Search

University-Industry Collaboration (UIC) has been identified as an essential item on Malaysia's agenda for transforming itself into knowledge and innovation-based economy. However, despite the efforts, most initiatives have had limited results. This paper reports on an explorative study that sought to understand the contemporary realities of UIC in the Malaysian context. In particular, the study identified the expectations and impediments to UIC, from the perspectives of three groups of stakeholders -- the academics, the industry players and policymakers. The findings reveal a number of barriers, including cultural differences, perceived lack of academic expertise and reputation, an inadequacy of institutional policies and regulations, lack of trust, issues of intellectual property rights, and the lack of an appropriate reward system. This paper concludes by proposing policy recommendations and strategies that could be used by the government, university, and the industry to promote further and foster university-industry collaborative opportunities and initiatives.

Recognising the significant role of artificial intelligence in the labour market is essential for China to develop sustainably. The research utilises the mixed frequency vector auto-regression (MF-VAR) technique, which would innovatively incorporate data at different frequencies into one model to identify the intricate correlation between the monthly artificial intelligence index (AII) and the quarterly unemployment rate (UR) in China. Through comparison, the MF-VAR method has a more substantial explanatory power than the low-frequency VAR (LF-VAR) model, the impulse responses of the former reveal that AII exerts favourable and adverse influences on UR. Among them, the positive effect occurs on the AII in the first and second months. In contrast, the negative one appears on the AII in the third month, highlighting that artificial intelligence has both stimulating and inhibiting effects on the labour market in China. By analysing UR’s predictive error variance decomposition, the total impact of China’s artificial intelligence technology on employment is a substitution; this outcome is accordant with the theoretical dis¬cussion. In the new round of scientific and technological revolution and industrial transformation, meaningful recommendations for China would be put forward to avert the wave of unemployment brought by the development of artificial intelligence technology. First published online 09 September 2024

Under the dual impact of the COVID-19 pandemic and the Russian-Ukrainian conflict, the excessive stimulation of monetary policy continuously pushes up global inflation (INF). Therefore, this article explores whether Bitcoin can serve as a safe haven for INF. We apply the rolling-window Granger causality test to solve the issue of parameter instability in vector autoregression (VAR) systems and investigate the time-varying interaction between INF and Bitcoin price (BP). The negative influence of INF on BP means a high inflation shock causes BP to decline, indicating that Bitcoin cannot be a safe asset against INF. This is because investors have decreased their willingness to hold Bitcoin under the high INF expectations and cause BP to fall. This finding is not supported by the Intertemporal Capital Asset Pricing Model, emphasising that INF positively impacts BP. Conversely, BP has positive and negative impacts on INF. The positive effect highlights the effectiveness of Bitcoin in predicting INF fluctuations, but economic factors could undermine this effectiveness. In the context of economic stagnation and market turmoil, investors can adjust their portfolio investments based on Bitcoin. The government should utilise the trend of BP to regulate the dynamics of INF to reduce uncertainty in the financial system. First published online 30 August 2024

This article primally explores the short-term fluctuation and long-term implications of the international Bitcoin price (BP) on the Chinese green bond (GB) market, within the sample period of 2014:M10–2023:M07. Bitcoin is the most important cryptocurrency and has a carbon-intensive feature, and its price suffers from great volatility and is closely related to the green finance market. Meanwhile, although China is the largest bitcoin mining state, it is pursuing a dual carbon target, which promotes its green bond market’s development. Thus, it is valuable to investigate the influence of BP on GBs in China. Based on the quantile autoregressive distributed lag approach, this paper indicates that the positive and negative impacts of BP on the GB market are significant in the long-term but not apparent in the short-term. These results emphasize the importance for market participants to obtain a better understanding of how BP affects GB under various market circumstances. Implementing specific policies, such as regulatory mechanisms for Bitcoin trade, market-oriented reform for the bond market, and information disclosure, can alleviate shocks from BP and accelerate the development of the GB market. First published online 20 May 2024

