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Article
Publication date: 26 December 2023

Xueting Gong, Dinkneh Gebre Borojo and Jiang Yushi

Due to their limited capacity for adaptation and dependence on natural resources for economic growth, developing countries (DCs) tend to be more prone to climate change. It is…

188

Abstract

Purpose

Due to their limited capacity for adaptation and dependence on natural resources for economic growth, developing countries (DCs) tend to be more prone to climate change. It is argued that climate finance (CF) is a significant financial innovation to mitigate the negative effects of climate variation. However, the heterogeneous impacts of CF on environmental sustainability (ES) and social welfare (SW) have been masked. Thus, this study aims to investigate the heterogeneous effects of CF on ES and SW in 80 CF receipt DCs from 2002 to 2018. This study also aims to investigate the effects of CF on ES and SW based on population size, income heterogeneity and the type of CF.

Design/methodology/approach

The method of moments quantile regression (MMQR) with fixed effects is utilized. Alternatively, the fully modified least square (FMOLS) and dynamic least square (DOLS) estimators are used for the robustness test.

Findings

The findings revealed that DCs with the lowest and middle quantiles of EF, carbon dioxide (CO2) emissions and human development exhibit large beneficial impacts of CF on ES and SW. In contrast, the positive effects of CF on ES breakdown for countries with the largest distributions of EF and CO2 emissions. Besides, the impacts of CF on ES and SW depend on income heterogeneity, population size and the type of CF.

Practical implications

This study calls for a framework to integrate CF into all economic development decisions to strengthen climate-resilient SW and ES in DCs.

Originality/value

To the best of the authors’ knowledge, this is the first study to investigate the effects of CF on ES and SW in a wide range of DCs. Thus, it complements existing related literature focusing on the effects of CF on ES and SW.

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Article
Publication date: 25 November 2024

Wei Jun Wang, Rafiu King Raji, Jian Lin Han and Yuan Chen

With the current developments within the sphere of Internet of Things (IoT) technology, many conventional articles are all being fitted with smart functionalities, ranging from…

63

Abstract

Purpose

With the current developments within the sphere of Internet of Things (IoT) technology, many conventional articles are all being fitted with smart functionalities, ranging from chairs, beds, shoes and caps to underwear. Bags which are utility as well as fashion items have not been left out of this smart craze, albeit to a less popular degree. The purpose of this study is to fill the research gap on the subject of smart bags research and applications and to contribute to the general discourse on IoT.

Design/methodology/approach

This study adopts literature search and database review, concept mapping as well as synthesis methodologies. Relevant literature form databases such as Web of Science, Google Scholar and Bing Scholar were interrogated. Manual sifting was done to eliminate papers that do not fit the set inclusion criteria. Literature on smart bags was organized into structured frameworks using concept mapping methodology. Applying a synthesis methodology enabled an exploration of the different technological trends in smart bag research and their areas of application.

Findings

The study identified about 15 different smart bag applications and functionalities. Discussed in this study is a classification of bags based on a number of points such as way of carrying, size, utility and fabrication materials. Also discussed are the description of what constitute a smart bag, relevant technologies for smart bag design and engineering and subsequently the current trends in smart bag applications. This study also discovered that the air travel industry tend to have some difficulties with this smart bag technologies, specifically with their built-in batteries.

Practical implications

The results of this study will provide researchers and other stakeholders with key information about existing problems and opportunities in smart bag research and applications. This will go a long way to help in guiding future research as well as policymaking in smart bag design and application.

Originality/value

To the best of the authors’ knowledge, this is the first review on the subject of smart bags even though smart bag research and commercial product design continue to gain momentum in recent years.

Details

Sensor Review, vol. 45 no. 2
Type: Research Article
ISSN: 0260-2288

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Article
Publication date: 9 January 2025

Shu-Hsien Liao, Da-Chian Hu and Cai-Jun Chen

This study proposed an extended theory of planned behaviour (TPB), that is, considering that behavioural beliefs, normative beliefs and control beliefs (beliefs) will influence…

48

Abstract

Purpose

This study proposed an extended theory of planned behaviour (TPB), that is, considering that behavioural beliefs, normative beliefs and control beliefs (beliefs) will influence perceived service quality (PSQ) on food delivery services. PSQ (behavioural intention) will influence electronic word-of-mouth (EWOM) (behaviour). In addition, exogenous variables including information from online ratings and consumer groups will affect the strength of the relationship between received service quality and EWOM on food delivery service.

