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1 – 10 of 24Hei-Chia Wang, Martinus Maslim and Hung-Yu Liu
A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as…
Abstract
Purpose
A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.
Design/methodology/approach
This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.
Findings
This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.
Originality/value
The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.
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Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined…
Abstract
Purpose
Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.
Design/methodology/approach
We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.
Findings
Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.
Originality/value
Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.
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Mohamed A. Shahat, Sulaiman M. Al-Balushi and Mohammed Al-Amri
The purpose of the current study is to assess Omani teachers’ performance on tasks related to the stages of engineering design. To achieve this, data from an engineering design…
Abstract
Purpose
The purpose of the current study is to assess Omani teachers’ performance on tasks related to the stages of engineering design. To achieve this, data from an engineering design test was used, and demographic variables that are correlated with this performance were identified.
Design/methodology/approach
This descriptive study employed a cross-sectional design and the collection of quantitative data. A sample of preservice science teachers from Sultan Qaboos University (SQU) (n = 70) participated in this study.
Findings
Findings showed low and moderate levels of proficiency related to the stages of engineering design. Differences between males and females in terms of performance on engineering design tasks were found, with females scoring higher overall on the assessment. Biology preservice teachers scored higher than teachers from the other two majors (physics and chemistry) in two subscales. There were also differences between teachers studying in the Bachelor of Science (BSc) program and the teacher qualification diploma (TQD) program.
Originality/value
This study provides an overview, in an Arab setting, of preservice science teachers’ proficiency with engineering design process (EDP) tasks. It is hoped that the results may lead to improved instruction in science teacher training programs in similar contexts. Additionally, this research demonstrates how EDP competency relates to preservice teacher gender, major and preparation program. Findings from this study will contribute to the growing body of research investigating the strengths and shortcomings of teacher education programs in relation to science, technology, engineering and mathematics (STEM) education.
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Atul Prashar and Moutusy Maity
This study aims to quantitatively consolidate the research conducted over the past four decades on how internal branding activities drive employee commitment. It summarizes…
Abstract
Purpose
This study aims to quantitatively consolidate the research conducted over the past four decades on how internal branding activities drive employee commitment. It summarizes several operationalizations of internal branding and tests the moderating effect of employee’s personal characteristics and job characteristics on the relationship between internal branding and employee commitment.
Design/methodology/approach
This paper uses meta-analysis as the research methodology. The analysis includes a sample of 65 studies (from 62 published works), yielding 226 effect sizes (coded into 82 composite effect sizes) over an aggregated sample of 21,706 respondents.
Findings
This study finds that brand communication, brand-centered human resource management (HRM), training and development, organizational support and culture, brand-centered leadership and an excellent reward system are the key operationalizations of internal branding. Furthermore, employee’s personal (education, age and gender) and job (tenure, work status and level of customer orientation) characteristics significantly moderate the internal branding–employee commitment relationship.
Research limitations/implications
Limited empirical literature on some of the internal branding operationalizations such as brand-centered HRM and rewards has curbed the scope of moderator analysis.
Practical implications
This paper proposes some effective ways of implementing internal branding strategies and provides support for boundary conditions that brand managers should consider to strengthen the impact of internal branding activities on employee commitment.
Originality/value
As per the authors’ knowledge, this paper is among the few quantitative consolidations of four decades of research on the internal branding–employee commitment relationship.
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Carmen Valor, Carlos Martínez-de-Ibarreta, Isabel Carrero and Amparo Merino
Brief loving-kindness meditation (LKM) is introduced here as a valid social marketing intervention. LKM positively influences prosocial cognitions and affects. However, it remains…
Abstract
Purpose
Brief loving-kindness meditation (LKM) is introduced here as a valid social marketing intervention. LKM positively influences prosocial cognitions and affects. However, it remains unclear whether brief meditation interventions can influence prosocial behavior. This study aims to provide evidence of the effects of short LKM on prosocial behavior.
Design/methodology/approach
This study reports the results of three experiments examining the effects of brief LKM on donations to unknown others. The results are then integrated with the results of seven other studies testing the effects of brief LKM on prosocial behavior using a meta-analysis (n = 683).
Findings
LKM increased love more than the control group (focused breathing) in the three experiments; however, its effects on donations were mixed. The meta-analysis shows that LKM has a small-to-medium significant effect compared to active control groups (d = 0.303); moreover, age and type of prosocial measure used moderate the effects.