Quantum computing is emerging as a groundbreaking force, promising to redefine the boundaries of technology and business. This paper provides an in-depth examination of the quantum realm, beginning with its fundamental principles and extending to its implications for today’s industries. We discuss how quantum algorithms threaten existing cryptographic measures while also uncovering vast opportunities in sectors like finance, healthcare, and logistics. The narrative then shifts to the evolution of new business models, exemplified by Quantum-as-a-Service (QaaS) and enhanced AI capabilities. Alongside the myriad opportunities, we address the challenges and ethical concerns surrounding the swift rise of quantum technologies. By emphasizing the importance of collaborative efforts among businesses, policymakers, and technologists, the article advocates for a balanced and responsible approach to quantum adoption. Through this analytical lens, the article paints a comprehensive picture of the impending quantum era, presenting both its transformative potential and the complexities it brings to our interconnected world.

Studies on the Chinese government’s online response present diverse voices and perspectives, but consensus cannot be reached. The literature has constantly ignored the construction of an online media mechanism, which includes professional positions and standardized procedures that were designed inside the online media to process information and facilitate an effective government response. Does the reconstruction of an online media mechanism benefit the effectiveness of government response? If so, how? From the perspective of the media mechanism, what are the influential factors for the effectiveness of government response and how do they work to exert this impact? Engaging with the media system dependence theory, I conducted semi-structured, in-depth interviews with the editor at the Beijing Municipal Government Website and official document drafters at prestigious research institutions. In conversation with the political system theory, multinomial logistic regression was used to analyze the daily government response data covering the period from 2019 to 2020. This article argues that the innovations in the mechanism inside the online media system can outsource some information management functions from the political system to the online media system. These innovations also assist in partial power transfer from the political system to the media system to achieve an effective government response. The political system faces dual pressure, including content pressure and time pressure, regarding its online response.

Understanding how bullying leads to eating disorders can help develop effective intervention strategies. This phenomenological study examines the relationship between bullying and coping strategies Malaysian adolescents diagnosed with eating disorders use. Using the snowball sampling technique, 12 Kuala Lumpur participants, six females and six males aged 10 to 19, responded to interview questions based on the literature reviews, theories, and a published questionnaire. By using thematic analysis, the researcher found out that all 12 participants with EDs were bullied about their weight or size. Most of the bullying and teasing were verbal or via online platforms. The findings suggest that bullying can be a significant cause of the development of eating disorders among adolescents in Malaysia. Proper coping strategies can be essential in overcoming the trauma of being bullied and treating eating disorders.

The research presented in this paper delves into the fascinating realm of how temperature, both physical and psychological, can influence human behavior, specifically in the context of charitable donations. It offers valuable insights into the intricate relationship between our sensory experiences and our motivations to engage in prosocial acts. Throughout the four comprehensive studies conducted, the authors shed light on the intricate dance between temperature and our innate need for social warmth and connection. They explore how feeling physically cold or being primed with cold imagery can trigger a desire for social affiliation and empathy, ultimately leading individuals to engage in charitable acts, such as donations. The studies provide robust evidence that when individuals experience the discomfort of cold, whether through physical touch or psychological priming, they are more inclined to seek remedies to rectify their feelings of loneliness and social isolation. This leads to a heightened desire for social connectedness, which, in turn, fosters increased levels of empathy and a greater likelihood of making charitable donations. In essence, the act of donation becomes a compensatory mechanism to counterbalance the perceived social deficit caused by feeling cold. Moreover, the research extends beyond the laboratory setting by conducting a field study in a coffee shop, demonstrating the generalizability of the findings. It reveals that the link between temperature, empathy, and charitable acts holds true in real-world scenarios. These findings offer valuable implications for social marketing and public service announcement campaigns, suggesting that the timing and context of these messages should be carefully considered. In conclusion, this research provides a deeper understanding of the complex interplay between temperature, social warmth, and charitable behavior. It highlights the significance of considering individuals' psychological states when designing campaigns or interventions aimed at promoting prosocial actions. Ultimately, it emphasizes the power of empathy and social connectedness in driving positive social change.