Design/methodology/approach

This study aimed to investigate the mediation (PSQ) and moderation (Online ratings and consumer groups) effects on the extended TPB for Taiwanese consumers (n = 823).

Findings

This study first found a positive relationship between different beliefs and PSQ (behavioural intention). In addition, there is a positive relationship between PSQ and EWOM. Online rating has a moderating effect between PSQ and EWOM. Consumer group has a moderating relationship between PSQ and EWOM.

Originality/value

This study first found that the three stages of beliefs-intention-behaviour for consumers on food delivery service are reciprocal with two paths, starting with offline-to-online in terms of generating the positive relationship between individual belies and PSQ. Next, it can generate positive power to return online with a behaviour of EWOM. In addition, online ratings can enhance and strengthen the positive effect between PSQ and EWOM.

Details

British Food Journal, vol. 127 no. 3
Type: Research Article
ISSN: 0007-070X

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Article
Publication date: 28 November 2024

Dan Luo, Xiewen Ni, Eugene Cheng-Xi Aw and Garry Wei-Han Tan

This study aims to propose and validate a research framework pertaining to the willingness to disclose information in the context of mobile banking apps. The interrelationships…

82

Abstract

Purpose

This study aims to propose and validate a research framework pertaining to the willingness to disclose information in the context of mobile banking apps. The interrelationships between privacy protection, perceived personalization, social presence, design aesthetics, consumer empowerment, parasocial interactions and privacy concerns are assessed as antecedents of willingness to disclose information.

Design/methodology/approach

Based on a self-administered survey, this study gathered data from 450 Chinese consumers. The data were analyzed using structural equation modeling via SmartPLS 4 software.

Findings

The findings indicate that (1) privacy concerns are negatively related to willingness to disclose information, (2) parasocial interaction reduces privacy concerns, (3) consumer empowerment is positively influenced by privacy protection and perceived personalization and (4) social presence and design aesthetics positively contribute to the formation of parasocial interaction.

Originality/value

The current study serves to reinforce a theoretical understanding of the willingness to disclose information in mobile banking apps, which is underresearched. The findings offer alternative psychological mechanisms (i.e. consumer empowerment and parasocial interaction) and relevant mobile banking app attributes to explain the willingness to disclose information.

Details

International Journal of Bank Marketing, vol. 43 no. 3
Type: Research Article
ISSN: 0265-2323

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Article
Publication date: 24 February 2025

Xiuli Zhang, Wenkai Gao, Jian Cui, Yuankang Shen, Tao Huang, Gengyuan Gao and Jun Cao

Rubber-plastic double-layer bush water-lubricated bearings have demonstrated superior performance, while research on their vibration characteristics remains limited. This paper…

4

Abstract

Purpose

Rubber-plastic double-layer bush water-lubricated bearings have demonstrated superior performance, while research on their vibration characteristics remains limited. This paper aims to investigate the lubrication and vibration properties of these bearings by experiments and examine the effect of rubber-to-plastic bush thickness ratio on bearing performance.

Design/methodology/approach

A water-lubricated journal bearing test rig is constructed, and three bearings with different bush thickness ratios are fabricated. Bush deformation under various loads is measured, and the friction coefficient and axis trajectory under different operating conditions are tested. The vibration responses of the bearings under directional harmonic excitation are studied. The influences of rotational speed, load and rubber-to-plastic bush thickness ratio on the bearing’s lubrication and vibration properties are analyzed.

Findings

The friction coefficient of the bearing initially decreases rapidly and subsequently increases gradually as the rotational speed or load increases. The bearing with a thicker rubber bush shows lower displacement amplitudes in its axis trajectory. Under a 45° directed excitation, significant oscillations are observed in the vertical displacement, while the horizontal displacement remains stable. The damping effect of the bearing with a thicker rubber bush is more pronounced.

Originality/value

This paper present the influence of rubber-to-plastic bush thickness ratio on bearing lubrication and vibration performance. The results are valuable for the design of this type of bearing.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0469/

Details

Industrial Lubrication and Tribology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0036-8792

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Article
Publication date: 24 February 2025

Chaofeng Shen, Jun Zhang and Yueyang Song

Accurately predicting the installed capacity of wind energy is essential for energy strategic planning, given the growing need for environmental protection worldwide and the quick…

1

Abstract

Purpose

Accurately predicting the installed capacity of wind energy is essential for energy strategic planning, given the growing need for environmental protection worldwide and the quick development of renewable energy. In order to provide an unprecedented high-precision scheme for wind energy installed capacity prediction and to further become the primary driving force in the process of energy planning and decision-making, this research focuses on overcoming the limitations of conventional prediction models and creatively proposes a multi-parameter collaborative optimization GM(1,1) power model. This will help the energy field advance in a more efficient and scientific direction.

