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Under the carbon tax policy, the authors examine the operational decisions of the low-carbon supply chain with the triple bottom line.
Abstract
Purpose
Under the carbon tax policy, the authors examine the operational decisions of the low-carbon supply chain with the triple bottom line.
Design/methodology/approach
This paper uses the Stackelberg game theory to obtain the optimal wholesale prices, retail prices, sales quantities and carbon emissions in different cases, and investigates the effect of the carbon tax policy.
Findings
This study’s main results are as follows: (1) the optimal retail price of the centralized supply chain is the lowest, while that of the decentralized supply chain where the manufacturer undertakes the carbon emission reduction (CER) responsibility and the corporate social responsibility (CSR) is the highest under certain conditions. (2) The sales quantity when the retailer undertakes the CER responsibility and the CSR is the largest. (3) The supply chain obtains the highest profits when the retailer undertakes the CER responsibility and the CSR. (4) The environmental performance impact decreases with the carbon tax.
Practical implications
The results of this study can provide decision-making suggestions for low-carbon supply chains. Besides, this paper provides implications for the government to promote the low-carbon market.
Originality/value
Most of the existing studies only consider economic responsibility and social responsibility or only consider economic responsibility and environmental responsibility. This paper is the first study that examines the operational decisions of low-carbon supply chains with the triple bottom line under the carbon tax policy.
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Keywords
Zhenyi Tang, Pengyi Zhang, Yujia Li and Preben Hansen
To gain a deeper understanding of users’ health information adoption and to promote the effectiveness of health information spread in the context of online limited information…
Abstract
Purpose
To gain a deeper understanding of users’ health information adoption and to promote the effectiveness of health information spread in the context of online limited information, this paper aims to examine how the information-motivation-behavioural (IMB) skills model can be used to organize online health information by experimenting how different IMB elements (information, motivation and behavioural skills) affect users’ intention to adopt health information.
Design/methodology/approach
The authors conducted an experiment with 48 participants who received health articles with various combinations and sequences of IMB elements, analysing the impact on information adoption intention to share and practice. The authors also examined the mediation effect of information usefulness and the moderating effect of perceived health status.
Findings
The authors found that: users’ adoption intention of information was influenced by the order of used IMB elements, not the number of elements used; users were more likely to adopt information that started with behavioural skills rather than the model-prescribed IMB sequence; and perceived usefulness mediated the relationship between IMB elements and users’ adoption intention, which means users with different levels of health status all pay more attention to information usefulness and practicability.
Originality/value
The study contributes to research on health communication by showing how the IMB model can be applied online to enhance the effectiveness of health information dissemination. It can also help online health communities arrange more effective and engaging health messages to promote users’ willingness to adopt.
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Keywords
Yujia Liu, Changyong Liang, Jian Wu, Hemant Jain and Dongxiao Gu
Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus…
Abstract
Purpose
Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.
Design/methodology/approach
This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.
Findings
The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.
Originality/value
The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.
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Tayfun Yörük, Nuray Akar and Neslihan Verda Özmen
The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.
Abstract
Purpose
The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.
Design/methodology/approach
In this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were.
Findings
On the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies.
Research limitations/implications
In this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs).
Originality/value
In the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.
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