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Employee recognition systems are ubiquitous in organizations (WorldatWork, 2019) and have positive effects on work outcomes (e.g. Stajkovic and Luthans, 2001). However, psychologically meaningful recognition relies on the recognition giver being motivated to observe and recognize coworkers. Crises such as the COVID-19 pandemic may impact recognition giving in varying ways, yet little research considers this possibility.

This longitudinal field study examined the impact of the COVID-19 crisis on recognition and acknowledgment giving among frontline and nonfrontline healthcare workers at daily and aggregated levels. We tested the relationships between publicly available daily indicators of COVID-19 and objectively measured daily recognition and acknowledgment giving within a web-based platform.

We found that the amount of daily recognition giving was no different during the crisis compared to the year before, but fewer employees gave recognition, and significantly more recognition was given on days when COVID-19 indicators were relatively high. In contrast, the amount of acknowledgment giving was significantly lower in frontline staff and significantly higher in nonfrontline staff during the pandemic than before, but on a daily-level, acknowledgment was unrelated to COVID-19 indicators.

Our results suggest that organizational crises may at once inhibit and stimulate employee recognition and acknowledgment.

Our research is the first to empirically demonstrate that situational factors associated with a crisis can impact recognition giving behavior, and they do so in ways consistent with ostensibly contradictory theories.

The paper explores the notion of worker voice in terms of its implications for supply chain justice. The paper proposes the value of the recognition perspective on social justice for framing workers’ experiences in global supply chains and identifies opportunities for the advancement of the worker voice agenda with recognition justice in mind.

The paper adopts a conceptual approach to explore the notion of worker voice in supply chains in terms of the recognition perspective on social justice.

Sustainable supply chain management (SSCM) scholarship has considered worker voice in terms of two key paradigms, which we term communication and representation. To address recognition justice for workers in global supply chains, the worker voice agenda must consider designing worker voice mechanisms to close recognition gaps for workers with marginalised identities; the shared responsibilities of supply chain actors to listen alongside the expectation of workers to use their voice; and the expansion of the concept of worker voice to cut across home-work boundaries.

The paper offers conceptual clarity on the emerging notion of worker voice in SSCM and is the first to interrogate the implications of recognition justice for the emergent worker voice agenda. It articulates key opportunities for future research to further operationalise worker voice upon a recognition foundation.

This study aims to develop and validate a multidimensional scale to measure the motivating factors that lead to opportunity recognition in social entrepreneurship among higher education institute (HEI) students.

The scale was developed through two phases; in phase 1, semi-structured interviews with social entrepreneurs and aspiring students were conducted to explore themes for item generation. Phase 2 included developing and validating the scale using exploratory (EFA) and confirmatory factor analysis (CFA). The sample included HEI students (n = 300 for EFA, n = 300 for CFA) with either academic background or volunteering experiences in social entrepreneurship.

A 24-item scale is developed in the study, with six factors measuring the motivating factors influencing opportunity recognition in social entrepreneurship: life experiences, social awareness, social inclination, community development, institutional voids and natural option for a meaningful career.

The scale facilitates the development of theories and models in social entrepreneurship. The scale also enables policymakers and social entrepreneurship educators to understand the motivating factors that lead to opportunity recognition among students. It would help them to provide target-specific support to students.

To the best of the authors’ knowledge, this study is the first attempt to develop a scale that measures opportunity recognition in social entrepreneurship based on specific motivating factors. The study used the model by Yitshaki and Kropp (2016) as the conceptual framework. This study is the first attempt to triangulate the model’s findings using a quantitative methodology and through the development of a measurement scale. Besides, the scale adds value to social entrepreneurship research, which lacks empirical research on HEI students.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

The purpose of this paper was to determine the factors that enable entrepreneurial opportunity recognition and the significant role of education and training in enhancing opportunity recognition.

This paper follows a systematic literature review method to answer the research questions. A systematic literature review allows us to determine the work carried out to date, how it was done, assess literature and report all relevant research. The authors have used the Preferred Reporting Items for Systematic and Meta-Analysis procedure.

The findings of this study showed that prior knowledge, social networks, external environment, entrepreneurial alertness, creativity, self-efficacy and entrepreneurial passion are the main factors that play a role in the opportunity recognition process. The authors were also able to establish the importance of education and training in enhancing opportunity recognition. Experiential learning is at the forefront of education methods used to improve prior knowledge and experience that directly impact the ability to recognize entrepreneurial opportunities.

The paper provides human resource development practitioners and entrepreneurship educators with factors that determine entrepreneurial opportunity recognition. It pinpoints the factors that can be exploited in enhancing employees and novice entrepreneurs’ ability to recognize viable entrepreneurial opportunities.

Opportunity recognition is recognized as the first step in the entrepreneurship process. Therefore, it is crucial for entrepreneurs to have the ability to recognize opportunities that are viable. Understanding the factors that contribute to a successful opportunity recognition is important. In addition, the role of education and training in opportunity recognition and enhancing entrepreneurial opportunity recognition cannot be overlooked.

