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The purpose of this paper is to propose a systemic methodology to assess the potential of and facilitate policies for the diffusion of a smartphone based service enabling supply chain (SC) operations in the e-grocery sector.

A System Dynamics (SD) model combining the Bass paradigm for innovation diffusion and an inventory management framework is developed. Semi-structured interviews are conducted to understand the industry business processes; a simple SD model is designed to capture the most important variables together with the relationships among them; a detailed SD model is calibrated and simulation outcomes are analysed.

The efficiency and reliability of the service drive its diffusion among producers and consumers, who in turn persuade retailers to adopt. The assessment methodology can be part of feasibility studies and marketing investigations in order to understand the impact of e-commerce tools on SC processes.

This contribution stresses the need to analyse how mobile information technologies may benefit all the business processes of the e-grocery SC, and not just one single process or stakeholder.

The approach offers a roadmap to identify the factors influencing the diffusion of mobile e-grocery services as well as the associated impacts on SC processes.

The work contributes to overcoming the lack of approaches studying the diffusion of e-grocery by taking into account all the relevant aspects and stakeholders involved and not only the consumer perspective.

The performance of a supply chain is characterized by its ability to remain market‐sensitive without losing the integration through the chain. One of the difficulties in designing and analysing a supply chain is that its processes are governed by the strategic attributes of the supply chain. In order to evaluate the alternatives that provide the route of performance improvement in a supply chain, an analytic network process (ANP)‐based model is proposed. The ANP‐based evaluation model is illustrated through an example of supply chain. It provides an effective framework for analyzing the alternatives affecting supply chain performance. It also provides the decision methodology to prioritise these alternatives, so that supply chain performance can be improved.

This article investigates the impact of the COVID-19 pandemic on the consumer preference for the attributes of online food delivery (OFD) services in India. It also shows how the order size influences the consumer's willingness to pay (WTP) for the attributes of OFD services.

This work incorporates a conjoint analysis-based approach to determine the consumer preference for the attributes of OFDs such as price, delivery time, restaurant rating and packing quality during the COVID-19 pandemic. The fractional factorial design is applied for the data collection. The relative importance of the attributes and the part-worth utility of the attributes' levels have been determined. Further, the utility associated with the attributes' levels is used to find the consumer's WTP for different attributes.

The COVID-19 pandemic has changed consumer preference from price to food and packing quality in India. When the order is small, consumers exhibit a higher preference to the delivery time than packing quality. In contrast, consumers show a higher preference to packing quality than delivery time with the increase in order size. The consumer's WTP attains the highest level in case of food quality, followed by convenience and packing quality. The WTP for the attributes rises with the increase in order size.

The insights highlight the need for the online food delivery industry to redesign the business framework in the post-pandemic era. The hygiene and safety measures maintained by the consumers during the pandemic have significantly changed their purchasing behaviour, raising their preference for service quality (food and packing quality) of the OFD services apart from price.

This work determines the consumers' utility for each attribute level of OFDs, along with their relative importance. Moreover, this study contributes to the existing literature by exhibiting the impact of the COVID-19 pandemic on the consumer preference and order size on consumer's WTP for the attributes.

Purpose – This chapter investigates the role of enabling organizations in the processes whereby institutional investors collectively influence corporate managers on Environmental, Social and Governance (ESG) issues. We develop a framework combining stakeholder and collective action theory to explain how institutional investors influence corporations through collective engagement and to specify how enabling organizations influence this process.

Methodology/approach – To evaluate our framework, we investigate the role of the organizational platform provided by the United Nations-backed Principles for Responsible Investment (PRI) initiative in supporting institutional investors’ collaborative engagement with corporations on ESG issues.

Findings – Our findings clarify how investors enhance their sources of power, legitimacy and urgency and attract managers’ attention through collaborative engagement, and show how they manage these attributes to reshape the legitimacy and urgency of their claims in the eyes of managers. Our results also show how enabling organizations such as the PRI initiative facilitate the emergence of collective action by lowering barriers to entry and providing a mobilizing structure, support collaborative efforts by adding their own legitimacy, normative power and persistence to the collaborative engagement, and create conditions for a lasting dialogue between investors and managers by providing a hybrid organizational space.

