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Psychometric analyses of self-administered questionnaire data tend to focus on items and instruments as a whole. The purpose of this paper is to investigate the functioning of the response scale and its impact on measurement precision. In terms of the response scale direction, existing evidence is mixed and inconclusive.

Three experiments are conducted to examine the functioning of response scales of different direction, ranging from agree to disagree versus from disagree to agree. The response scale direction effect is exemplified by two different latent constructs by applying the Rasch model for measurement.

The agree-to-disagree format generally performs better than the disagree-to-agree variant with spatial proximity between the statement and the agree-pole of the scale appearing to drive the effect. The difference is essentially related to the unit of measurement.

A careful investigation of the functioning of the response scale should be part of every psychometric assessment. The framework of Rasch measurement theory offers unique opportunities in this regard.

Besides content, validity and reliability, academics and practitioners utilising published measurement instruments are advised to consider any evidence on the response scale functioning that is available.

The study exemplifies the application of the Rasch model to assess measurement precision as a function of the design of the response scale. The methodology raises the awareness for the unit of measurement, which typically remains hidden.

This study explores how compassionate managerial leadership style can help to mitigate workplace stressors and alleviate stress experiences among employees — particularly in an extreme situation, such as the current global COVID-19 pandemic. The study's context is Nigeria's banking, manufacturing and healthcare sectors, which have a history of high employee stress levels.

Using a qualitative, interpretive methodology, the study adopts the thematic analysis process (TAP) to draw and analyse data from semi-structured telephone interviews with 10 banking, 11 manufacturing and 9 frontline healthcare workers in Nigeria.

It was found that a compassionate managerial leadership can drive a considerate response to employees' “fear of job (in)security”, “healthcare risk” and concerns about “work overload, underpayment and delayed payment”, which respondents considered to be some of the key causes of increased stress among employees during the current COVID-19 pandemic.

The study is limited to exploring the relationship between compassionate managerial leadership and an organisation's ability to manage employee stress in the COVID-19 situation, using 30 samples from organisations operating in three Nigerian cities and sectors. Future studies may involve more Nigerian cities, sectors and samples. It may also possibly include quantitative combination to allow generalisation of findings.

In order to survive in extreme situations, such as the COVID-19 pandemic, organisations are forced to take drastic and often managerialist-driven work measures which can trigger high stress levels, low productivity and absenteeism among employees. Hence, organisations would benefit from implementing compassion-driven policies that are more inclusive and responsive to the workplace stressors facing employees.

Employee stress has been widely explored in many areas, including definitions, stressors, strains, possible interventions and coping strategies. There remains, however, a dearth of scholarship on how management-leadership compassion can help to reduce employee stress levels in extreme conditions, such as the COVID-19 pandemic — particularly in emerging economies.

An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

This study aims to examine the emotional and cognitive responses of frontline homelessness service support staff to the highly insecure attachment styles (AS) exhibited by people experiencing multiple exclusion homelessness (MEH), that is, a combination of homelessness and other forms of deep social exclusion.

Focus groups were conducted with frontline staff (N = 19) in four homelessness support services in Scotland. Hypothetical case vignettes depicting four insecure AS (enmeshed, fearful, withdrawn and angry-dismissive) were used to facilitate discussions. Data is analysed thematically.

Service users with AS characterised by high anxiety (enmeshed or fearful) often evoked feelings of compassion in staff. Their openness to accepting help led to more effective interactions between staff and service users. However, the high ambivalence and at times overdependence associated with these AS placed staff at risk of study-related stress and exhaustion. Avoidant service users (withdrawn or angry-dismissive) evoked feelings of frustration in staff. Their high need for self-reliance and defensive attitudes were experienced as hostile and dismissing. This often led to job dissatisfaction and acted as a barrier to staff engagement, leaving this group more likely to “fall through the net” of support.

Existing literature describes challenges that support staff encounter when attempting to engage with people experiencing MEH, but provides little insight into the causes or consequences of “difficult” interactions. This study suggests that an attachment-informed approach to care can promote more constructive engagement between staff and service users in the homelessness sector.

