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Airline self-service technology (SST) has attracted attention from both the academic and aviation sectors. As the use of SST can reduce airlines’ operating costs, investigating SST usage at airports is particularly important for the aviation sector. The extant literature has explored users’ SST usage intention, but users’ switching intentions from traditional manual counter services to SST is still limited. Therefore, to address this issue, we used the push–pull–mooring (PPM) theoretical framework to develop a research model to explore user switching intention.

We utilized a mixed-methods approach. A qualitative approach (i.e., semistructured interviews) was first employed to recognize and choose the candidate factors. Then, we collected 450 valid responses through an online survey to test the model. The partial least squares method was used for data analysis.

We found that several push (perceived dissatisfaction and perceived inconvenience), pull (perceived ease of use, perceived usefulness and service process fit), and mooring (personal innovativeness and inertia) factors significantly influence switching intention. Additionally, mooring factors exert contextual effects on the relationships between push and switching intentions and between pull factors and switching intentions.

This study contributes to the literature by further increasing our understanding of user switching intentions regarding SSTs from the PPM perspective and offering guidance for the aviation sector to attract and retain customers.

The growth of self-service technologies (SST) in the retail sector has led to an increased prevalence of SST failures, and spurred academic debate on customer self-recovery of the failed services. This study sets out to explore why customers prefer or decline to engage in self-recovery. A framework integrating elements from self-determination theory and theory of planned behavior is developed to explore the impact of motivational factors, attitudes and self-efficacy on self-recovery intention.

Structural equation modeling (SEM) was used to analyze the sample consisting of 297 users of retail kiosks in convenience stores.

The results revealed that intrinsic motivation and identified regulation directly affect customers' attitude and intention to engage in self-recovery. Despite an insignificant direct relationship, external regulation impacted self-recovery intention through attitude. Further, the association between intrinsic motivation and self-recovery intention is moderated by self-efficacy.

Much of the extant SST recovery literature has focused on company-rendered service recovery, providing little guidance to firms on how to promote self-recovery among customers. The integrated motivational-cognitive theoretical base in this study allows for a more differentiated inquiry into the factors shaping self-recovery intention, resulting in a deeper understanding of this topic. The novel insights will help retailers develop effective strategies for promoting self-recovery among users of retail kiosks.

This study examines the effects of financial literacy and financial risk tolerance on investor behavior by introducing social stigma as a mediator and emotional intelligence as a moderating factor.

Data is collected from 761 financially independent individual investors, with a minimum age of 25 years, a minimum of five years of stock market experience and residing in five selected major Indian cities. The collected data is subsequently analyzed using SmartPLS. Homogeneous purposive sampling followed by snowball sampling was employed.

The findings of the study demonstrate a strong and noteworthy impact of financial literacy on investor behavior. The research reveals that social stigma acts as a partial mediator and emotional intelligence plays a significant moderator with direct effects and indirect effects between financial literacy, financial risk tolerance, social stigma and investor behavior.

Exploring emotional intelligence in financial decisions enriches academic programs by integrating it into financial education. Collaboration between academia and financial institutions yields practical tools, infusing emotional intelligence into services. This prompts systemic shifts, reshaping education and societal discourse, fostering inclusive, emotionally intelligent financial landscapes, aiming to redefine both academic teachings and real-world financial practices.

Integrating emotional intelligence into government-led financial literacy programs can transform societal perspectives on financial decision-making. Customized services, destigmatizing workshops and collaborative efforts with academia foster an emotionally intelligent financial landscape, reshaping traditional paradigms.

Promoting open societal discussions about finances combats stigma, fostering a supportive space for risk-taking. Emphasizing emotional intelligence in awareness campaigns cultivates inclusivity and confidence. Normalizing financial talks empowers individuals, enhancing their well-being. Elevating both financial literacy and emotional intelligence enhances overall financial health, nurturing a community adept at navigating financial journeys.

This study marks a notable contribution to behavioral finance and social stigma theory by examining their intersection with emotional intelligence. It uniquely introduces social stigma as a mediator and emotional intelligence as a moderator, unexplored in this context. This novelty underscores the research’s significance, offering practical insights into financial well-being.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2023-0626

The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis.

The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model.

On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets.

The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.

This study aims to explore how managers exercise agency in strategic investment decisions (SIDs) by drawing on their knowledgeability of the strategic context. Specifically, the authors address the role of position–practice relations and irresistible causal forces in this conduct.

The authors examine SID-making (SIDM) practices in four case organisations operating in highly competitive markets, conducting interviews with managers at various levels and analysing company documents. Drawing on strong structuration theory, the authors show how managerial decision makers draw upon their knowledge of organisational context when exercising agency in SIDs.

The authors provide insights into how SIDM behaviour, specifically agents’ conduct, is shaped by a combination of position–practice relations and the agents’ comprehension of their organisation’s context.

The authors extend the SIDM literature by surfacing the issue of how actors’ conjuncturally-specific knowledge of external structures shapes the general dispositions they draw on in exercising agency in practice.

The authors extend the SIDM literature by surfacing the issue of how actors’ conjuncturally-specific knowledge of external structures shapes the general dispositions they draw on in exercising agency in practice. Particularly, the authors contribute to this literature by identifying irresistible causal forces and illuminating why actors might not resist in SIDM processes, despite having the potential to do so.