Design/methodology/approach

The theoretical framework of the fundamental GM(1,1) power model is thoroughly examined in this study and serves as the basis for further optimizations. To unlock the potential of each parameter optimization, single-parameter optimization investigations of the model are conducted from the viewpoints of the fractional optimization, background value optimization and grey action optimization, respectively. Conversely, an inventive multi-parameter collaborative optimization power model is built. The model is given dynamic flexibility by adding time-varying parameters. The sine function and interpolation technique are used to further optimize the background value. The model’s meaning is enhanced by the inclusion of a power exponent. Furthermore, several parameters are cooperatively tuned with the aid of the sophisticated Firefly algorithm, giving the model stronger predictive powers. A multi-dimensional and multi-regional model comparison analysis is formed by selecting the wind energy installed capacity data of North America, Italy, Japan and South Korea for in-depth empirical analysis in order to confirm the model’s validity.

Findings

The findings show that the multi-parameter collaborative optimization model (Model 5) has an exceptional in-sample and out-of-sample prediction effect. The relative prediction error MAPEs are 0.41% and 0.31%. It has a clear advantage over the simple GM(1,1) power model and other single optimization models in applications in North America, South Korea, Japan, and Italy. Its seven variable parameters are the reason for this. These factors help create a very accurate prediction effect through joint optimization from multiple perspectives. It is noteworthy that Model 4’s nonlinear optimization of the grey action is impressive. It performs better than background value optimization and fractional-order optimization. Furthermore, according to the model’s prognosis, North America’s installed wind energy capacity is expected to develop linearly and reach 513.214 bn kilowatts in 2035. This gives the planning for energy development in this area a vital foundation.

Originality/value

The novel idea of the multi-parameter collaborative optimization GM(1,1) power model and its clever integration with the firefly method to accomplish parameter optimization constitute the fundamental value of this study. The substantial benefits of multi-parameter optimization in the stability of the prediction effect have been firmly validated by a thorough comparison with the basic and single-optimization models. Like a lighthouse, this novel model illuminates a more accurate path for wind energy installed capacity prediction and offers high-value reference bases for a variety of aspects, including government energy planning, enterprise strategic layout, investor decision-making direction, fostering technological innovation, advancing academic research and developing energy transformation strategies. As a result, it becomes a significant impetus for the growth of the energy sector.

Highlights

  • (1)

    This study proposes a new gray prediction model. Compared with the traditional grey prediction model, the modeling mechanism of this model is optimized.

  • (2)

    This study is based on multi-parameter collaborative optimization to achieve the improvement of model prediction effect. The traditional grey model is two-parameter, while the model proposed in this study is seven-parameter collaborative optimization;

  • (3)

    In this study, swarm intelligence algorithm-firefly algorithm is used to optimize the hyperparameters, so as to obtain the best cooperative optimization multi-parameter values;

  • (4)

    The application of the model is divided into two parts: empirical and application. In the empirical stage, 5 kinds of prediction models are used to predict, which proves that the model proposed in this paper is effective and improves the prediction accuracy. The application part uses the model to forecast the installed wind power capacity in North America, and the future development trend is linear growth, which is expected to double the installed capacity by 2035.

This study proposes a new gray prediction model. Compared with the traditional grey prediction model, the modeling mechanism of this model is optimized.