Purpose: Big data analytics is a recently offered state of the art technology with potential business influence. Nevertheless, the roadmap for the effective implementation of its also the street to exploit its essential value remains unclear. This specific analysis seeks to create a much greater understanding of the enablers facilitating BDA implementation in the banking, in addition to the financial service part, from the view of interdependencies and interrelations. Design/Methodology/Approach: We use an integrated approach that incorporates Delphi assessment, interpretive structural modeling, and fuzzy MICMAC technique to identify the interactions between enablers, which determine the accomplishments of BDA implementation. Our incorporated method uses experts' domain understanding and gains a novel insight into the underlying causal relations about enablers, linguistic evaluation of the mutual impacts among variables, and also like two revolutionary techniques for visualizing the results. Findings: Our findings highlight the main key role of enabling components, including technical and skilled workforce, infrastructure readiness, financial assistance, and also selecting good main details strategies. These elements have considerable driving impacts on other enablers in a hierarchical style. The outcomes provide reliable, robust and easy insights into the qualities of BDA implementation in banking and financial programs as a whole system, while demonstrating attainable influences of all interconnected crucial components. Originality/Value: This analysis explores the main key enablers for good BDA implementation in the banking and financial service sector. Much more enough, it reveals the interrelationships of components by calculating operating as well as dependence degrees. This particular exploration provides managers with a clear strategic path toward effective BDA implementation.

This exploration employs the generalized supremum augmented Dickey-Fuller (GSADF) approach to explore whether there are digital tech bubbles in China. The empirical results suggest the existence of multiple digital tech bubbles, which are mostly accompanied by an excessive rise. However, the appearance of digital tech bubbles is curbed since 2016, mainly due to the increasing mature regulations in relevant fields. Besides, bubbles in different digital technologies are similar during the same period, which could be attributed to the close relationships among them. Additionally, we further investigate the factors influencing the explosive behaviours, and find that the Chinese stock market positively affects digital tech bubbles, while economic policy uncertainties and situations negatively influence such explosive behaviors. In the context of the new round of scientific and technological revolution and industrial transformation, these conclusions provide valuable implications to achieve the target of constructing a “Digital China” by becoming moderately cautious about potential bubbles in the digital tech industry. First published online 11 October 2023

Purpose: The retail banking market has witnessed substantial changes over the years due to new technological advancements, increased regulations, and the rise of digital channels. Meeting customer expectations stands out as a key challenge for sustainable growth of the industry. This examines the impact of service quality, customer knowledge management, and confirmation on customer satisfaction within the domestic retail banking sector. Furthermore, it seeks to explore the association between customer satisfaction and customer loyalty in retail banking. Design/Methodology/Approach: Self-administered questionnaires were distributed to retail banking customers in the Klang Valley using convenience and purposive sampling techniques. A total of 268 samples were obtained for data analysis with the Structural Equation Modelling (SEM) and Necessary Condition Analysis (NCA). Findings: The study’s results validated the strong connection between service quality, customer knowledge management, and confirmation with customer satisfaction in Malaysian retail banks. Additionally, customer satisfaction has significant impact on customer loyalty. The NCA outcomes further substantiate these findings, highlighting the significant role of customer knowledge management, service quality, and confirmation as significant factors affecting customer satisfaction in Malaysian retail banks. Implications/Originality/Value: This study offers valuable insights for academia to better understand this area as well as allows sector stakeholders to improve and sharpen their competitive edge in the market.

The development and availability of information technology and the possibility of deep integration of internal IT systems with external ones gives a powerful opportunity to analyze data online based on external data providers. Recently, machine learning algorithms play a significant role in predicting different processes. This research aims to apply several machine learning algorithms to predict high frequent daily hotel occupancy at a Chinese hotel. Five machine learning models (bagged CART, bagged MARS, XGBoost, random forest, SVM) were optimized and applied for predicting occupancy. All models are compared using different model accuracy measures and with an ARDL model chosen as a benchmark for comparison. It was found that the bagged CART model showed the most relevant results (R2 > 0.50) in all periods, but the model could not beat the traditional ARDL model. Thus, despite the original use of machine learning algorithms in solving regression tasks, the models used in this research could have been more effective than the benchmark model. In addition, the variables’ importance was used to check the hypothesis that the Baidu search index and its components can be used in machine learning models to predict hotel occupancy.

