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Secondary customers often experience secondary vulnerabilities that manifest in family-centred transformative services as other- and self-related customer needs. Yet, a relational perspective on primary and secondary customers’ needs is lacking. The study analyses secondary customers’ needs and their relationship to primary customers’ needs to enhance well-being in customer entities. The service inclusion lens is used to understand customers’ experiences of vulnerability.

The study uses an exploratory approach. The data consists of ethnographic observations and interviews of elderly residents (primary customers), their family members (secondary customers) and nurses in two nursing homes.

Primary and secondary customers’ needs are interrelated (or unrelated) in four ways: they are separate, congruent, intertwined or discrepant. The vulnerability experiences fluctuate in intensity and over time, individually reflecting on these need dimensions.

The study contributes to service research concerning customers’ experiences of vulnerability, secondary customers and their inclusion in services. Primary customers’ service inclusion may increase/decrease secondary customers’ service inclusion and their experience of vulnerability. Moreover, secondary customers’ inclusion is often necessary to foster primary customers’ inclusion and well-being.

Fostering service inclusion and well-being for primary and secondary customers requires balanced inclusion and acknowledging the needs of both groups. Service providers may need to act as moderators within customer entities if discrepant needs occur.

The study addresses the under-researched areas of family members’ customer needs, their relation to primary customers’ needs, experiences of secondary vulnerability and context-related vulnerability.

Reflecting on recent empirical developments as well as insights from regulatory state theory, the paper considers directions in which the regulatory state could develop in the post-COVID-19 era.

This is a de-contextualised analysis of regulatory developments drawing on the prior regulatory state literature and literature on post-crisis responses. Taking into account recent empirical developments related to the COVID-19 pandemic, the paper sets out, in a comparative context, scenarios for the future development of the regulatory state.

Predicting the direction in which the regulatory state will develop is challenging, particularly at this early stage. Yet, we provide a conceptual framework for thinking about possible futures of the regulatory state and how domestic and international factors might mediate these futures.

The paper provides a structured approach to the analysis of the regulatory state bringing together insights from the literature on the regulatory state, public management reform, and global regulatory shifts.

While there is burgeoning service literature identifying consumer vulnerabilities and questioning the assumption that all consumers have the resources to co-create, limited research addresses solutions for consumers experiencing vulnerabilities. Service systems can provide support for consumers but can also create inequities and experienced vulnerabilities. This paper aims to identify current and further research needed to explore this issue and addresses marketplace problems for consumers experiencing vulnerabilities.

This viewpoint discusses key issues relating to solving marketplace problems for consumers experiencing vulnerabilities. A call for papers focused on solving marketplace problems for consumers experiencing vulnerabilities resulted in a large number of submissions. Nine papers are included in this special issue, and each one is discussed in this editorial according to five emergent themes.

Vulnerabilities can be temporary, or permanent, and anyone can suddenly experience vulnerabilities. Inequities and vulnerabilities can be due to individual characteristics, environmental forces, or due to the structure of the marketplace itself. Solutions include taking a strengths-based approach to addressing inequities and using a multiple-actor network to provide support.

The recommendations addressed in this paper enable more positive approaches to solving marketplace problems for consumers experiencing vulnerabilities.

Taking a solutions-focused lens to research relating to vulnerabilities will contribute toward addressing inequities within the marketplace.

Increasingly, service literature is identifying inequities; however, very limited research addresses solutions for solving marketplace problems for consumers experiencing vulnerabilities. This paper suggests taking an approach focusing on strengths, rather than weaknesses, to determine strategies, and using the support of other actors (Transformative Service Mediators) where required.

Purpose: The study elaborates the contextual conditions of the academic workplace in which gender, age, and nationality considerably influence the likelihood of self-categorization as being affected by workplace bullying. Furthermore, the intersectionality of these sociodemographic characteristics is examined.

Basic Design: The hypotheses underlying the study were mainly derived from the social role, social identity, and cultural distance theory, as well as from role congruity and relative deprivation theory. A survey data set of a large German research organization, the Max Planck Society, was used. A total of 3,272 cases of researchers and 2,995 cases of non-scientific employees were included in the analyses performed. For both groups of employees, binary logistic regression equations were constructed. the outcome of each equation is the estimated percentage of individuals who reported themselves as having experienced bullying at work occasionally or more frequently in the 12 months prior to the survey. The predictors are the demographic and organization-specific characteristics (hierarchical position, scientific field, administrative unit) of the respondents and selected interaction terms. Using regression equations, hypothetically relevant conditional marginal means and differences in regression parameters were calculated and compared by means of t-tests.

Results: In particular, the gender-related hypotheses of the study could be completely or conditionally verified. Accordingly, female scientific and non-scientific employees showed a higher bullying vulnerability in (almost) all contexts of the academic workplace. An increased bullying vulnerability was also found for foreign researchers. However, the patterns found here contradicted those that were hypothesized. Concerning the effect of age analyzed for non-scientific personnel, especially the age group 45–59 years showed a higher bullying probability, with the gender gap in bullying vulnerability being greatest for the youngest and oldest age groups in the sample.

