Search results

The purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.

This research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.

The methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.

The research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.

The societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.

This research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.

Founded in 2004, OPPO has experienced the boom of the Chinese mobile phone market, the trend of mobile Internet and the prosperity of the smartphone market. While adjusting its business structure based on changes in the market environment, it has transitioned itself from an audio device manufacturer to a smart-phone manufacturer that offers hardware, software, and service.

This case study focuses on OPPO's evolution and strategy, and provides an insight into its history, competition, and strategic choices based on whether or not OPPO should release a feature phone with a foldable display at the MWC 2019, and discusses the core competitiveness that helped OPPO succeed against the market downturn. This case study helps students understand the development of corporate strategies and the process of building core competitiveness in the microcompetition in the red ocean market. We also wish to help students understand how to come up with the most appropriate decision-making framework and conduct a critical analysis on the issues based on the internal and external factors of their businesses while they make strategic decisions. When it comes to different dimensions and indicators coming to contradictory conclusions in particular, what should the manager of a business do to make the correct strategic decision?

The purpose of this study is to investigate how consumer brand engagement (CBE) promotes brand love and eWOM within the influence of brand interactivity and consumer involvement, especially in the context of the smartphone industry and Chinese social media marketing. This study also examines the important role of brand love and gender in this relationship.

Data from 403 participants who are followers of the smartphone brand’s social media page was collected via an online survey. PLS-SEM was also used for examining the research model.

The findings of this research reveal that the (perceived) brand interactivity, and consumer involvement, are key factors influence CBE. As expected, brand love and consumers’ eWOM intention are significant outcomes of CBE. In particular, the relationship between CBE and consumers’ eWOM intention is mediated by brand love. However, gender did not moderate the proposed relationships.

By integrating social exchange theory and attribution theory, this study deepens the understanding of CBE and brand love by examining the relationship between brand interactivity, consumer involvement, CBE, brand love and eWOM in the context of smartphone and Chinese social media marketing.

Smartphone apps collect users' personal information, which triggers privacy concerns for app users. Consequently, app users restrict apps from accessing their personal information. This may impact the effectiveness of in-app advertising. However, research has not yet demonstrated what factors impact app users' decisions to use apps with restricted permissions. This study is aimed to bridge this gap.

Using a quantitative research method, the authors collected the data from 384 app users via a structured questionnaire. The data were analysed using AMOS and fuzzy-set qualitative comparative analysis (fsQCA).

The findings suggest privacy concerns and risks have a significant positive effect on app usage with restricted permissions, whilst reputation, trust and perceived benefits have significant negative impact on it. Some app-related factors, such as the number of apps installed and type of apps, also impact app usage with restricted permissions.

Based on the findings, the authors provided several implications for app stores, app developers and app marketers.

This study examines the factors that influence smartphone users' decisions to use apps with restricted permission requests. By doing this, the authors' study contributes to the consumer behaviour literature in the context of smartphone app usage. Also, by explaining the underlying mechanisms through which the principles of communication privacy management theory operate in smartphone app context, the authors' research contributes to the communication privacy management theory.

The dearth of leadership competencies to transform traditional industries to Industry 4.0 is a barrier to global production. This study explains the deficiencies in leadership competencies that hinder the transformation of traditional industries to Industry 4.0.

Leadership was explained into transactional leadership, digital leadership and Leadership 4.0. Then, the network of relationships between these leadership constructs was plotted in a path diagram to learn the mediating effect of digital leadership.

The results indicate that a lack of digital competencies to coordinate tasks, share information and solve problems in a digitalized environment is the barrier to the transformation.

The findings can be used in human resources (HR) management. In addition, the findings provide evidence to present the contingency theory as a universal theory of leadership.

The study is the first to assess the mediating effect of digital leadership on transactional leadership to explain the changes to strategic leadership due to the emergence of Leadership 4.0.

Complex technology not only provides potential economic benefits but also increases the difficulty of application. Whether and how upstream technological complexity affects downstream manufacturers' innovation through vertical separation structure is worth discussing, but it has not been effectively discussed.

Through theoretical analysis and empirical testing, this article discusses the cost effect and market competition effect caused by upstream technological complexity on downstream manufacturers and further elucidates the impact of upstream technological complexity on downstream manufacturers' innovation.

Research has found that the impact of upstream technological complexity on the downstream manufacturers' innovation depends on the cost effect and market competition effect. The cost effect caused by the complexity of upstream technology inhibits the innovation of downstream manufacturers. In contrast, the market competition effect promotes the innovation of downstream manufacturers. There are differences in the cost effect and market competition effect of upstream technological complexity on different types of downstream manufacturers, so there is also significant heterogeneity in the impact of upstream technological complexity on innovation of different types of downstream manufacturers.

The conclusions of this article improve the understanding of the relationship between upstream technological complexity and downstream innovation and provide helpful implications for industrial chain innovation.