This study is based on multi-parameter collaborative optimization to achieve the improvement of model prediction effect. The traditional grey model is two-parameter, while the model proposed in this study is seven-parameter collaborative optimization;

In this study, swarm intelligence algorithm-firefly algorithm is used to optimize the hyperparameters, so as to obtain the best cooperative optimization multi-parameter values;

The application of the model is divided into two parts: empirical and application. In the empirical stage, 5 kinds of prediction models are used to predict, which proves that the model proposed in this paper is effective and improves the prediction accuracy. The application part uses the model to forecast the installed wind power capacity in North America, and the future development trend is linear growth, which is expected to double the installed capacity by 2035.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 5 December 2023

Şeniz Özhan, Erkan Ozhan and Ozge Habiboglu

Brand reputation (BR) is one of the most important factors that affect the consumer–brand relationship and give businesses a competitive advantage. Businesses with a strong BR can…

222

Abstract

Purpose

Brand reputation (BR) is one of the most important factors that affect the consumer–brand relationship and give businesses a competitive advantage. Businesses with a strong BR can increase their market shares and product market prices, in addition to gaining a competitive advantage. In order for businesses to have these advantages, they need to know and analyze their consumers. This study aimed to develop an alternative analysis method by using classification algorithms and regression analysis to measure and evaluate the effect of consumers' BR perceptions on their willingness to pay premium prices (WPP).

Design/methodology/approach

The research data were collected from 483 participants by the online survey method due to the COVID-19 pandemic. The data were first analyzed with regression analysis, and the effect of BR on WPP was found to be significant. Then, using artificial intelligence (AI) methods that were not used in previous studies, consumers' perceptions of BR and WPP were clustered and classified.

Findings

The results revealed the highest and lowest customer groups with BR and WPP and empirically demonstrated that highly accurate practical classification models can be applied to determine strategies in line with these findings.

Originality/value

The model proposed in this study offers an integrated approach by using AI and regression analysis together and tries to fill the gap in the literature in this field. Therefore, the novelty of this study is to quantitatively reveal and evaluate the relationship between BR and WPP by using AI classification algorithms and regression analysis together.

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Article
Publication date: 26 February 2025

Xiaoxiao Meng and Jiaxin Liu

This study aims to explain the privacy paradox, wherein individuals, despite privacy concerns, are willing to share personal information while using AI chatbots. Departing from…

0

Abstract

Purpose

This study aims to explain the privacy paradox, wherein individuals, despite privacy concerns, are willing to share personal information while using AI chatbots. Departing from previous research that primarily viewed AI chatbots from a non-anthropomorphic approach, this paper contends that AI chatbots are taking on an emotional component for humans. This study thus explores this topic by considering both rational and non-rational perspectives, thereby providing a more comprehensive understanding of user behavior in digital environments.

Design/methodology/approach

Employing a questionnaire survey (N = 480), this research focuses on young users who regularly engage with AI chatbots. Drawing upon the parasocial interaction theory and privacy calculus theory, the study elucidates the mechanisms governing users’ willingness to disclose information.

Findings

Findings show that cognitive, emotional and behavioral dimensions all positively influence perceived benefits of using ChatGPT, which in turn enhances privacy disclosure. While cognitive, emotional and behavioral dimensions negatively impact perceived risks, only the emotional and behavioral dimensions significantly affect perceived risk, which in turn negatively influences privacy disclosure. Notably, the cognitive dimension’s lack of significant mediating effect suggests that users’ awareness of privacy risks does not deter disclosure. Instead, emotional factors drive privacy decisions, with users more likely to disclose personal information based on positive experiences and engagement with ChatGPT. This confirms the existence of the privacy paradox.

Research limitations/implications

This study acknowledges several limitations. While the sample was adequately stratified, the focus was primarily on young users in China. Future research should explore broader demographic groups, including elderly users, to understand how different age groups engage with AI chatbots. Additionally, although the study was conducted within the Chinese context, the findings have broader applicability, highlighting the potential for cross-cultural comparisons. Differences in user attitudes toward AI chatbots may arise due to cultural variations, with East Asian cultures typically exhibiting a more positive attitude toward social AI systems compared to Western cultures. This cultural distinction—rooted in Eastern philosophies such as animism in Shintoism and Buddhism—suggests that East Asians are more likely to anthropomorphize technology, unlike their Western counterparts (Yam et al., 2023; Folk et al., 2023).

