payment through the zakat institution theoretically is more preferable than direct payment to mustahik (zakat recipients) in the zakat campaign. However, based on this research, despite digital marketing and platforms having been well-used by both private and public zakat institutions, the millennials still prefer direct zakat payment than through online platforms. The findings of this research suggest the importance of strengthening zakat literacy through a more effective digital marketing strategy of zakat institutions which target the millennials.

This study fills the gap in the literature on how millennials choose their zakat payment method, whether through digital platforms developed by private and public zakat institutions or directly to the targeted zakat recipients. The use of multinomial logistic regression approach adds the novelty of this research.

This study examines the effect of human and technological resource bundling on the financial and non-financial performance of third-party logistics (3PL) firms in India.

For achieving the research aim, 248 practitioners from India based 3PL firms were surveyed. The relationships between human and technology resources and firm performance were examined using structural equation modelling (SEM).

The results of empirical tests revealed that human and technological resources significantly enhance the performance of the 3PL firm. However, the firm's logistic capabilities related to track and trace, order management and final assembly do not mediate this relationship.

This study contributes by examining resource bundling in India's 3PL industry using empirical data and providing knowledge of the relationship between resources and business performance. It guides managers to consciously develop resource capabilities that influence firm performance.

This paper investigates how the channel leadership strategies develop a post-coronavirus disease (COVID-19) resilient agri-supply chain, which reduces supplier and retailer's price loss and enhances the logistics service quality level considering logistics outsourcing of agri-product especially for the rapidly changing market condition.

Based on the classical leadership theory, two channel leadership strategies, i.e. LPL and SL, are considered. The proposed framework first derives the equilibrium price and service quality level decision among the supplier, the logistics provider and the retailer. Then it compares both leadership strategies in terms of the equilibrium prices and service quality theoretically. This article also presents a case study of Arabian dates pricing and supply chain to test the theoretically derived propositions.

Selection of suitable leadership strategy is a critical factor for profit maximization of the supply chain drivers and proper optimization of equilibrium price and service quality. Here, the product's quality and the market's socio-economic condition play an important role in selecting a suitable leadership strategy. A random transformation of the physical market to an e-commerce portal creates a wide variation of the market's socio-economic parameters, affecting the equilibrium pricing and the logistics provider's service quality.

This study proposes a post-COVID-19 resilient agri-supply chain framework considering price and quality-dependent stochastic market demand, incorporating a wide range of socio-economic factors in the model to counteract the effect of rapid behavior change of agri-market due to COVID-19 norms. This research examines the effect of different channel leadership strategies to facilitate suitable decisions on prices and service quality and retrieve the profit of the supplier, retailer and logistics provider. The future models can incorporate competitiveness in logistics outsourcing, fourth-party logistics (4PL) and contract farming in the agri-supply chain. Each of the extensions can open avenues in different directions.

As the post-COVID-19 market and the customer behavior is randomly changing, and the traditional market is rapidly converting into supermarkets and e-commerce portals, this paper examines the model with a wide variety of e-commerce portals with multi-variation of product. It is conclusive that the product's quality and the market's socio-economic behavior significantly impact the equilibrium decision. The drivers of the supply chain must take them into account before choosing a particular channel leadership strategy.

This study considers a multi-product and multi-market (e-commerce) model by integrating a wide variety of products and the market's socio-economic parameters. The model is tested in a price and quality-dependent stochastic market condition, contributing to the literature by reconciling two different channel leadership strategies into the global logistics of fresh agri-product.

There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches.

The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class.

The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy.

The results facilitate halal restaurateurs in identifying customer review behavior.

Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews.

This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

This paper aims to measure the extent of digital financial inclusion (DFI) and examine the effect of socioeconomic characteristics on using government remittances and the adoption of digital financial services (DFS) during the COVID-19 pandemic.

The World Bank Global Financial Inclusion (Global Findex) database 2021 is used in this study, with a sample size of 3,000 Indian individuals. The study measured the demand-side analysis of DFI, namely, accessibility and usage of DFS with selected socioeconomic characteristics such as gender, age, income, education, being in the workforce and residential status of respondents. The dependent variable is binary in nature; therefore, the logistic regression model is used for the data analysis.

The results of the study reveal that individuals’ socioeconomic factors, such as female, all the age groups, tertiary education, third- and fourth-income quintile and workforce, are found to have a significant association with “accessibility,” an exogenous variable of DFS. Besides, respondents’ socioeconomic attributes, namely, female, tertiary education, income for all quintiles and workforce, are more likely to use DFSs in the COVID-19 pandemic. The study also finds the residential status of individuals is influencing the accessibility and usage of DFS.

The findings of the study provide valuable insights to the service providers and policymakers regarding the rapid expansion of DFS by digital infrastructure, simplifying the banking procedures and highlighting the importance of digital financial literacy to accomplish government goals through serving the unbanked population and also design strategies for achieving the objectives of Digital India: “Faceless, Paperless, and Cashless” of DFI across the country.

Notable studies used World Bank Findex survey data to explore the determinants of financial inclusion in general. This research is one among the few studies to explore the determinants of India’s DFI. Moreover, this study measured the effect of individual socioeconomic attributes on the adoption of DFSs during the COVID-19 pandemic, which has not been included in prior studies. Therefore, this study has added value to the existing literature on financial technology innovation and DFS for the sustainable development of emerging nations.

Outstanding unexercised stock options can motivate managers to engage in actions that increase the value of their company’s stock, including buying back their firm’s stock. The objective of granting stock options to managers is to align their interests with stockholders by tying a portion of their compensation to the company’s stock performance. However, unexercised stock options may have unintended consequences by providing managers with a vested interest in artificially boosting stock prices via stock buybacks. The primary objective of this research is to study the main factors that influence firms' buyback decisions amongst hospitality firms at a time when these firms were clamoring for taxpayer bailouts. Results from logistic regression seem to suggest that outstanding executive stock options are a major contributory factor in a firm’s buyback decision. Estimates also indicate that larger, more profitable firms will likely engage in stock buybacks. These findings survive a battery of tests.

The authors use logistic regression to predict the probability of a firm’s buyback decision based on a given set of exogenous explanatory variables.

The paper supports the hypothesis that buyback decisions are guided by the motive to prop support stock prices in the presence of outstanding restricted stock options/warrants granted to firms' executives.

The paper focuses on the buyback decision of U.S. hospitality firms. The results, therefore, might not be generalizable to firms in other industries or countries.

U.S. share repurchase corporate policy and government regulation needs to be revisited given the economic imperative for firms to invest in activities to restore employment and put them in a position for economic recovery.

Public criticism of the size, structure and form (i.e. loan vs grant) of COVID-19 bailouts warrants an examination of whether the factors that drive hospitality and tourism firms to repurchase shares support economic recovery.

Consistent with agency theory, the authors find a significant positive association between outstanding restricted stocks and a firm’s decision to support the stock prices by buying back shares.