Kybernetes最新文献

Purpose

Since the introduction of the outstanding web AI chat system, ChatGPT, it has caused a significant impact in both academia and the business world. Many studies have started to explore its potential applications in various fields. However, there is a lack of research from the perspective of user experience. To fill this theoretical gap and provide a theoretical basis for the operation and design of related services, this study plans to develop a set of evaluation scales for AI chat system user experience and explore the relationship between various factors and user satisfaction.

Design/methodology/approach

This study obtained 41 evaluation indicators through literature review and user research. Subsequently, these indicators were used as questionnaire items, combined with satisfaction metrics. A total of 515 questionnaires were distributed, and factor analysis and linear regression were employed to determine the specific elements influencing user experience and the user satisfaction model.

Findings

This study found that the factors influencing user experience are usefulness, accuracy, logical inference, interactivity, growth, anthropomorphism, convenience, credibility, ease of use, creativity, and security. Among these factors, only accuracy, anthropomorphism, creativity, and security indirectly influence satisfaction through usefulness, while the rest of the factors have a direct positive impact on user satisfaction.

Originality/value

This study provides constructive suggestions for the design and operation of related services and serves as a reference for future theoretical research in this area.

Purpose

In this paper, we aim to provide an extensive analysis to understand how various factors influence electricity prices in competitive markets, focusing on the day-ahead electricity market in Romania.

Design/methodology/approach

Our study period began in January 2019, before the COVID-19 pandemic, and continued for several months after the onset of the war in Ukraine. During this time, we also consider other challenges like reduced market competitiveness, droughts and water scarcity. Our initial dataset comprises diverse variables: prices of essential energy sources (like gas and oil), Danube River water levels (indicating hydrological conditions), economic indicators (such as inflation and interest rates), total energy consumption and production in Romania and a breakdown of energy generation by source (coal, gas, hydro, oil, nuclear and renewable energy sources) from various data sources. Additionally, we included carbon certificate prices and data on electricity import, export and other related variables. This dataset was collected via application programming interface (API) and web scraping, and then synchronized by date and hour.

Findings

We discover that the competitiveness significantly affected electricity prices in Romania. Furthermore, our study of electricity price trends and their determinants revealed indicators of economic health in 2019 and 2020. However, from 2021 onwards, signs of a potential economic crisis began to emerge, characterized by changes in the normal relationships between prices and quantities, among other factors. Thus, our analysis suggests that electricity prices could serve as a predictive index for economic crises. Overall, the Granger causality findings from 2019 to 2022 offer valuable insights into the factors driving energy market dynamics in Romania, highlighting the importance of economic policies, fuel costs and environmental regulations in shaping these dynamics.

Originality/value

We combine principal component analysis (PCA) to reduce the dataset’s dimensionality. Following this, we use continuous wavelet transform (CWT) to explore frequency-domain relationships between electricity price and quantity in the day-ahead market (DAM) and the components derived from PCA. Our research also delves into the competitiveness level in the DAM from January 2019 to August 2022, analyzing the Herfindahl-Hirschman index (HHI).

Purpose

It is only logical that a firm aims to make a profit after entering the market. However, some firms enter the market with the goal of market expansion and even burn money to pursue market share, which is counterintuitive in practice. To explore the theoretical foundations behind this rare phenomenon, this paper focuses on discussing the impact of the market expansion entry strategy on the entrant firm and the incumbent firm.

Design/methodology/approach

Using a game theory model of a supply chain with an incumbent and an entrant, this paper explores the mathematical conditions for the entrant to adopt either the traditional or the market expansion entry strategy and investigates the incumbent’s benefits and losses under different entry strategies.

Findings

The results show that when the market-expansion effect and the selling price ceiling are moderate, the entrant firm always adopts the market expansion entry strategy, and the incumbent firm obtains a free ride from the entrant firm and benefits from it. The entire industry profits and the industry consumer surplus are increased. In particular, we further investigate the cases in which the incumbent firm has a first-mover advantage or there is a troublesome cost, and the results confirm the aforementioned conclusions.

Originality/value

By considering market share as the entrant’s goal, this paper contributes to the dual-purpose literature. Moreover, based on the model’s mathematical results, this paper offers relevant management insights for the entrant and its stakeholders in the e-commerce platform.

Purpose

Social network group decision-making (SNGDM) has rapidly developed because of the impact of social relationships on decision-making behavior. However, not only do social relationships affect decision-making behavior, but decision-making behavior also affects social relationships. Such complicated interactions are rarely considered in current research. To bridge this gap, this study proposes an SNGDM model that considers the interaction between social trust relationships and opinion evolution.

Design/methodology/approach

First, the trust propagation and aggregation operators are improved to obtain a complete social trust relationship among decision-makers (DMs). Second, the evolution of preference information under the influence of trust relationships is measured, and the development of trust relationships during consensus interactions is predicted. Finally, the iteration of consensus interactions is simulated using an opinion dynamics model. A case study is used to verify the feasibility of the proposed model.

