Search results

The long-term development of a mobile gaming application (app) depends on its continued use by its users. The expectation–confirmation model of IS continuance was used as the basic framework, to which bi-dimensional consumption emotions were added to help better explain satisfaction judgment and continuance intention in the context of mobile gaming app use. The paper aims to discuss this issue.

The data were analyzed using structural equation models. The effects of positive consumption emotions and negative consumption emotions were examined, respectively, in Models 1 and 2. Competing models (Models 3 and 4) were also examined in order to compare the proposed model.

Both PE and NE have an effect on the satisfaction of mobile gaming app users and their continued usage intention, a finding that represents an important contribution to the extension of technology continuance theory. Comparison with the IS continuance model shows that the new model can explain significantly more variance in continuance intention.

Mobile gaming firms should pay attention to users’ consumption emotions, especially negative emotions. Some specific emotions involved in mobile gaming app use were identified, which could guide firms’ understanding of users’ emotions.

This study offers insight into the role of consumption emotions in forming continuance intentions toward mobile gaming app use in China, a topic that has not previously been investigated.

Do social movement organizations increase the supply of a public good? We address this question by investigating the role of generalist social movement organizations and technology-focused organizations for the development of the electric vehicle (EV) charging infrastructure in California from 1995 until 2012. We find that increases in the membership of Electric Auto Association (EAA) chapters in the cities of California enhanced the number of EV charging stations set up in each city. Our analyses also show that the organizational diversity of the environmental movement spurred the growth of EAA membership but did not directly increase the establishment of charging stations.

The present study aims to examine the use of open access (OA) scholarly communication in India and investigate the factors affecting the adoption and use of OA scholarly communication among researchers.

The study adopted a quantitative research approach using a survey method. Science Citation Index Expanded (SCI-EXPANDED) of Web of Science database was selected as a source for identifying potential researchers and researchers' contact details. A web-based questionnaire was designed using Google Forms, and a link to the questionnaire was sent by email to 4,237 researchers belonging to Science and Technology. Unified theory of acceptance and use of technology (UTAUT) is the primary basis for formulating the present study's conceptual model. Hierarchical multiple regression (HMR) was applied for identifying the factors that influence the adoption and use of OA scholarly communication.

The study found that researchers have limited knowledge of different OA concepts, initiatives and resources, resulting in a deficient level of participation in OA publishing. The HMR analysis authenticates that attitude, facilitating conditions, Internet usage self-efficacy, article processing charge (APC) and researchers' working experience significantly influence the adoption and use of OA scholarly communication. Based on the findings, the study proposed a validated model to investigate the adoption and use of OA scholarly communication in different institutions, research disciplines and developing countries with similar conditions.

The findings have several practical and policy implications for improving OA publishing in India, formulating OA policies and providing directions for further research.

This is the first study focusing on adopting and using OA scholarly communication in India. Findings may be helpful in planning and implementing OA initiatives. The influencing factors and the relative importance identified in the present study offered empirical evidence to demonstrate the researchers' attitudes and perceptions for adopting and using OA scholarly communication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2021-0265.

Behavioral scientists have long sought to capture how individuals’ understandings, perceptions and beliefs affect their decisions, often through examining the underlying cognitive processes that drive action (Schendel & Hofer, 1979). Economists, for example, are interested in how individuals’ utility functions influence their actions. Marketing researchers investigate how consumers’ preferences are reflected in their purchase behaviors. Organization researchers examine individual characteristics that influence outcomes such as job satisfaction, promotion, and turnover (Aiman-Smith et al., 2002).

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

This study aims to investigate the development of predictive tools in performance measurement and management (PMM), and modeling of a forward‐looking method to help managers to quantitatively target performance measures based on achieving desired improvement, minimum cost and strategic priorities.

A case‐based methodology is used to test the conceptual approach in a production system. Mathematical models are used to model modules of the proposed approach. The proposed approach is applied to an actual conventional power plant unit to show its applicability and superiority over conventional methods.

The developed system enables managers to develop systematic ways to manage future performance; for example, planning, performance forecasting and target setting. The predictive ability of the developed system is comparable with the judgment of the manager in the case company.

This paper proposes the use of mathematical models in the development of performance measures targeted on performance prediction and desired improvement. The paper also offers practical help to organizations to embed a forward‐looking capability into their operations.