This paper aims to investigate the causal inferences between mobile application adoption and changes in travelers’ purchasing behavior regarding services supported by the travel and tourism industry.

Using a quasi-experiment conducted by an airline, data sets from more than 10,000 travelers were collected, and hypotheses were tested using propensity score matching and difference-in-difference methods.

Mobile application adoption has a significant positive effect on the total purchasing frequency of services and a significant adverse effect on booking tickets in advance and purchasing frequency from self-owned websites. Besides, this finding also suggested that members or travelers who had high average purchases in the past tend to buy more air tickets on average after mobile application adoption, while the number of days to book tickets in advance and purchase auxiliary services declined after mobile application adoption. However, males purchased more auxiliary services via mobile applications.

The study is based on the causal effect of mobile application adoption on purchasing behavior. Nevertheless, the theoretical basis remains relatively weak. Furthermore, the underlying mechanisms that cause the changes in purchasing behavior via mobile applications need to be elucidated.

This study enriches the hospitality and tourism literature on mobile application adoptions, multichannel purchasing behavior and revenue management. First, a quasi-experimental design is used to verify a causal relationship between mobile applications’ adoption and travelers’ purchasing behavior in the travel and tourism industry. Second, this study adds to examining travelers’ multichannel purchasing behavior in the travel and tourism industry. Third, this work enriches the current literature that explores auxiliary services and revenue management in the travel industry.

Mobile application adoption significantly impacted the travel and tourism industry. Besides, To the best of the authors’ knowledge, this is one of the first empirical studies that examined changes in purchasing behavior due to mobile application adoption from the perspective of service type. The findings provide the first evidence of the impact of mobile application adoption on service purchasing in the travel industry.

By examining the impact of product innovation on abnormal financial returns following the launch of new products, this study aims to test the explanatory power of a new compound measure of product innovativeness (Ganbaatar and Douglas, 2019).

It is a longitudinal study in which the authors used the compound product innovativeness score (CPIS) for the first time to measure product innovativeness. The abnormal financial returns are estimated through the event study design, where four different models are used. Artificial neural network analysis is done to determine the impact of the CPIS on abnormal returns by utilising a hexic polynomial regression model.

The authors find effect sizes that substantially exceed practically significant levels and that the CPIS explain 65% of the variance in the firm’s abnormal returns in market valuation. Moreover, new-to-the-market novelty predicts 83% of the variation, while new-to-the-firm (catch-up) innovation insignificantly impacts firm value.

This paper demonstrates how the CPIS, an objective and direct measure of product innovativeness, can be used to gain more insight into the innovation effect.

Implications for the business practice of this study include the necessity of relentless innovation by firms in contested differentiated markets, particularly where technological advance is ongoing. Larger and mature firms must practice corporate entrepreneurship to renew their products on a continuous basis to avoid slipping backwards in their markets. Innovation leadership, rather than following the leader, is also important to increase competitive advantage, given the result that innovation followship does not produce abnormal financial returns.

In this study, the authors focused on the effect of product innovativeness on firm performance. While the literature affirms a positive relationship between innovation and firm performance, the effect size of this relationship varies, due largely to the authors contend to simplistic measures of innovativeness. In this study, the authors adopt the relatively novel “compound” measure of product innovativeness (Ganbaatar and Douglas, 2019) to better encapsulate the nuances of both technical novelty and market novelty. This measure of product innovativeness is applicable to firms of all sizes but is more easily applied to entrepreneurial new ventures and SMEs, and it avoids the shortcomings of prior firm-level and subjective measures of innovativeness for both smaller and larger firms. Using a more effective analytical method (Artificial Neural Network), the authors investigated whether there is a “practically” significant effect size due to product innovation, which could be valuable for entrepreneurs in practice. The authors show that the CPIS measure can very effectively explain abnormalities in the stock market, exhibiting a moderate effect size and explaining 65% of the variation in abnormal returns.

This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.

It is important to implement artificial intelligence (AI) because it can simplify and solve complex problems faster than humans. Because AI learns about people and their behavior from the first purchase, AI marketing can boost marketing efforts by leveraging data to target extremely precise consumer groups. There is a debate about the efficacy of AI marketing due to the constraints and limits imposed by the system's nature. This chapter presents insights from published studies regarding the relationship of AI with marketing and how AI can affect marketing. A real-world example of Netflix's usage of AI in marketing has been demonstrated. Then, consumer attitudes regarding AI were revealed. Then, several ethical considerations concerning AI were highlighted. Finally, the anticipated future of AI marketing was addressed. This chapter demonstrated the significance of firms implementing AI marketing to get a competitive advantage. Although some of the difficulties mentioned in this study need to be resolved, AI marketing has a bright future. There are ethical concerns about bias and privacy that should be addressed further. This chapter will encourage firms to use AI systems in marketing, and it will open the door to concerns that will need to be investigated academically in the future.