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This paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm.

A novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function.

The simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement.

The adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.

Indian and Chinese businesses are slowly making advances in each other’s markets. The purpose of this study was to understand factors that influence the business environment of a country and how does it compare between India and China.

The study uses firm-level survey data from the World Bank’s Enterprise Surveys. The study considered survey data for 2,700 Chinese firms and 9,281 Indian firms. Exploratory factor analysis and structural equation modeling were used to analyze the factors that drive the business environment in both countries and why differences appear.

The data set reveals different factor structures for India and China. Across both the countries, infrastructure support and governance play a major role in shaping the business landscape, though in the case of India, regulatory dynamics play a crucial role. The study concludes that these differences drive the basic difference in business environment across both countries.

The study considers only the formal sector, and informal businesses have been left out because of paucity of data. The dynamics of informal business sector can be considered for future studies.

While making market entry or investment decisions globally, businesses can analyze country-specific environmental factors through this framework. As the study is based on the perceptions of businesses, policy-makers can also focus on these factors to attract businesses to specific countries or regions.

The study is an original research study. The study adds to the existing academic literature and is expected to help policy-makers and senior managers in focusing on specific aspects of business environment when developing policies or taking market entry decisions.

This study, based on social exchange theory, aims to explore the association between appreciative leadership and employees' helping behaviors by investigating the mediation role of emotional reactions (pride, anxiety), and choosing organizational trust as a boundary condition between appreciative leadership and helping behaviors.

A total sample of 285 reliable questionnaires were collected in three time lags from employees working in the Pakistani education and banking sectors. PROCESS macro using SPSS and AMOS are employed for data analyses of the proposed model.

The findings reveal that appreciative leadership has positive impacts on employees' helping behaviors and emotional reactions (pride, anxiety) mediate the relationship of appreciative leadership and employees' helping behaviors. In addition, the results show that high organizational trust strengthens the positive relationship between appreciative leadership and employees' helping behaviors.

This research has provided empirical proof between the relationship of appreciative leadership and helping behaviors and the findings are of great significance for managers, employees, and organizations. The study proposes that leaders should have appreciative behavior while treating their subordinates. Moreover, it is revealed that the role of organizational trust should be given more attention and importance because it is a factor moderating the employees' helping behaviors.

The present study, among the first empirical efforts investigating the relationship between appreciative leadership and helping behaviors, organizational trust as a moderator, enriches the existing academic literature of and provides worthy insight into the research on appreciative leadership and helping behaviors.

This study aims to explore the impact of information security knowledge on information security compliance behavior using neuroscience science.

This study uses electroencephalography (EEG) equipment to collect brain cognitive data from participants under two specific conditions. The conditions are before and after information security knowledge interventions contextualized with the “Wannacry” ransomware incident.

Firstly, the information security knowledge interventions lead to differences in the participants’ behavioral data under the two conditions, with participants showing increased attention to security warnings after intervention. Secondly, the P300 waveform is observed in the decision-making process for information security compliance behavior both before and after intervention, suggesting that the cognitive processes of the brain are influenced by information security knowledge. Finally, the amplitude of the P300 waveform is larger after intervention compared to before, and the energy in the β frequency band induced by decision-making for information security compliance behavior is higher before intervention than after.

Few studies have focused on the corresponding research regarding how to enhance information security compliance behavior. This study confirms that information security knowledge can significantly improve information security compliance behavior encouraging users to proactively implement security measures to counter information security threats. The findings reveal the impact of EEG on the cognitive processes involved in decision-making for information security compliance behavior and offer a novel approach for improving information security behavior through information security knowledge interventions.

Build-operate-transfer (BOT) contracts are widely used in the construction and operation of charging piles for new energy vehicles worldwide and stipulate that governments grant charging pile operators franchises for a certain period of time to invest in the construction and operation of the charging piles. The charging piles are then transferred to governments when the concession expires. To encourage charging pile operators to build and operate charging piles, governments usually provide two kinds of subsidies, namely construction and operating subsidies.

The authors establish a typical game model to study the optimal BOT contract between a government and a charging pile operator and their preferences for the two kinds of subsidies.

First, the authors show that there are substitution and complementarity effects between the concession period and the subsidy level. Second, the operator prefers the construction subsidy (operating subsidy) when the additional operating cost is low (high). The government prefers the operating subsidy (construction subsidy) when consumer sensitivity to the number of charging piles is low (high) and the concession period is short or long (moderate). Finally, the adjusted joint subsidy can not only improve social welfare but also that the charging pile operator can obtain the same profit as under the operating subsidy at a lower subsidy amount.

This work develops the first analytical model to study two subsidies in the construction and operation of charging piles and investigate the optimal BOT contract and subsidy preferences. The insights are compelling not only for the charging pile operator but also for policymakers in practice from a circular economy perspective.

Mobile social apps permeate every facet of daily life through the pervasive use of smartphones. Customer retention with mobile social apps has become extremely important for app-related companies. The purpose of this paper is to explore why experienced users in mobile social apps (e.g. WeChat) are likely to continue using the app.

This study proposed a conceptual model to identify key determinants of continuance intention of WeChat users and highlight the effects of individual experience. Data were collected from WeChat users, which is one of the most popular mobile social apps in China. The study employed partial least squares regression to test the research model based on a survey of 295 valid responses.