Social implications – In explaining how to enhance institutional investors’ collective action on ESG issues, this paper shows how we could reorient financial market forces toward sustainability.

Originality/value of paper – The paper benefited from a unique access to confidential and internal data from the UN-PRI initiative and provides a new framework.

Twitter is a popular microblogging service which has proven, in recent years, its potential for propagating news and information about developing events. The purpose of this paper is to focus on the analysis of information credibility on Twitter. The purpose of our research is to establish if an automatic discovery process of relevant and credible news events can be achieved.

The paper follows a supervised learning approach for the task of automatic classification of credible news events. A first classifier decides if an information cascade corresponds to a newsworthy event. Then a second classifier decides if this cascade can be considered credible or not. The paper undertakes this effort training over a significant amount of labeled data, obtained using crowdsourcing tools. The paper validates these classifiers under two settings: the first, a sample of automatically detected Twitter “trends” in English, and second, the paper tests how well this model transfers to Twitter topics in Spanish, automatically detected during a natural disaster.

There are measurable differences in the way microblog messages propagate. The paper shows that these differences are related to the newsworthiness and credibility of the information conveyed, and describes features that are effective for classifying information automatically as credible or not credible.

The paper first tests the approach under normal conditions, and then the paper extends the findings to a disaster management situation, where many news and rumors arise. Additionally, by analyzing the transfer of our classifiers across languages, the paper is able to look more deeply into which topic-features are more relevant for credibility assessment. To the best of our knowledge, this is the first paper that studies the power of prediction of social media for information credibility, considering model transfer into time-sensitive and language-sensitive contexts.

The purpose of this study is to build a retail‐banking specific quality scale and, through its examination and comparison with the SERVQUAL and BSQ metrics that are currently used in banking, to deepen understanding of quality determinants in the industry. Furthermore, the study is set to provide additional input to the debate over generic against setting/industry/time‐specific quality metrics.

The study is implemented through a two‐stage process of literature review and empirical survey. Evidence drawn from Greek retail banking, through a specially designed research tool, is analyzed through reliability, factorial and regression analysis to determine the scale's item and factorial structure and assess its reliability and validity.

The BANQUAL‐R metric is introduced, with key elements assurance/empathy, effectiveness, reliability and confidence, a combination of SERVQUAL and BSQ dimensions. Findings back the setting‐specific approach of service quality and the notion that SERVQUAL provides the skeleton on which setting‐specific scales should be built.

Bank managers are provided with a reliable and valid metric of service quality in retail banking. Its dimensionality implies that under credit‐crunch conditions service delivery should be directed towards reinstating customers' trust and confidence that are put in danger. Banks should redirect resources from tangibles to the human contact‐related service elements.

Although the subject of “service quality measurement” is extensively researched, the continuously changing marketing environment calls for an ongoing assessment of quality factors. With respect to its academic value, the study accumulates knowledge that will eventually outgrow the boundaries of academia and pervade management.

A good customer experience means meeting the customer expectation. Thus, unexpected customer experience is usually a good point to initiate improvement or innovation for product or service design. Attempting to enhance the customer experience in the customer journey, this study aims to demonstrate a customer journey centred service design approach to receive the design requirements based on customers' needs and to use a systematic approach to generate solutions.

A holistic service design method named 3E model was proposed. It integrates customer experience journey map (CXJM), the theory of inventive problem solving (TRIZ) and service assembly and service replacement mechanism into three design stages. In stage 1, CXJM is enhanced with emotional range analysis to identify the customer pain points as well as customers' requirements (CRs) in exhibition, tourism and hotel sectors for initializing service design. Stage 2 investigates the specific design requirements (DRs) of the smart exhibition system and the contradictions. Then, the innovative principles were analyzed. In Stage 3, expected exhibition service system was designed.

The new service system which named the smart expo system based on information and communication technology (ICT) is proposed. It consists of “Tourism Link assists”, “i-Kaohsiung hotel service center”, “Smart AEC” and “O2O e-tickets”.

The proposed 3E model builds a systematic and coherent design method for the smart exhibition service area. It provides the linkage and action-oriented guidance from customer pain points, service parameters, innovative principles to solutions.