Existing studies have demonstrated the significances of job, personal and social resources in stimulating work engagement (WKE). However, the role of psychological empowerment (PYE) in linking these resources to WKE has received limited attention. Hence, drawing on the conservation of resources (COR) theory, this paper investigates the effects of friends and family support (FFS), supervisor support (SST), and employee resilience (ERS) in stimulating PYE for WKE amid the uncertainty associated with the COVID-19 pandemic.

With a survey instrument, the authors collected data from 259 knowledge workers in Malaysia and analyzed the hypothesized model with the partial least square (PLS) structural equation modeling technique.

The data support the significant effects of FFS, SST, and ERS on PYE. Also, PYE was a significant mediator of FFS, SST, and ERS effects on WKE.

This study offers empirical support for the relevance of COR theory in explaining how employees adjust to the burden of uncertainty and psychological stress associated with the COVID-19 mandated “work from home” (WFH) practices through their engagement in work.

This study aims to explore what and how digital innovation, as a knowledge-based and multi-dimensional process, can be used to increase the accountability of public and private sector organizations during the COVID-19 pandemic.

Taking an interpretivist approach, qualitative research is designed around Strong Structuration Theory (SST). A content analysis of relevant documents and semi-structured interviews focusing on the relationships between digital innovation and accountability in extraordinary times is conducted.

The results show the existence of digital innovation barriers and facilitators that can have an impact on accountability during extraordinary times. The research highlights how managers of public organizations focus largely on the social dimension of knowledge (i.e., competencies shaped by collective culture), while managers of private organizations focus mainly on the human dimension of knowledge (i.e., skills gained through learning by doing).

The paper enriches the accountability literature by historicizing SST for extraordinary times and by utilizing a multiple-dimensional approach to digital innovation. Also, the work underlines specific strategies organizations could usefully adopt to improve accountability through digital innovation in the public and private sectors during extraordinary times.

This article emphasizes the crucial integration of technological components with knowledge. In particular, the digital innovation is considered as a strong synergy of human and social dimensions that compels organizations toward enhanced accountability, particularly in the face of extraordinary challenges.

This paper aims to investigate the problem of estimating the angle of attack (AoA) and relative velocity for vertical axis wind turbine (VAWT) blades from computational fluid dynamics data.

Two methods are implemented as function objects within the OpenFOAM framework for estimating the blade’s AoA and relative velocity. For the numerical analysis of the flow around and through the VAWT, 2 D unsteady Reynolds-averaged Navier–Stokes (URANS) simulations are carried out and validated against experimental data.

To gain a better understanding of the complex flow features encountered by VAWT blades, the determination of the AoA is crucial. Relying on the geometrically-derived AoA may lead to wrong conclusions about blade aerodynamics.

This study can lead to the development of more robust optimization techniques for enhancing the variable-pitch control mechanism of VAWT blades and improving low-order models based on the blade element momentum theory.

Assessment of the reliability of AoA and relative velocity estimation methods for VAWT’ blades at low-Reynolds numbers using URANS turbulence models in the context of dynamic stall and blade–vortex interactions.

This paper aims to reveal the role of two theories that impact seniors’ entrepreneurial intentions. Both the socioemotional selectivity theory (SST) and the self-determination theory (SDT) re-shape seniors’ intentions to create entrepreneurial opportunities and activities after they retire.

This paper uses secondary data resources for developing the research concept, which might leverage seniors’ ultimate goal of creating entrepreneurial activities. A comprehensive past-paper analysis was performed. One hundred papers were initially considered for inclusion in this research. However, after a rigorous synthesisation process, 80 publications were selected for further analysis.

This paper presents an investigation of seniors’ entrepreneurship, with a specific emphasis on the SST and the SDT. It suggests potential models that could gauge senior entrepreneurs’ propensity to engage in entrepreneurial endeavours to support the socioeconomic advancement of society. Furthermore, this research discussed the limitations of the enlightening concepts presented to scholars and decision-makers.

The originality of this paper is illuminated by its idea of integrating two theories (the SST and the SDT), suggesting that these theories can possibly better observe senior entrepreneurs’ intentions in creating an entrepreneurial venture after they retire.

The determination of human behaviours due to individual characteristics (personality traits) or situational factors has long remained inconclusive. Although the literature stream on personality as the determinants of behaviour is voluminous, the interest of researchers is also growing towards organisational situational cues as the determinant of behaviours. According to situation strength theory (SST), behaviours are determined by situations in strong situations and by personality in weak situations. This study aims to propose a theoretical model of intrapreneurial behaviour (IB) emanation from empowering leadership (EL) by extending the epistemology of SST under the influence of organisational strong situations of job autonomy (JA) and perceived organisational support (POS).

Using SST, the present study argues that strong situations play a key role in determining human behaviours, and the same can be viewed deductively to assess IB.

The study attempts to propose whether EL is capable of predicting IB under the strong situation effect of JA and POS while dampening the impact of human personality characteristics.

The current study offers a significant departure from current human resource practices in person-situation dialectics, moving away from personality assessments and toward the creation of cues from strong situations for fostering human behaviour. As a result, personality researchers are being encouraged to conduct a reality check on the extensive personality research conducted in occupational settings. Considering organisational situational cues can impact human resource scope in areas such as talent management, selection, promotion and employment.