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This study aims to investigate the direct effect of intellectual capital on the organizational performance of Indonesian state-owned enterprises (SOEs) and their subsidiaries. Furthermore, it also examines whether the relationship is mediated by open innovation and moderated by organizational inertia.

This study is designed as quantitative research. A survey method is employed to collect data by distributing questionnaires to the upper-level managers of the SOEs and their subsidiaries. A total of 293 questionnaires were distributed to the respondents, and 97 responses were obtained for further analysis. The partial least square structural equation modeling (PLS-SEM) is used to test the hypotheses. A mediation-moderation research framework is employed.

The results show that intellectual capital has a positive effect on organizational performance. Further results also demonstrate that open innovation mediates the intellectual capital–organizational performance relationship and organizational inertia moderates the intellectual capital–organizational performance relationship. Theoretically, the findings contribute to the resource-based view (RBV) and knowledge-based view (KBV) by providing empirical evidence of the importance of distinctive internal resources in achieving superior organizational performance. Practically, the findings provide strategic information for managers that they should properly manage intellectual capital, open innovation and organizational inertia because of their effects on organizational performance.

First, this study addresses the previous research gaps by confirming that intellectual capital has a positive effect on organizational performance in the research setting of an emerging market. Second, by using a mediation research framework, this study shows that open innovation mediates the relationship between intellectual capital and organizational performance. Third, by using a moderating research framework, this study also reveals that organizational inertia weakens the relationship between intellectual capital and organizational performance. Those associations are rarely researched.

The adoption of mobile payment remains low in certain regions, highlighting the need to identify the factors that enable and inhibit its adoption. This study aims to address this gap by investigating the role of information security, loss aversion and the moderating influence of the herd effect on Inertia and behavioral intentions in the adoption of mobile payment systems.

A structural equation model was developed and tested with 332 valid questionnaires to examine the proposed hypotheses.

The empirical results reveal that information security plays a significant role as an enabler, while loss aversion acts as an inhibitor of mobile payment adoption. Furthermore, the study uncovers the moderating influence of the herd effect on the relationship between Inertia and behavioral intentions.

This study was conducted in a specific region and may not be generalizable to other regions. Future studies could expand the sample size and scope to enhance the external validity of the findings.

This study offers practical implications for mobile payment service providers. Understanding the key enabling and inhibiting factors identified in this study can guide providers in designing and improving their services. Strengthening information security measures can help build trust among potential adopters, while offering incentives can mitigate the impact of loss aversion and encourage early adoption.

The findings of this study have social implications as they contribute to promoting the adoption of mobile payment systems. Increased adoption can enhance financial inclusion and stimulate economic development.

This study provides novel insights into the enabling and inhibiting factors of mobile payment adoption and highlights the moderating role of the herd effect. By shedding light on the influence of social norms on individual behavior in the context of mobile payment adoption, this study contributes to the existing literature and advances our understanding of this phenomenon.

One of the challenges encountered in the design of guided projectiles is their prohibitive cost. To diminish it, an appropriate avenue many researchers have explored is the use of the non-actuator method for guiding the projectile to the target. In this method, biologically inspired by the flying concept of the single-winged seed, for instance, that of maple and ash trees, the projectile undergoes a helical motion to scan the region and meet the target in the descent phase. Indeed, the projectile is a decelerator device based on the autorotation flight while it attempts to resemble the seed’s motion using two wings of different spans. There exists a wealth of studies on the stability of the decelerators (e.g. the mono-wing, samara and pararotor), but all of them have assumed the body (exclusive of the wing) to be symmetric and paid no particular attention to the scanning quality of the region. In practice, however, the non-actuator-guided projectiles are asymmetric owing to the presence of detection sensors. This paper aims to present an analytical solution for stability analysis of asymmetric decelerators and apprise the effects of design parameters to improve the scanning quality.

The approach of this study is to develop a theoretical model consisting of Euler equations and apply a set of non-dimensionalized equations to reduce the number of involved parameters. The obtained governing equations are readily applicable to other decelerator devices, such as the mono-wing, samara and pararotor.