Originality/value
Results suggest that LKM can nurture prosocial emotions such as love and lead young individuals to donate. However, these emotions may not be sufficient to lead adult meditators to share their resources with unknown others. This study presents the first meta-analysis of brief LKM and provides insights into the use of meditation in social marketing programs.
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Arash Arianpoor and Ahmad Abdollahi
The purpose of this study is to propose a framework for the convergence of maturity model and education and evaluation in accounting.
Abstract
Purpose
The purpose of this study is to propose a framework for the convergence of maturity model and education and evaluation in accounting.
Design/methodology/approach
The present research was conducted in two phases. In the first phase, to determine the indicators of convergence of the maturity model and education and evaluation in accounting, a Meta-Synthesis method was used. The conceptual model includes two dimensions of “Teaching and learning processes” and “Evaluation methods"; five levels of initial, repeatable, defined, managed and optimized; and a total number of 35 indicators. In the second phase, a questionnaire was developed, and academics as accounting faculty members in Iranian public universities were employed to fill out the questionnaire electronically and present a final framework. Having received the questionnaires, 66 questionnaires were analyzed statistically.
Findings
The results showed that the two dimensions of “Teaching and learning processes” and “Evaluation methods” considering initial, repeatable, defined, managed and optimized levels include 35 indicators, which form a framework for the convergence of maturity model and education and evaluation in accounting. The results show that both dimensions have positive and significant regression path coefficients in the convergence model. Moreover, the dimension of teaching and learning processes has the highest regression path coefficient indicating a greater impact on the convergence model. Besides, all five levels have positive and significant regression path coefficients with dimensions. Finally, in this study, all indicators were prioritized according to five levels.
Originality/value
Due to the success of maturity models and the urgent developments that require transformative improvements in accounting education, maturity models can respond to the challenges associated with education and learning in accounting. Thus, conceiving an image of the convergence of maturity model, education and evaluation in accounting seems imperative which has been scarcely investigated previously.
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Peter Bannister, Elena Alcalde Peñalver and Alexandra Santamaría Urbieta
This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI…
Abstract
Purpose
This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and longstanding calls to define and disseminate quality implementation good practice.
Design/methodology/approach
A virtual nominal group technique engaged experts (n = 14) in idea generation, refinement and consensus building across asynchronous and synchronous stages. The resulting qualitative and quantitative data were analysed using thematic analysis and descriptive statistics, respectively.
Findings
The GenAI Academic Integrity Policy Development Blueprint for EMI Tertiary Education is not a definitive mandate but represents a roadmap of inquiry for reflective deliberation as institutions chart their own courses in this complex terrain.
Research limitations/implications
If repeated with varying expert panellists, findings may vary to a certain extent; thus, further research with a wider range of stakeholders may be necessary for additional validation.
Practical implications
While grounded within the theoretical underpinnings of the field, the tool holds practical utility for stakeholders to develop bespoke policies and critically re-examine existing frameworks.
Social implications
As texts produced by students using English as an additional language are at risk of being wrongly accused of GenAI-assisted plagiarism, owing to the limited efficacy of text classifiers such as Turnitin, the policy recommendations encapsulated in the blueprint aim to reduce potential bias and unfair treatment of students.
Originality/value
The novel blueprint represents a step towards bridging concerning gaps in policy responses worldwide and aims to spark discussion and further much-needed scholarly exploration to this end.
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Kalpana Chandrasekar and Varisha Rehman
Global brands have become increasingly vulnerable to external disruptions that have negative spillover effects on consumers, business and brands. This research area has recently…
Abstract
Purpose
Global brands have become increasingly vulnerable to external disruptions that have negative spillover effects on consumers, business and brands. This research area has recently garnered interest post-pandemic yet remains fragmented. The purpose of this paper is to recognize the most impactful exogenous brand crisis (EBC) and its affective and behavioural impact on consumers.
Design/methodology/approach
In Study 1, we applied repertory grid technique (RGT), photo elicitation method and ANOVA comparisons, to identify the most significant EBC, in terms of repercussions on consumer purchases. In Study 2, we performed collage construction and content analysis to ascertain the impact of the identified significant crisis (from Study 1) on consumer behaviour in terms of affective and behavioural changes.