Practical implications

The findings of this study offer valuable insights for developers, policymakers and educators navigating the rapidly evolving landscape of intelligent technologies. First, regarding technology design, the study suggests that AI chatbot developers should not focus solely on functional aspects but also consider emotional and social dimensions in user interactions. By enhancing emotional connection and ensuring transparent privacy communication, developers can significantly improve user experiences (Meng and Dai, 2021). Second, there is a pressing need for comprehensive user education programs. As users tend to prioritize perceived benefits over risks, it is essential to raise awareness about privacy risks while also emphasizing the positive outcomes of responsible information sharing. This can help foster a more informed and balanced approach to user engagement (Vimalkumar et al., 2021). Third, cultural and ethical considerations must be incorporated into AI chatbot design. In collectivist societies like China, users may prioritize emotional satisfaction and societal harmony over privacy concerns (Trepte, 2017; Johnston, 2009). Developers and policymakers should account for these cultural factors when designing AI systems. Furthermore, AI systems should communicate privacy policies clearly to users, addressing potential vulnerabilities and ensuring that users are aware of the extent to which their data may be exposed (Wu et al., 2024). Lastly, as AI chatbots become deeply integrated into daily life, there is a growing need for societal discussions on privacy norms and trust in AI systems. This research prompts a reflection on the evolving relationship between technology and personal privacy, especially in societies where trust is shaped by cultural and emotional factors. Developing frameworks to ensure responsible AI practices while fostering user trust is crucial for the long-term societal integration of AI technologies (Nah et al., 2023).

Originality/value

The study’s findings not only draw deeper theoretical insights into the role of emotions in generative artificial intelligence (gAI) chatbot engagement, enriching the emotional research orientation and framework concerning chatbots, but they also contribute to the literature on human–computer interaction and technology acceptance within the framework of the privacy calculus theory, providing practical insights for developers, policymakers and educators navigating the evolving landscape of intelligent technologies.

Details

Online Information Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1468-4527

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Article
Publication date: 24 February 2025

Anett Erdmann and Luis Toro-Dupouy

In higher education, the appreciation and implementation of artificial intelligence (AI) has led to debate and polarization. This study examines how the institutional environment…

9

Abstract

Purpose

In higher education, the appreciation and implementation of artificial intelligence (AI) has led to debate and polarization. This study examines how the institutional environment at universities impacts the value perception and intention to adopt AI in higher education. It seeks to optimize the adoption pathway by identifying essential “must-have factors” and “value drivers.”

Design/methodology/approach

Employing the technology-organization-environment (TOE) framework alongside the technology acceptance model (TAM) framework and perceived value concepts, this research utilizes a partial least squares structural equation modeling (PLS-SEM) approach complemented by necessary condition analysis (NCA), a novel approach in this context, which allows us to distinguish between critical antecedents and value drivers in the evaluation and adoption of AI technology.

Findings

Results indicate that organizational complexity, readiness, competency, compatibility and competitive advantage significantly affect the perceived value of AI, mediated by stakeholders’ perceptions of its ease of use and usefulness. These factors enhance the intention to adopt AI in higher education. Perceived usefulness has the highest effect size and the importance of ease of use differs between Europe and South America. External factors like partner support and competitive advantage are necessary considerations to be met, while competitive advantage and some internal factors are sufficient conditions for AI adoption.

Originality/value

The study underscores the importance of certain institutional factors, setting priorities for management actions in AI adoption. It differentiates between the general appreciation of AI and the intention of practical implementation, highlighting the role of partner support as a necessary condition, although a non-value-driving factor.

Details

European Journal of Innovation Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1460-1060

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Article
Publication date: 3 July 2024

Dongyu Chen

The emergence of crowdfunding has provided a new way to solve the problem of financing difficulties for start-ups. Although crowdfunding has attracted great attention from the…

55

Abstract

Purpose

The emergence of crowdfunding has provided a new way to solve the problem of financing difficulties for start-ups. Although crowdfunding has attracted great attention from the government and industry, investors are still hesitating to invest online. This paper aims to investigate the investment behavior of China’s debt-based internet crowdfunding market and construct a model to explain online investment decisions from the perspectives of trust and economic valence.

Design/methodology/approach

This paper uses the survey method to verify the research model. The questionnaire was constructed based on the items in prior studies. The data were collected from users with project investment experience on a leading crowdfunding platform in China. A total of 474 valid responses were collected. Partial least squares-structural equation modeling was used in the data analysis.

Findings

This paper finds that initial trust and perceived benefits are the key factors influencing investment intentions; investors are more concerned about potential benefits than about risk; perceived benefits partially mediate the relationship between initial trust and investment intentions. Fundraisers’ reputation is also found to have a greater impact on investors’ initial trust than investors’ trust propensity.

Originality/value

The conclusions of this paper provide important insights into the design of internet crowdfunding platforms and the regulation of policymakers.

Details

Nankai Business Review International, vol. 16 no. 1
Type: Research Article
ISSN: 2040-8749

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