Findings

The proposed model can predict consensus achievement based on a group’s initial trust relationship and preference information and effectively captures the dynamic characteristics of opinion evolution in social networks.

Originality/value

This study proposes an SNGDM model that considers the interaction of trust and opinion. The proposed model improves trust propagation and aggregation operators, determines improved preference information based on the existing trust relationships and predicts the evolution of trust relationships in the consensus process. The dynamic interaction between the two accelerates DMs to reach a consensus.

Purpose

This study investigates how the initiatives and controversies related to environmental, social and governance (ESG) explain firm efficiency.

Design/methodology/approach

Firstly, this study applies data envelopment analysis with the epsilon-based measure to estimate the firm efficiency of 80 companies in the Chinese energy sector in 2022. This approach accounts for the diversity and relative importance of inputs and outputs from a multidimensional perspective. Secondly, this study regresses the variables of ESG initiatives and controversies on the estimated firm efficiency scores through a generalised additive model, which can capture nonlinear patterns.

Findings

This study finds that a) the samples have i) about 49% room for improvement in efficiently optimising their resources and business outcomes and ii) the highest scores in governance initiatives, followed by social initiative. b) 69% of them have controversy scores that are greater than the average value. c) A cluster analysis indicates that companies with higher social initiatives have higher firm efficiency than their counterparts. d) ESG initiatives and controversies are nonlinearly related to firm efficiency.

Research limitations/implications

The findings have practical implications for policy makers and managers who prioritise ESG, particularly regarding (i) the need to examine firm performance from a multidimensional perspective, that is, to measure multiple inputs and outputs simultaneously, (ii) the nonlinearity of the nexus between ESG and efficiency in graphical forms, and (iii) the need to balance ESG initiatives and address ESG controversies.

Originality/value

This study integrates statistical approaches in examining and ensuring sustainable growth and efficiency within the Chinese energy sector and beyond.

Purpose

In the context of Industry 4.0, intelligent construction technologies (ICT) represented by information technology and networking will undoubtedly provide new impetus to the development of the prefabricated building supply chain (PBSC), but they will also bring various potential risks. So far, there is a large lack of research on the comprehensive consideration of the risks associated with the intelligent transformation of PBSC based on the information sharing perspective, and the critical risks and interactions are still unclear, making it difficult to identify efficient risk mitigation strategies. Therefore, this paper aims to reveal the interactions between stakeholders and clarify the critical risk nodes and interactions in information sharing of PBSC (IS-PBSC), and propose targeted risk mitigation strategies.

Design/methodology/approach

Firstly, this paper creatively delineates the risks and critical stakeholders of IS-PBSC. Secondly, Data is collected through questionnaires to understand the degree of risks impact. Thirdly, with the help of NetMiner 4 software, social network analysis is conducted and IS-PBSC risk network is established to reveal critical risk nodes and interactions. Finally, further targeted discussion of critical risk nodes, the effectiveness and reasonableness of the risk mitigation strategies are proposed and verified through NetMiner 4 software simulation.

Findings

The results show that the critical risks cover the entire process of information sharing, with the lack of information management norms and other information assurance-related risks accounting for the largest proportion. In addition, the government dominates in risk control, followed by other stakeholders. The implementation of risk mitigation strategies is effective, with the overall network density reduced by 41.15% and network cohesion reduced by 24%.

Research limitations/implications

In the context of Industry 4.0, ICT represented by information technology and networking will undoubtedly provide new impetus to the development of the PBSC, but they will also bring various potential risks. So far, there is a large lack of research on the comprehensive consideration of the risks associated with the intelligent transformation of PBSC based on the information sharing perspective, and the critical risks and interactions are still unclear, making it difficult to identify efficient risk mitigation strategies.

Originality/value

Based on the results of risk network visualization analysis, this paper proposes an ICT-based IS-PBSC mechanism that promotes the development of the integration of ICT and PBSC while safeguarding the benefits of various stakeholders.

Purpose

Since the implementation of the regulatory inquiry system, research on its impact on information disclosure in the capital market has been increasing. This article focuses on a specific area of study using Chinese annual report inquiry letters as the basis. From a text mining perspective, we explore whether the textual information contained in these inquiry letters can help predict financial restatement behavior of the inquired companies.

Design/methodology/approach

Python was used to process the data, nonparametric tests were conducted for hypothesis testing and indicator selection, and six machine learning models were employed to predict financial restatements.

Findings

Some text feature indicators in the models that exhibit significant differences are useful for predicting financial restatements, particularly the proportion of formal positive words and stopwords, readability, total word count and certain textual topics. Securities regulatory authorities are increasingly focusing on the accounting and financial aspects of companies' annual reports.

Research limitations/implications

This study explores the textual information in annual report inquiry letters, which can provide insights for other scholars into research methods and content. Besides, it can assist with decision making for participants in the capital market.