In the e-commerce environment, it is very common for consumers to select goods or services based on online reviews from social platforms. However, the behavior of some unscrupulous merchants who hire a “water army” to brush up on reviews of their products has been continuously exposed, which seriously misleads consumers’ purchasing decisions and undermines consumer trust. Until now, it has been a challenging task to accurately detect the “water army”, who could easily alter their behaviors or writing styles. The focus of this paper is on some collusive clues between members of the same social platform to propose a new graph model to detect the “water army”. First is the extraction of six kinds of user collusive relationships from two aspects: user content and user behavior. Further, the use of three aggregation methods on such collusive relationships generates a user collusive relationship factor (CRF), which is then used as the edge weight value in our graph-based water army detection model. In the combination of the graph grouping method and evaluation rules on candidate subgraphs, the graph model effectively detects multiple collusive groups automatically. The experimental results based on the Mafengwo platform show that the CRF generated from the coefficient of variation (CV) method demonstrates the best performance in detecting collusive groups, which provides some practical reference for the detection of “water armies” in an e-commerce environment.

Today, wearable health products play a crucial role in most locations, such as constant wellness monitoring of people, street traffic management, weather forecasting, along with smart house. These sensor devices constantly generate massive amounts of data and are kept in cloud computing. This particular chapter proposes Internet of Things design to store and system scalable sensor information for healthcare apps. Proposed architecture comprises 2 primary architecture, specifically, MetaFog-Redirection and Choosing and Grouping architecture. Though cloud computing offers scalable data storage, effective computing platforms must process it. There's a requirement for scalable algorithms to process the big sensor information and recognize the helpful patterns. To conquer this problem, this particular chapter proposes a scalable MapReduce based logistic regression to process such massive quantities of sensor information. Apache Mahout includes scalable logistic regression to system BDA in a distributed way. This particular chapter uses Apache Mahout with Hadoop Distributed File System to process the sensor information produced by the wearable health units.

This research investigates insight into how leaders could lead the rising waters of the Covid-19 pandemic with agility and compassion. Based on pertinent literature, leaders must redefine their leadership mindset to harness team behavioural change toward team dynamics in organizational resilience. Through phenomenological research of eight companies’ leaders, a proposed model was presented to define the behavioural shift that facilitates leaders in orientating team mindset while embracing compassion. The next normal needs of the organization on the change of leaders’ behaviour, implying that building unique and pleasing agile and compassionate practices would lead to an improved organizational team workplace. This focuses on leaders’ efforts to empower teams through a cohesive concept to enable long-term team success. The research contributes to the body of knowledge through its constructivist approach and identification of underlying team dynamics pivotal for organizational resilience.

This article begins with a brief overview of the Cambodian Economic Development and offers a brief history of the insurance market industry. The article continues with a more detailed discussion about insurance law and a critical look at the current insurance contract law in Cambodia that has yet to establish a sufficient legal framework for its insurance industry’s development. The Insurance Law of Cambodia, particularly the insurance contract law, the definition of insurable interest, the principle of duty of good faith, and its remedies are in flawed condition. A suggestion for future legislation reform has then made in this article. Consequently, the discussion and comparison of the current status of Cambodian insurance law with the well-developed insurance law in common law system will be taken part as a route reference for future reform in Cambodia.

In this study, the researchers try to identify the factors that encourage/prohibit them to reason and behave ethically in order to derive a prescriptive guidance that can enhance the process, while practicing managers to develop policies and programs that strengthened employees’ ethical resolve on the job. In this study, Bratman’ Belief-Desire-Intention (BDI) model used to explain the research framework, the ethical decision intention component (ethical awareness, ethical judgment and ethical intention) are examined how their relationship towards each other. The researchers has employed survey-based approach to meet the study’s objectives and self-administrated questionnaires were used to gather data from 120 first line managers who work in 4-star and 5-star hotels in Malaysia respectively. These significance findings make a unique contribution towards the development of moral theories, as the researchers will not perceive them as individual but a comprehensive process to aware, evaluate and intend to act morally right. Through the findings of this study, it urges organizations to emphasize the importance of the ethical dilemma in their professional education at both the initial stage and through ongoing professional development.