Results showed that trust, which was driven by user satisfaction and perceived critical mass, played a critical role in influencing the continuance intention of WeChat users. Moreover, tie strength exerted a negative moderating effect on the relationship between trust and continuance intention. Specifically, tie strength and perceived critical mass had strong impacts on the continuance intention of low-experience users. In addition, the effect of frequency was closely associated with the continuance intention of high-experience users.

This study addressed the issue of mobile social app continuance intention by providing an innovative means to explore the key antecedents of user continuance intention from the experience perspective. The findings not only prove that trust plays a central role in influencing the continuance intention of experienced users but also reveal that the determinants of continuance intention vary among users with different experience. The results provide insights into the key antecedents of experienced WeChat user continuance intention and contribute to the literature on mobile social apps and individual differences.

The results provide suggestions for mobile social app practitioners to effectively plan mobile social app retention practices and to set up appropriate incentive mechanisms for retaining users with different experiences.

Although abundant studies have focused on the adoption of media users, few studies have investigated the post-adoption behavior of experienced users in the context of mobile social apps. This study revealed the key determinants of the continuance intention of WeChat users and pinpointed the different impacts of these antecedents on users with different levels of experience. It also provides useful guidelines for practitioners to effectively retain users with different levels of experience.

The online profile picture of a doctor serves as a pivotal source of visual information, playing a key role in shaping the doctor’s professionalism in the online health market. Doctors can strategically curate the elements of their online profile pictures, such as attire and background, to either reinforce a formal image or craft a casual image, thereby influencing patients’ doctor choices. Despite this, the relative effectiveness of formal or casual images in swaying patient choices remains unclear. This study endeavors to bridge this knowledge gap.

This study tested the influence of a doctor’s attire and background in their profile picture on patient decision-making, with a focus on discerning variations in impact among patient groups differentiated by the severity of their illnesses (i.e. high-severity versus low-severity conditions). We formulated four hypotheses and tested them through an econometric analysis and a controlled laboratory experiment.

The empirical findings reveal that doctors’ formal profile pictures, characterized by formal attire and backgrounds, exert a more pronounced influence on patient choices than casual images. The severity of a patient’s illness positively moderated the relationship between formal images and patient choices.

These insights make a significant contribution to the understanding of patient behavior in selecting doctors within the online health market. Furthermore, they offer valuable guidance for doctors in optimizing their online profile presentation to better align with patient preferences and expectations.

The purpose of this paper is to develop a concept of an alignment between market and technology orientations (MTs) and investigates the difference in new product innovation activities and performance among the four groups of high-tech firms which are classified into four categories labeled as MT, technology orientation (TO), market orientation (MO) and conservative (CO) firms.

Data were collected from 360 high-tech firms in China. MANOVA was used to identify whether or not new product innovation activities (i.e. timing of market entry and product quality) and performance vary across the four groups.

The results indicate that the four groups of firms significantly differ with respect to new product performance and with new product innovation activities pertaining to timing of market-entry strategy and product quality. Further, the results show that first, MT firms have highest new product performance; second, TO firms are speed leaders which have first-to-market with new product; and third, MO firms are quality champions which are best for the perceived new product quality.

This paper responds to call for synthetic studies of multiple orientations and cross-disciplinary research, especially in the areas of marketing and strategic management. This paper is the first to integrate MO and TO and examine the interactive effects of these two orientations on new product innovation. Against prior study believing the combination of strategic orientations play an important role in innovation management, the findings that TO firms are speed leaders and MO firms are quality champions suggest that the combination of different strategic orientations is not beneficial to all respects of new product innovation, such as timing of market-entry strategy and product quality.

Given the importance of online social network (OSN) media features, many studies have focused on how different types of OSNs with various media features influence users’ usage and engagement. However, a recent literature review indicates that few empirical studies have considered how different types of OSNs with different information accessibility levels influence users’ beliefs and self-disclosure. By comparing two OSN platforms (OSNs with high-level information accessibility vs OSNs with low-level information accessibility), the purpose of this paper is to address this opportunity by investigating the differential impacts of the two platforms on individuals’ psychological cognition – particularly users’ social exchange beliefs – and explaining how these beliefs translate into OSN self-disclosure.

This study used a factorial design approach in an experimental setting to examine how different levels of information accessibility (high vs low), influence the social exchange beliefs (i.e. perceived social capital bridging, perceived social capital bonding and perceived privacy risks) of OSN users and subsequently influence OSN self-disclosure.

The results show that users on OSNs with high-level information accessibility express significantly higher perceived social capital bridging and perceived privacy risks than users on OSNs with low-level information accessibility. However, users on OSNs with low-level information accessibility express higher social bonding beliefs than users on OSNs with high-level information accessibility, indicating that there are different effect mechanisms toward OSN self-disclosure.

The focus of this research helps unveil the complex relationships between OSN design features (e.g. information accessibility), psychological cognition (e.g. social capital bridging, social capital bonding and privacy risks) and OSN self-disclosure. First, it clarifies the relationship between information accessibility and self-disclosure by examining the mediating effect of three core social exchange beliefs. Second, it uncovers the distinct effects of high-level information-accessible OSNs and low-level information-accessible OSNs on OSN self-disclosure.

The increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.

Intelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R², T-Value and GOF.

The study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.

Study limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.

The research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.