Originality/value

We use information technology to study the textual information in annual report inquiry letters and apply it to forecast financial restatements, which enriches the research in the field of regulatory inquiries.

Purpose

Green innovation in supply chains is crucial for socioeconomic development and stability. Factors that influence collaborative green innovation in the supply chain are complex and diverse. Exploring the main influencing factors and their mechanisms is essential for promoting collaborative green innovation in supply chains. Therefore, this study analyzes how upstream and downstream enterprises in the supply chain collaborate to develop green technological innovations, thereby providing a theoretical basis for improving the overall efficiency of the supply chain and advancing green innovation technology.

Design/methodology/approach

Based on evolutionary game theory, this study divides operational scenarios into pure market and government-regulated operations, thereby constructing collaborative green innovation relationships in different scenarios. Through evolutionary analysis of various entities in different operational scenarios, combined with numerical simulation analysis, we compared the evolutionary stability of collaborative green innovation behavior in supply chains with and without government regulation.

Findings

Under pure market mechanisms, the higher the green innovation capability, the stronger the willingness of various entities to collaborate in green innovation. However, under government regulation, a decrease in green innovation capability increases the willingness to collaborate with various entities. Environmental tax rates and green subsidy levels promote collaborative innovation in the short term but inhibit collaborative innovation in the long term, indicating that policy orientation has a short-term impact. Additionally, the greater the penalty for collaborative innovation breaches, the stronger the intention to engage in collaborative green innovation in the supply chain.

Originality/value

We introduce the factors influencing green innovation capability and social benefits in the study of the innovation behavior of upstream and downstream enterprises, expanding the research field of collaborative innovation in the supply chain. By comparing the collaborative innovation behavior of various entities in the supply chain under a pure market scenario and government regulations, this study provides a new perspective for analyzing the impact of corresponding government policies on the green innovation capability of upstream and downstream enterprises, enriching theoretical research on green innovation in the supply chain to some extent.

Purpose

This study explores how emotional intelligence, customer orientation, deep acting and surface acting influence job satisfaction among middle managers in their interactions with customers, colleagues and business partners. By examining these factors, we aim to provide insights into their collective impact on job satisfaction and interpersonal dynamics within organizational contexts.

Design/methodology/approach

By involving 95 middle managers at Indonesian Internet service providers as respondents, this research used a questionnaire to collect data. Next, the data were analyzed using the partial least square-structural equation modeling (PLS-SEM) technique, which evaluated measurement models and structural models. A total of twelve hypotheses were tested in this study.

Findings

This study found that customer orientation does not have a significant effect on deep acting, thereby nullifying its indirect effect on job satisfaction. Conversely, it's demonstrated that both deep acting and surface acting serve as partial mediators in the relationship between emotional intelligence and job satisfaction. Furthermore, surface acting emerges as a partial mediator in the connection between customer orientation and job satisfaction.

Originality/value

By exploring the relationship between customer orientation, emotional intelligence and job satisfaction among employees, this study seeks to reveal novel insights. The study examines the impact of these critical elements, which are necessary for middle managers to effectively manage their emotions and cultivate significant connections, on their overall job satisfaction and interpersonal dynamics in their diverse responsibilities.

Purpose

This study aims to investigate the landscape of family-based organic farm businesses in the Kashmir Valley, India, analyzing the factors that either facilitate or hinder their adoption. The research also intends to uncover sources of information seeking. The primary purpose is to provide qualitative evidence to address existing knowledge gaps and offer insights for promoting sustainable farm practices in the region.

Design/methodology/approach

The research employs a qualitative approach, drawing on focus group interviews. The study thoroughly explores the background and relevant literature, utilizing a comprehensive research framework. Data is collected from family based farmers engaged in organic farming practices in the Kashmir Valley. The data is analyzed using content analysis ensuring a robust and thorough exploration of the subject matter.

Findings

This study reveals a notable transition in the agricultural landscape of the Kashmir Valley, showcasing a widespread adoption of organic farming on considerable land. The study reveals that key facilitators for organic farming among family-based farms are farm productivity, entrepreneurial intention, governance, environmental consciousness, and health concerns. The exchange of information, both through formal and informal channels, is found to be a crucial factor influencing the adoption of organic farming. The study also unveiled significant inhibitors that hinder the adoption of organic farming on commercial scales, including on-farm challenges such as difficulties in acquiring inputs and facing reduced yields, market-related concerns, and a lack of support and assistance from government agencies.

Originality/value

This research contributes significantly to the existing literature by advancing the understanding of organic farm business and agri-entrepreneurship. It unveils key factors that either support or hinder family-based organic farms, identifying crucial information sources and presenting valuable insights for policymakers. Furthermore, this study provides practical guidance for overcoming obstacles, enhancing infrastructure, and translating identified facilitators into successful agri-ventures in the Kashmir region.