Purpose – As the world grapples with the pervasive effects of the coronavirus pandemic, a notable disconnect has emerged in the public's understanding of scientific and medical research. Particularly, the travel industry has become unquestionably vulnerable amid the COVID-19 outbreak; this pandemic has interrupted the industry's operations with devastating economic consequences. This paper aims to highlight the importance of deconstructing barriers between medical science and public awareness related to COVID-19, taking tourism as a case in point. It also discusses the role of interdisciplinary research in facilitating the tourism and hospitality industry's recovery and alleviating tourists' uncertainties in the wake of COVID-19. Design/methodology/approach – This paper offers a synthesis of news coverage from several media outlets, framed within the literature on knowledge transformation across disciplines. This framing focuses on the medical sciences (e.g. public health) and social sciences (e.g. tourism management) to identify gaps between medical scientific knowledge and public awareness in the context of COVID-19. The authors' experience in public health and tourism management further demonstrates a missing link between academic research and the information made available in public health and everyday settings. A potential research agenda is proposed accordingly. Findings – This paper summarizes how salient issues related to knowledge transfer can become intensified during a global pandemic, such as medical research not being communicated in plain language, which leads some citizens to feel apathetic about findings. Reporting on the prevalence and anticipated consequences of disease outbreaks can hence be difficult, especially early in the development of diseases such as COVID-19. Research limitations/implications – By assuming a cross-disciplinary perspective on medical/health and social science research, this paper encourages academic and practical collaboration to bring medical research to the masses. This paper also outlines several research directions to promote public health, safety and sustainability through tourism. Practical implications – This paper highlights that it is essential for medical knowledge to be disseminated in a manner that promotes public understanding. The tourism and hospitality industry can benefit from an essential understanding of medical findings, particularly during this pandemic. Without a firm grasp on COVID-19's origins and treatment, the tourism and hospitality industry will likely struggle to recover from this catastrophe. Social implications – Taking COVID-19 as a case in point, this study advocates leveraging the strengths of disparate domains to bring medical findings to a wider audience and showcase cutting-edge developments for the greater good. This study also emphasizes the importance of engaging the general public in reputable scientific research findings to increase public awareness in a professional and accurate manner. Originality/value – This paper presents a unique and critical discussion of the gap between medical science knowledge and public awareness, as well as its implications for tourism and hospitality recovery after COVID-19, with a focus on applying medical scientific knowledge to post-pandemic industry recovery. 研究目的 – 當全球正與冠狀病毒流行病所帶來的廣泛影響抗衡之際，公眾對科學和醫學研究的理解卻出現了一個令人關注的脫節現象。在2019冠狀病毒病爆發期間，旅遊業無疑格外受到影響。這流行病中斷了旅遊業的運作，給業界帶來毀滅性的經濟影響。本文旨在以旅遊業為一個適時的例子、強調我們必須剖析與2019冠狀病毒病相關的醫學與公共意識之間的障礙。本文亦討論跨學科研究對促進2019冠狀病毒病疫情後旅遊及酒店業的復甦、及減低旅客的不確定性所扮演的角色。 研究設計/方法/理念 – 本文提供一個來自數個媒體管道的新聞報導的綜合論述，而這論述是在與跨學科知識轉化相關的文獻領域內陳述而成的。論述的重點放在醫學科學(例如公共衛生) 和社會科學(例如觀光管理) 上，目的是確定在2019冠狀病毒病的背景下、醫學知識與公共意識之間的差距。作者們在公共衛生及觀光管理方面的經歷進一步顯示學術研究、與公共衛生和日常環境上的公開資訊之間缺乏銜接，本文就此提出一個研究議程。 研究結果 – 本文總結與知識轉化相關的顯著問題如何會在全球流行病爆發期間變得更嚴重。譬如、醫學研究的資訊會因沒有以通俗語言來傳遞而令有些國民對其研究結果漠不關心。因此，關於疾病的傳播和爆發所預期的影響的信息是難於廣傳的，特別是在像2019冠狀病毒病等疾病形成的初期。 原創性 – 本文就醫學知識與公共意識之間的差距作出了一個獨特、有批判性的討論，亦論述這差距對2019冠狀病毒病疫情過後旅遊及酒店業復甦的意義。討論的焦點放在如何應用醫學知識在疫情後幫助產業復甦上。 研究的局限/含意 – 作者們採用涵蓋醫學/衛生和社會科學研究的跨學科角度，鼓勵學者與從事實務人員相互合作、以便把醫學研究帶給廣泛的民眾。本文亦概述多個研究方向，透過旅遊業來推動公共衛生、公眾安全及可持續性。 實際意義 – 本文強調醫學知識必須透過能提高公共意識的方式來傳遞。旅遊及酒店業會因我們對醫學研究結果擁有必要的認識而受惠，尤其是在這流行病蔓延的期間。若我們對2019冠狀病毒病的病源和療法沒有確切的了解，則旅遊及酒店業將難從這災難中恢復過來。 對社會的意義 – 本研究利用2019冠狀病毒病這個適時的例子，來提倡借助各個不同領域的優點，為了謀求公眾的利益、把醫學的研究結果帶給更多民眾、及傳達醫療最新發展的信息。本研究亦強調以專業、精準的方法，引起公眾對可信賴的科學研究結果產生興趣，從而提高公共意識至為重要。