The results show that the stability of the body can be preserved under certain conditions. Moreover, pertinent conclusions are outlined on the sensitivity of flight behavior to the variation of design parameters.

The analytical solution and sensitivity analysis presented here can efficiently reduce the design cost of the asymmetric decelerator.

This exploratory study aims to investigate and identify the factors influencing discontinuance intention among past users of local digital wallets in Brunei Darussalam.

This study uses a mixed-method approach that integrates quantitative and qualitative research method. An online survey is distributed via widely used social media platforms, using purposive sampling to target previous users of local digital wallets. Structured questionnaires capture demographic data, whereas open-ended inquiries delve into reasons for discontinuation. Descriptive analysis will extract the demographic profiles of the samples. Inductive thematic analysis, guided by Braun and Clarke's framework, will extract and analyze qualitative responses to unveil emergent themes. Data saturation, anticipated beyond 12 responses, will signify sample adequacy.

Demographic profiles based on gender, age and payment preferences of discontinuers supplement the justification for identified themes influencing digital wallet discontinuation in Brunei Darussalam. These themes include “Acceptability Challenge,” highlighting limited vendor acceptance; “Financial Management and Security Issues,” revealing concerns over impulsive buying behavior and security robustness; “Limited Benefits,” referring to short-term interest driven by promotional benefits; “Technological Inertia,” emphasizing reluctance to change from conventional payment methods and “Technical Challenges,” encompassing internet connectivity and operational functionality issues.

This study acknowledges few limitations, including a limited number of respondents, comprising majorly of the younger age groups and females. Self-reported data usage introduces potential response bias, impacting result validity. The qualitative approach limits comprehensive understanding, suggesting validation through quantitative correlational studies. Additionally, the cross-sectional design restricts insight into the dynamic nature of digital wallet discontinuance in Brunei, suggesting the need for longitudinal studies.

The findings of this study offer valuable insights for digital wallet providers, policymakers and businesses operating within the realm of Brunei Darussalam. By tackling pertinent issues such as vendor acceptance, financial security and promotional incentives, stakeholders can effectively improve user experiences and mitigate intentions of discontinuing usage. Recommended strategies encompass the enlargement of vendor networks, the implementation of stringent security measures and the customization of promotional campaigns. Furthermore, comprehending demographic inclinations enables the tailoring of offerings, thereby fostering enduring adoption rates.

This study’s findings hold social significance for financial inclusion, technological literacy and consumer empowerment in Brunei Darussalam. Overcoming barriers to digital wallet adoption, such as limited vendor acceptance, promotes financial inclusion in the long run. Improved understanding of digital wallets enhances technological literacy and empowers users to make informed decisions. By catering to diverse demographic needs, stakeholders can promote social equity and ensure widespread access to digital payment benefits, thus positively impacting Brunei Darussalam’s socioeconomic landscape.

This study contributes to the existing knowledge gap on digital wallet discontinuance in Brunei Darussalam. By uncovering key themes and factors influencing past users’ decisions, it advances understanding in the context of postadoption dynamics. The study provides valuable insights for local and global fintech adoption strategies.

The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.

The main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.

Longitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.

This paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

Although much attention has been paid to understanding employee resistance to reform, little study has been done to explore the effect of employee resistance to public service units' (PSUs) reform in China. To address this need, this work aims to investigate the antecedents of employee resistance to PSUs' reform, especially from the perspective of the heterogeneity of the employees' age.

This study considers the PSUs in Harbin, China, as an example and uses survey questionnaires to analyze the factors influencing employees' resistance when PSUs reform. Besides, the authors developed a research model based on the status quo bias theory, the equity-implementation model.

According to the applied research model, employee resistance to PSU change is primarily influenced by perceived switching costs and benefits. According to their age, this survey also confirms how the employees responded to the reform implementation.

The results of this empirical study inform suggestions for the sustainable development of PSUs and organizational transformations. Overall, this work advances the theoretical understanding of employees' resistance to PSUs’ reform, thereby offering practical insights for managing employee resistance during organizational change.