This study aims to analyze the effect of entrepreneurs’ improvisational behavior on business risk-taking and opportunity recognition, as well as to analyze its subsequent impact on firm performance. Moreover, this study examined whether the effect of entrepreneurs’ improvisational behavior on business risk-taking and opportunity recognition could be moderated by firm size and the business experience of entrepreneurs.

Online survey data were collected from 304 firms in Thailand that were randomly selected from a business directory. The data were assessed using partial least squares structural modeling.

The results confirmed that entrepreneurs who exhibited high levels of improvisational behavior tended to report that their firms engaged more actively in risk-taking and opportunity recognition. Moreover, risk-taking and opportunity recognition played a chain mediating effect in explaining the association between the improvisational behavior of entrepreneurs and firm performance. Regarding the moderating effects, this paper found that firm size negatively moderated the effect of improvisational behavior on risk-taking and opportunity recognition, while business experience of entrepreneurs only positively moderated the effect of improvisational behavior on risk-taking.

This study provided new knowledge by showing that improvisational behavior of entrepreneurs should be integrated with other firm advantages determined by firm size and the business experience of entrepreneurs to strengthen the ability to be more effective at risk-taking and opportunity recognition.

The research on structure function recognition mainly concentrates on identifying a specific part of academic literature and its applicability in the multidiscipline perspective. A specific part of academic literature, such as sentences, paragraphs and chapter contents are also called a level of academic literature in this paper. There are a few comparative research works on the relationship between models, disciplines and levels in the process of structure function recognition. In view of this, comparative research on structure function recognition based on deep learning has been conducted in this paper.

An experimental corpus, including the academic literature of traditional Chinese medicine, library and information science, computer science, environmental science and phytology, was constructed. Meanwhile, deep learning models such as convolutional neural networks (CNN), long and short-term memory (LSTM) and bidirectional encoder representation from transformers (BERT) were used. The comparative experiments of structure function recognition were conducted with the help of the deep learning models from the multilevel perspective.

The experimental results showed that (1) the BERT model performed best, with F1 values of 78.02, 89.41 and 94.88%, respectively at the level of sentence, paragraph and chapter content. (2) The deep learning models performed better on the academic literature of traditional Chinese medicine than on other disciplines in most cases, e.g. F1 values of CNN, LSTM and BERT, respectively arrived at 71.14, 69.96 and 78.02% at the level of sentence. (3) The deep learning models performed better at the level of chapter content than other levels, the maximum F1 values of CNN, LSTM and BERT at 91.92, 74.90 and 94.88%, respectively. Furthermore, the confusion matrix of recognition results on the academic literature was introduced to find out the reason for misrecognition.

This paper may inspire other research on structure function recognition, and provide a valuable reference for the analysis of influencing factors.

The impact of various factors on how TV sports audiences perceive sport event sponsors’ billboards around sports facilities has been the subject of extensive research. Despite numerous factors that impact the effectiveness of sponsor signage at sporting events, there has been a lack of research regarding the language used for such signage around sports facilities’ billboards. Therefore, this study aims to investigate the effects of billboard advertisement language on TV sports audiences’ recognition, recall and search intention to sponsor signage.

This study employed an online experimental design. Participants (n = 925) were recruited from two linguistically different regions: Chinese and English. Participants were randomly assigned to one of two conditions: watching tennis video matches with billboard advertisements presented in either the Roman alphabet exclusively or in a combination of the Roman alphabet and Chinese characters.

This study revealed that although language cannot significantly impact audiences’ unaided recall of a brand, it does have a discernible effect on brand recognition and search intention among audiences. Additionally, people are more likely to search for brands in their native language. Participants from various regions tend to have different recognition rates and search intentions for sport sponsors.

This is the first manuscript examining the use of different languages in relation to audiences’ recognition and recall of sports sponsorship. It provides empirical evidence of the importance of carefully considering the language used in sponsor signage around stadium billboards to optimize the efficacy of sponsorships at sports events.

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

Artificial intelligence (AI) applications pose a potential threat to users' data security and privacy due to their high data-dependence nature. This paper aims to investigate an understudied issue in the literature, namely, how users perceive the threat of and decide to use a threatening AI application. In particular, it examines the influencing factors and the mechanisms that affect an individual’s behavioral intention to use facial recognition, a threatening AI.

The authors develop a research model with trust as the key mediating variable by integrating technology threat avoidance theory, the theory of planned behavior and contextual factors related to facial recognition. Then, it is tested through a sequential mixed-methods investigation, including a qualitative study (for model development) of online comments from various platforms and a quantitative study (for model validation) using field survey data.

Perceived threat (triggered by perceived susceptibility and severity) and perceived avoidability (promoted by perceived effectiveness, perceived cost and self-efficacy) have negative and positive relationships, respectively, with an individual’s attitude toward facial recognition applications; these relationships are partially mediated by trust. In addition, perceived avoidability is positively related to perceived behavioral control, which along with attitude and subjective norm is positively related to individuals' intentions to use facial recognition applications.

This paper is among the first to examine the factors that affect the acceptance of threatening AI applications and how. The research findings extend the current literature by providing rich and novel insights into the important roles of perceived threat, perceived avoidability, and trust in affecting an individual’s attitude and intention regarding using threatening AI applications.