This paper probes the interrelationship between Bitcoin price (BP) and the U.S. partisan conflict (PC) by performing the bootstrap full- and sub-sample Granger causality tests. The positive influence from PC to BP reveals that Bitcoin can be considered as a tool to avoid the uncertainty caused by the rise in PC. However, this view cannot be supported by the negative impact, the major reason is that the burst of bubble undermines the hedging ability of Bitcoin. The above results are inconsistent with the intertemporal capital asset pricing model (ICAPM), underlining that high PC may drive BP to rise, in order to compensate for the losses and costs from factionalism. Conversely, BP has a negative impact on PC, suggesting that the U.S. political situation can be reflected by the Bitcoin market. Under the circumstance of the fiercer factionalism in the U.S., this investigation can benefit investors and related authorities. First published online 04 February 2021

With the development of e-commerce, an increasing number of online reviews can serve as a promising data source for enterprises to improve online products. This paper proposes a method for modelling consumer satisfaction based on online reviews using the improved Kano model from the perspective of risk attitude and aspiration. Firstly, the attributes concerned by consumers are extracted from online reviews, and sentiment analysis of the extracted attributes is carried out using Standford CoreNLP. Secondly, to identify the types of product attributes, an improved Kano model is proposed based on the effects of product attributes on consumer total utility. On this basis, different attribute types are illustrated from the perspective of risk attitude. Then, the consumer aspirations are mined based on the risk attitudes of different attributes and the attribute impact on consumer satisfaction. According to the risk attitudes and aspirations of different attributes, the quantified satisfaction functions are constructed to provide more objective and accurate improvement suggestions. Finally, the proposed method is applied to the hotel service improvement to illustrate the effectiveness. First published online 13 April 2021

Electronic marketing (eM) is a flourishing phenomenon that is gaining intense concern because of a significant impact on organizational performance. Over the past few decades, the relevance of eM has been observed in numerous fields (e.g., consumers, organizational strategy, advertisement, and overall philosophy of management to understand the insights globally). To effectively maneuver the field, all stakeholders, particularly academicians and practitioners, must comprehend the current position of the eM theory and practices for dynamic utilization. A systematic bibliometric analysis can serve this issue by providing a holistic view of the publication trend and its trajectory in terms of various themes, including citations and publication metrics. This study analyzes the bibliometric data from 2000 to 2019 to reveal the most productive countries, universities, authors, journals, and prolific publications in electronic marketing. To this end, VOS viewer software was used to visualize the mapping based on co-citation, bibliographic coupling (BC), and co-occurrence (CC). The primary addition of this research is to provide an overview of eM tendencies and paths that may help researchers know the tendencies and future research directions worldwide.