Interpre4tation and Relevance: The results of the study especially support the social identity theory regarding gender. In the sample studied, women in minority positions have a higher vulnerability to bullying in their work fields, which is not the case for men. However, the influence of nationality on bullying vulnerability is more complex. The study points to the further development of cultural distance theory, whose hypotheses are only partly able to explain the results. The evidence for social role theory is primarily seen in the interaction of gender with age and hierarchical level. Accordingly, female early career researchers and young women (and women in the oldest age group) on the non-scientific staff presumably experience a masculine workplace. Thus, the results of the study contradict the role congruity theory.

This paper proposes a holistic, proactive and adaptive approach to cybersecurity from a service lens, given the continuously evolving cyber-attack techniques, threat and vulnerability landscape that often overshadow existing cybersecurity approaches.

Through an extensive literature review of relevant concepts and analysis of existing cybersecurity frameworks, standards and best practices, a logical argument is made to produce a dynamic end-to-end cybersecurity service system model.

Cyberspace has provided great value for businesses and individuals. The COVID-19 pandemic has significantly motivated the move to cyberspace by organizations. However, the extension to cyberspace comes with additional risks as traditional protection techniques are insufficient and isolated, generally focused on an organization's perimeter with little attention to what is out there. More so, cyberattacks continue to grow in complexity creating overwhelming consequences. Existing cybersecurity approaches and best practices are limited in scope, and implementation strategies, differing in strength and focus, at different levels of granularity. Nevertheless, the need for a proactive, adaptive and responsive cybersecurity solution is recognized.

This paper presents a model that promises proactive, adaptive and responsive end-to-end cybersecurity. The proposed cybersecurity continuity and management model premised on a service system, leveraging on lessons learned from existing solutions, takes a holistic analytical view of service activities from source (service provider) to destination (Customer) to ensure end-to-end security, whether internally (within an organization) or externally.

This article aims at the sociological inquiry seeking to identify meanings ascribed to the term of vulnerability by official spokespersons, to explore a novel public health policy with reference to vulnerable populations and to trace its enactment with particular attention to vulnerable populations in Greece; finally a case of contest among the state and the civil society over refugees' rights will be located against public health politics and biopolitics in the context of the pandemic Covid-19.

The interpretivist perspective towards analysis of textual data is adopted. Discourse analysis and content analysis are applied to analyze four sets of data.

The main findings show: (1) ambiguity over the terminology, (2) insufficient policy design and policy enactment towards the protection of vulnerable populations' health, (3) an illuminative case of contest among civil society and the state against infringement of refugees' human rights which may interpreted in terms of a tradition of solidarity.

The Foucauldian notion of biopolitics provides the grounds to understanding how market prevails over life at the expense of those in greater need, and how the state, serving homo economicus, intensifies instead of alleviating health vulnerabilities.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

This paper explores how global pandemic crises affect the financial vulnerability of municipalities.

This paper is developed from the relevant literature an analytical framework to examine municipal financial vulnerability before a global pandemic crisis and in its immediate aftermath by mapping and systematizing its dimensions and sources. To illustrate how it can be used and evaluate its robustness and flexibility, such a tool was applied to Portugal and Italy, two countries that particularly suffered from the Covid-19 crisis.

The application of the analytical framework has shown how financially vulnerable municipalities are to global pandemic crises. Financial vulnerability relates to issues ranging from institutional design to internal financial conditions and the perception of the capacity to cope with a crisis. Results further reveal that vulnerability has an inherent contingent nature in time and space and can lead to paradoxical outcomes.

This paper provides a tool that can be useful for both academic and public policy purposes, to further appreciate municipal financial vulnerability, especially during crises.

Municipalities can use the framework to better manage their financial vulnerability, strengthening their anticipatory and copying capacities, while oversight authorities can use it to help municipalities become less financially vulnerable or, at least, more aware of their financial vulnerability.

Municipal financial vulnerability to global shocks has not been explored extensively. Also, the Covid-19 pandemic is different from previous global crises as it affected society overnight with the implementation of lockdown and social distancing measures.

The General Data Protection Regulation (GDPR) introduces significant data protection obligations on all organizations within the European Union (EU) and those transacting with EU citizens. This paper presents the GDPR privacy label and uses two empirical studies to examine the effectiveness of this approach in influencing consumers' privacy perceptions and related behavioral intentions.

The paper tests the efficacy of two GDPR privacy label designs, a consent-based label and a static label. Study 1 examines the effects of each label on perceptions of risk, control and privacy. Study 2 investigates the influence of consumers' privacy perceptions on perceived trustworthiness and willingness to interact with the organization.

The findings support the potential of GDPR privacy labels for positively influencing perceptions of risk, control, privacy and trustworthiness and enhancing consumers' willingness to transact and disclose data to online organizations.

The findings are useful for organizations required to comply with the GDPR and present a solution to requirements for transparent communications and explicit consent.

This study examines and demonstrates the efficacy of visualized privacy policies in impacting consumer privacy perceptions and behavioral intentions.