The recent influence of the mass media communication emphasized the visionary image of the music and eventually the close relationship between the music and fashion industries was formed. Consumers who share a similar taste in music could relate to each other more actively, and as a result, they began to develop similar aesthetic views and emotions. From this matter, the aim of this research is to conduct a survey that would go over a variety of music and fashion preferences of the consumers in order to analyze the relationship between the two. The main objective was to investigate how strongly the preference styles of music and fashion match each other and find similarity of their sensibility.

The participants were directed to choose their preference styles of music and fashion, grade their interest in music and fashion and evaluate their sensibilities by grading their feelings about ten polar paired adjectives.

There indeed is a close relationship between music and fashion. People who are more interested in music and fashion tend to have a stronger correlation between their preferences.

Through the analysis of variance it was found that the sensibility associated with different music and fashion styles are not the same. Furthermore, the authors could group the sensibility words into three common factors. Here it was found that music requires more diverse expressional adjectives as representatives compared to fashion.

The close relationship between preference styles of the two artistic elements and the similarity or their sensibility could be visualized through the distance of each style and adjectives in a correspondence map.

The purpose of this study is to determine the factors that impact the trust of microenterprises in online selling platforms and cargo carriers and examine the consequences of a lack of trust.

This study adopts a qualitative exploratory approach and uses grounded theory to generate insights based on interviews with 27 microenterprise owners selling internationally on an online selling platform.

The results show that a lack of competence, an absence of an integrated claim system and a lack of transparency are the main factors affecting sellers’ trust in online selling platforms. The relationship between the sellers’ intention to continue to use the online selling platform and their trust in the platform was found to be moderated by switching costs.

This study is limited in that the results were mainly based on the sellers’ perspectives although the phenomenon of interest involves various actors. To mitigate this limitation and cross-check the data, the customer reviews and some of the sellers’ account statistics were also analyzed.

This study introduces the sellers’ perspectives on the dynamics of supply chain management in international micro trade. These dynamics provide a guideline for how to build and manage an online selling platform targeting microenterprises.

Unlike previous studies, this study examines online transaction behaviors from the standpoint of sellers, not buyers. Moreover, it is the first study examining the damaged or lost shipments within the context of online transactions in international micro trade.

The novel Coronavirus (COVID-19) pandemic, which started in late December 2019, has spread to more than 200 countries. As no vaccine is yet available for this pandemic, government and health agencies are taking draconian steps to contain it. This pandemic is also trending on social media, particularly on Twitter. The purpose of this study is to explore and analyze the general public reactions to the COVID-19 outbreak on Twitter.

This study conducts a thematic analysis of COVID-19 tweets through VOSviewer to examine people’s reactions related to the COVID-19 outbreak in the world. Moreover, sequential pattern mining (SPM) techniques are used to find frequent words/patterns and their relationship in tweets.

Seven clusters (themes) were found through VOSviewer: Cluster 1 (green): public sentiments about COVID-19 in the USA. Cluster 2 (red): public sentiments about COVID-19 in Italy and Iran and a vaccine, Cluster 3 (purple): public sentiments about doomsday and science credibility. Cluster 4 (blue): public sentiments about COVID-19 in India. Cluster 5 (yellow): public sentiments about COVID-19’s emergence. Cluster 6 (light blue): public sentiments about COVID-19 in the Philippines. Cluster 7 (orange): Public sentiments about COVID-19 US Intelligence Report. The most frequent words/patterns discovered with SPM were “COVID-19,” “Coronavirus,” “Chinese virus” and the most frequent and high confidence sequential rules were related to “Coronavirus, testing, lockdown, China and Wuhan.”

The methodology can be used to analyze the opinions/thoughts of the general public on Twitter and to categorize them accordingly. Moreover, the categories (generated by VOSviewer) can be correlated with the results obtained with pattern mining techniques.

This study has a significant socio-economic impact as Twitter offers content posting and sharing to billions of users worldwide.

According to the authors’ best knowledge, this may be the first study to carry out a thematic analysis of COVID-19 tweets at a glance and mining the tweets with SPM to investigate how people reacted to the COVID-19 outbreak on Twitter.