The quality of organizational leadership can have a significant impact on organizational success and employee well-being. Some research has shown that leaders with secure attachment styles are more effective leaders, but the connection between different attachment styles and different leadership styles is unclear. Relationships between attachment styles and leadership styles were examined in this study. University personnel completed the Relationship Questionnaire and the Multifactor Leadership Questionnaire. Pearson correlation and multiple regression analyses revealed positive correlations between transformational leadership and secure attachment and negative relationships between transformational leadership and insecure attachment styles. Results of this study may help leaders recognize the relationship between their attachment style and their ability to increase organizational effectiveness and to decrease turnover.

The article investigates different types of strategies for managing user-generated content (UGC) and provides some insights into their implications.

A unique sample of Italian hotels with current and prospective customers in the digital environment is investigated. A taxonomy of user-provider interactions mediated by UGC is developed. A mixed approach was designed to meet the study aims. Firstly, an exploratory factor analysis was performed in order to illuminate different strategies of UGC and electronic word-of-mouth (E-WOM) management. Secondly, a cluster analysis was implemented in order to explain hoteliers' behavior toward users' contents.

The study results suggested the existence of three clusters, which reflected three different types of interactions between hotels and customers in the digital domain. Interestingly, most of Italian hotels were found to adopt a reductionist approach to UGC and E-WOM management, turning out to be ineffective to exploit them for the purpose of quality improvement and hospitality service excellence.

Hotels were found to be largely unaware of the importance of UGC and web-based communication with customers to improve their digital business strategy. Tailored management approaches are needed to realize the full potential of hotels' online content responsiveness for the purpose of value co-creation and service co-production.

This is one of the first studies investigating the strategic and management perspectives embraced by hotels to handle their interactions with customers in the digital arena.

This article explores the value of engaging a hybrid learning strategy in a micro-enterprise setting when responding to a global pandemic. The research question asks: “Does a hybrid learning strategy enhance a micro-enterprise's response to extreme events?”.

A micro-enterprise owner–manager (OM) reflects on their experience running a business during the coronavirus disease 2019 (COVID-19) pandemic, which resulted in the complete decimation of the organization’s revenue stream in early 2020. Captured in conversation with an academic peer, these practitioner insights provide valuable case evidence relating to micro-enterprise response to extreme commercial events, such as a global pandemic.

The journey from initial survival-mode to emergent market opportunity recognition and subsequent growth is recorded. The paper contributes to the limited research on the impact of learning strategy plans on micro-enterprise crisis response strategies and provides insights into the value of engaging a hybrid learning strategy when responding to a significant external business shock.

Drawing from these insights, the authors offer a literature-informed framework from which to consider the dynamics of an adaptive strategic response in a micro-enterprise setting, offering a means through which micro-enterprises can plan for and respond to extreme events in the future.

The purpose of this paper is to examine the evidence of herding phenomenon, spill-over effects related to herding and whether herding is driven by fundamentals or non-fundamentals for various sub-periods and sub-samples.

The cross-sectional absolute deviation model is applied to China’s A- and B-share markets in combination with fundamental information.

Herding is prevalent on both A- and B-share markets. In detail, investors on A-share market herd for small and growth stock portfolios irrespective of market states while they only herd for large or value stocks in down market, therefore leading the whole herding behaviour to be pronounced in down market. Comparatively, on B-share market, herding is robust for various investment styles (small or large, value or growth) or market situations. Additionally, spill-over effects related to herding do not exist no matter from A-shares to B-shares or from B-shares to A-shares. Moreover, investors on B-share markets tend to herd as the response to non-fundamental information more frequently during financial crisis.

Investors on A- and B-share markets tend to herd as the response to non-fundamental information more frequently during financial crisis. Analysing the herding behaviours could be helpful in controlling the financial risk.

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.

The purpose of this paper is to explore the role of accountability in times of exception. The Italian government's account-giving practices are critically analysed with respect to the distinct modes in which duties of accountability are discharged for the exceptional measures taken during the early stages of the COVID-19 pandemic outbreak in early 2020.

This paper draws on an exploratory case study. The case analysis draws primarily on data obtained through publicly available documents and covers the period between January 1 and August 7, 2020.

The paper reveals that the Italian government employed various accountability styles (rebuttal, dismissal, reactive, proactive and coactive). Each style influenced both how the government justified its conduct and how it sought to form distinctive relationships with social actors.

The paper uses the notion of “styles of accountability” to empirically illustrate how an unprecedented public governance challenge can reveal broader accountability trends. The paper contributes to accountability research by elucidating how governments tackle ambiguity and uncertainty in their systems of public accountability in extraordinary times.