Findings
Study 1 results reveal Spread-of-diseases and Natural disaster to be the most impactful EBC based on consumer’s purchase decisions. Study 2 findings uncover three distinct themes, namely, deviant demand, emotional upheaval and community bonding that throws light on the affective and behavioural changes in consumer behaviour during the two significant EBC events.
Research limitations/implications
The collated results of the two studies draw insights towards understanding the largely unexplored conceptualisation of EBC from a multi-level (micro-meso-macro) perspective. The integrated framework drawn, highlight the roles and influences of different players in exogenous brand crisis management and suggests future research agendas based on theoretical underpinnings.
Originality/value
To the best of our knowledge, this is the first study which identifies the most important EBC and explicates its profound impact on consumer purchase behaviour, providing critical insights to brand managers and practitioners to take an inclusive approach towards exogenous crises.
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Caitlin Brandenburg, Paulina Stehlik, Christy Noble, Rachel Wenke, Kristen Jones, Laetitia Hattingh, Kelly Dungey, Grace Branjerdporn, Ciara Spillane, Sharmin Kalantari, Shane George, Gerben Keijzers and Sharon Mickan
Clinician engagement in research has positive impacts for healthcare, but is often difficult for healthcare organisations to support in light of limited resources. This scoping…
Abstract
Purpose
Clinician engagement in research has positive impacts for healthcare, but is often difficult for healthcare organisations to support in light of limited resources. This scoping review aimed to describe the literature on health service-administered strategies for increasing research engagement by medical practitioners.
Design/methodology/approach
Medline, EMBASE and Web of Science databases were searched from 2000 to 2021 and two independent reviewers screened each record for inclusion. Inclusion criteria were that studies sampled medically qualified clinicians; reported empirical data; investigated effectiveness of an intervention in improving research engagement and addressed interventions implemented by an individual health service/hospital.
Findings
Of the 11,084 unique records, 257 studies were included. Most (78.2%) studies were conducted in the USA, and were targeted at residents (63.0%). Outcomes were measured in a variety of ways, most commonly publication-related outcomes (77.4%), though many studies used more than one outcome measure (70.4%). Pre-post (38.8%) and post-only (28.7%) study designs were the most common, while those using a contemporaneous control group were uncommon (11.5%). The most commonly reported interventions included Resident Research Programs (RRPs), protected time, mentorship and education programs. Many articles did not report key information needed for data extraction (e.g. sample size).
Originality/value
This scoping review demonstrated that, despite a large volume of research, issues like poor reporting, infrequent use of robust study designs and heterogeneous outcome measures limited application. The most compelling available evidence pointed to RRPs, protected time and mentorship as effective interventions. Further high-quality evidence is needed to guide healthcare organisations on increasing medical research engagement.
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Lauranna Teunissen, Kathleen Van Royen, Iris Goemans, Joke Verhaegen, Sara Pabian, Charlotte De Backer, Heidi Vandebosch and Christophe Matthys
Explore what popular food influencers among Flemish emerging adults portray in their Instagram recipe posts in terms of (1) references to food literacy, (2) nutritional value, (3…
Abstract
Purpose
Explore what popular food influencers among Flemish emerging adults portray in their Instagram recipe posts in terms of (1) references to food literacy, (2) nutritional value, (3) rational and emotional appeals and (4) the relation between the nutritional value and rational/emotional appeals.
Design/methodology/approach
A content and nutritional analysis of Instagram recipe posts from seven food influencers (N = 166).
Findings
Findings reveal that food influencers rarely embed references to food literacy in their recipe posts, especially regarding meal planning, food selection, meal consumption and evaluating food-related information. Only in 28.9% of the posts information was given on how to prepare a recipe. Second, 220 recipes were included in the 166 recipe posts, of which the majority (65%) were main course meals that met at least six of the 11 nutrient criteria for a healthy main meal (67.2%). Finally, food influencers promote their recipe posts as positive narratives, focusing on the tastiness (66%) and convenience (40.9%) of meals.
Originality/value
This is the first study to evaluate what food influencers post nutritionally in their Instagram recipes, as well as how they promote these recipes. Health promotors should note the influential role of food influencers and seek ways to collaborate to provide information on how food literacy cues can be embedded in influencers' communications and provide insights into how influencers' recipes can be optimised.
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