In order to accurately describe and explore the relationship characteristics between the faults of the numerical control equipment and identify weak links in reliability, a new method for associated fault analysis of CNC equipment based on social network analysis (SNA) are proposed. First, an associated fault directed graph is established based on the statistical analysis on the associated faults of CNC equipment; then, a social network analysis method is introduced in consideration of the network attribute, the relationship between faults and the key subsystem affecting the reliability of CNC equipment are analyzed from three levels of association analysis, element analysis and matrix analysis, and then weak links in reliability of CNC equipment are identified; and finally, a series of fault records of a machining center production site are used as research objects for empirical research ,thereby verifying the scientific validity of the method. Abbreviations: CNC: Computer Numerical Control; FMECA: Failure Mode Effect and Criticality Analysis; SNA: Social Network Analysis; LAN: Local Area Network

Online product reviews play important roles in the word-of-mouth marketing of e-commerce enterprises, but only helpful reviews actually influence customers’ purchase decisions. Current research focuses on how to predict the helpfulness of a review but lacks a thorough analysis of why it is helpful. In this paper, feature sets covering review text and context cues are firstly proposed to represent review helpfulness. Then, a set of gradient boosted trees (GBT) models is introduced, and the optimal one, which as implemented in eXtreme Gradient Boosting (XGBoost), is chosen to predict and explain review helpfulness. Specially, by including the SHAP (Shapley) values method to quantify feature contribution, this paper presents an integrated framework to better interpret why a review is helpful at both the macro and micro levels. Based on real data from Amazon.cn, this paper reveals that the number of words contributes the most to the helpfulness of reviews on headsets and is interactively influenced by features like the number of sentences or feature frequency, while feature frequency contributes the most to the helpfulness of facial cleanser reviews and is interactively influenced by the number of adjectives used in the review or the review’s entropy. Both datasets show that individual feature contributions vary from review to review, and individual joint contributions gradually decrease with the increase of feature values.

This paper estimates the social welfare effect of China’s largest online ride-sharing platform. Under the plausible assumption that consumers would change from traditional transportation to online ride-sharing when the marginal benefit of saved time outgrows the additional cost, we calculate the distribution of implied wage rate of passengers. We then use the passenger wage rate to calculate the social welfare generated by the decrease in waiting time and the reduction of waiting uncertainty brought about by the ride-sharing platform. Our estimate suggests that the ride-sharing platform created a total of 130.5 billion Yuan of social welfare in the three years between 2016 and 2018, and the consumer surplus and producer surplus created by an average transaction are 5.4 Yuan and 2.5 Yuan, respectively. The robustness test finds that our results were insensitive to the assumed risk aversion coefficient in the model, the subsample number used for each city, and the inclusion of nonlinear terms in the model. Alternative hypotheses, such as learning effect, seem unable to explain our result. First published online 02 February 2022

Ideally, when we think about the law, we ideally view it as just, fair, and equal. However, the harsh reality of legislation is that it has also been used as [...]

It's a tale as old as Canada itself: contested First Nations land rights, a lucrative land development, escalating tensions leading to excessive force and damaged relations. For those who know [...]

The road to establishing democracy in Canada has been a long one, with the first instances of popular elections taking place in what was then the colony of New France, [...]

Research purpose. The job types might be potential determinants of subjective well-being (SWB), which is seldom investigated in the current literature. This article examines this relationship by collecting data from the Chinese Household Income Project in 2013 to explore this relationship.

ABSTRACT A major experimental drought research project entitled 'Mechanisms and Early Warning of Drought Disasters over Northern China' (DroughtEX_China) was launched by the Ministry of Science and Technology of China [...]