Overall, given that employee resistance emotion exists in an organization, this study offers theoretical and practical implications for change management strategies.

The purpose of this study is to examine the behaviour of retail investors while making an investment decision and how it gets affected by the behavioural biases of the investors using a moderated-mediation framework.

A mixed method approach has been used to fulfil the objectives of the study. In the first study, a qualitative analysis of the interviews with 15 retail investors was conducted. As part of the quantitative study, a total of 201 responses from Indian retail investors were collected using systematic sampling and analysed using structural equation modelling and Process Macro.

The results indicate that anchoring bias, availability bias, herding bias, switching cost, sunk cost, regret avoidance and perceived threat have a significant effect on retail investors’ investing intention. The attitude of the investors towards investing decisions mediates the effects of behavioural bias and the status quo on investment intention. The results of the moderated-mediation analysis indicate that mediating effect of attitude varied at the low and high-risk aversion of investors.

The findings of this study will help regulators and retail investors to understand the critical behavioural biases which affect the investors’ investing intention.

The paper contributes to the literature on investors’ behaviour, status quo bias theory (SQB) and behavioural bias. This study uniquely proposes a moderated-mediation framework to understand the effects of biases on retail investors’ investment intention.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

This study looks to understand the opposing forces that would influence continuance intention. This is significant as users will take into account the positive and negative use experiences in determining their continuance intention. Therefore, this study looks to highlight the opposing forces of users’ continuance intention by proposing the Expectation-Confirmation-Resistance Model (ECRM).

Through an online survey, 411 responses were obtained from mobile payment users. Subsequently, a hybrid approach comprised of the Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) was utilized to analyze the data.

The results revealed that all hypotheses proposed in the ECRM are supported. More precisely, the facilitating and inhibiting variables were found to significantly affect continuance intention. In addition, the ECRM was revealed to possess superior explanatory power over the original model in predicting continuance intention.

This study successfully developed and validated the ECRM which captures both facilitators and inhibitors of continuance intention. Besides, the relevance and significance of users’ innovative resistance to continuance intention have been highlighted. Following this, effective business and research strategies can be developed by taking into account the opposing forces that affect users’ continuance intention.

This study aims to develop a model of learning-by-hiring in which knowledge gains may occur at the time of recruitment but also after recruitment when other incumbent organizational members assimilate a recruit’s knowledge. The author’s model predicts that experienced recruits are more likely to catalyze change to their organization’s core technological capabilities.

The continuous-time parametric hazard rate regressions predict core technological change in a long panel (1970–2017) of US biotechnology industry patent data. The author uses over 140,000 patents to model the evolution of knowledge of over 52,000 scientists and over 4,450 firms. To address endogeneity concerns, the author uses the Heckman selection method and does robustness tests using a difference-in-difference analysis.

The author finds that a hire’s prior research and development (R&D) experience helps overcome inertia arising from her or his new-to-an-organization “distant” knowledge to increase the likelihood of core technological change. In addition, while the author finds that incumbent organizational members resist technological change, experienced hires may effectively induce them to adopt new ways of doing things. This is particularly the case when hires collaborate with incumbents in R&D projects. Understanding the effects of hiring on core technological change, therefore, benefits from an assessment of hire R&D experience and its effects on incumbent inertia in an organization.

First, the author does not recommend managers to hire scientists with considerable distant knowledge only as this may be detrimental to core technological change. Second, the author recommends organizations striving to effectuate technological change to hire people with considerable prior R&D experience as this confers them with the ability to influence other members and socialize incumbent members. Third, the author recommends that managers hire people with both significant levels of prior experience and distant knowledge as they are complements. Finally, the author recommends managers to encourage collaboration between highly experienced hired scientists and long-tenured incumbent organizational members to facilitate incumbent learning, socialization and adoption of new ways of doing things.

This study develops a model of learning-by-hiring, which, to the best of the authors’ knowledge, is the first to propose, test and advance KM literature by showing the effectiveness of experienced hires to stimulate knowledge diffusion and core technological change over time after being hired. This study contributes to innovation, organizational learning and strategy literatures.