Aaltodoc Repository :: Browsing by Author "Zhou, Xun"

Browsing by Author "Zhou, Xun"

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Bedtime smartphone use and academic performance: A longitudinal analysis from the stressor-strain-outcome perspective
(Elsevier, 2022-12-01) Lin, Yanqing; Zhou, Xun; Department of Information and Service Management; School Common, BIZ
The penetration of smartphones into human life finds expression in problematic smartphone use, particularly under the Covid-19 home confinement. Problematic smartphone use is accompanied by adverse impacts on personal wellbeing and individual performance. However, little is known about the mechanism of such adverse impacts. Motivated by this, the present study strives to answer (i) how bedtime smartphone use impacts students’ academic performance through wellbeing-related strains; (ii) how to mitigate the adverse consequences of bedtime smartphone use. Drawing upon the stressor-strain-outcome paradigm, the current work presents a comprehensive understanding of how smartphone use indirectly deteriorates college students’ academic performance through the mediators of nomophobia — “the fear of being unavailable to mobile phones” (Lin et al., 2021) — and sleep deprivation. This allows a more flexible remedy to alleviate the adverse consequences of smartphone use instead of simply limiting using smartphones. This study collects a two-year longitudinal dataset of 6093 college students and employs the structural equation modeling technique to examine the stressor‐strain‐outcome relationship among bedtime smartphone use, nomophobia, sleep deprivation, and academic performance. This study finds robust evidence that wellbeing-related strains (i.e., nomophobia and sleep deprivation) mediate the negative relationship between bedtime smartphone use and academic performance. Furthermore, engaging in physical activity effectively mitigates the adverse effects of bedtime smartphone use upon nomophobia and sleep deprivation. This study not only enriches the current literature regarding the indirect effect mechanism of smartphone use but also provides valuable insights for academics and educational policymakers.
Critical Success Factors for A/B Testing in Online Marketing
(2019) Lonkila, Elja; Zhou, Xun; Tieto- ja palvelujohtamisen laitos; Kauppakorkeakoulu; School of Business
The increase of data-driven decision making in digital-facing organisations in the 2010’s has brought methodologies such as A/B testing into the toolbox of online marketers. A/B testing in particular has become an essential part of the design process for advertisements, websites, and any user-facing interfaces. This thesis aims to form a critical appraisal of A/B testing as a method by conducting a systematic literature review on how much the topic has been studied before. In addition, this thesis identifies the common pitfalls seen in implementation of A/B tests. The motivation to form a critical look into the subject rises from the rapid growth of the popularity of A/B testing. As the amount of companies utilising the methodology rises, it is important to review the topic to identify current best practices and possible deficiencies in research.
Enhancing Lean Manufacturing Utilizing Industry 4.0 Technologies: A Focus on Cyber-Physical Systems
(2019) Repo, Vilko; Zhou, Xun; Tieto- ja palvelujohtamisen laitos; Kauppakorkeakoulu; School of Business
The industry is a significant part of our economy, providing our material goods in a highly mechanized and automatized way. The industry has gone through three acknowledged revolutions, and now the industry is on the verge of the fourth industrial revolution as known as Industry 4.0. Industry 4.0 surfaced in the 21st century and has gained interest exponentially after 2016. However, the research on this topic is still in its early stages. Thus, this has been taken into account as the research is conducted on a more specific topic. Industry 4.0 composes of many different technologies, from which the main focus of this thesis' is on Cyber-Physical Systems and what they might offer to Lean Manufacturing. The aim of this thesis is also to provide basic knowledge of lean and Industry 4.0 in general so that it allows more profound exploring of Cyber-Physical Systems and helps to perceive the possibilities more extensively. This thesis also presents significant concerns regarding the use of Cyber-Physical Systems. Based on these topics, the conclusion can be formed to answer what possibilities Industry 4.0 offers. The main findings from this literature review prove that Industry 4.0 and Cyber-Physical Systems can offer a great variety of possibilities to Lean Manufacturing. As it is debated that Lean Manufacturing, as is, is reaching its limits, implementing Industry 4.0 technologies are becoming crucial to maintain competitive capability. Cyber-Physical Systems could offer enhancement through streamlining the manufacturing process, reducing waste and workload, and better deficit and abnormality detection, for example. As a contribution, this thesis provides information in a chronological order to provide extensive enough understanding and an answer to the research question.
How do customers respond to robotic service? A scenario-based study from the perspective of uncertainty reduction theory
(2021) Lin, Yanqing; Zhou, Xun; Fan, Wenjie; Department of Information and Service Management; School Common, BIZ; Bui, Tung X.
Confronted with an increasing popularization and advancement of applying artificial intelligence in robotic technology, practitioners in the service sector have been increasingly deploying service robots in their operations. Motivated by a paucity of knowledge on how consumers would respond to the robotic service, this study establishes on the uncertainty reduction theory to advance a research model that seeks to unveil how both customer trait and service characteristic affect customers' revisit intention to robotic service via perceived risk. Based on a scenario-based experiment with 190 responses in the hotel reception service context, our results reveal that perceived risk partially mediates the relationship between personal innovativeness and service revisit intention, so does between service heterogeneity and revisit intention. Furthermore, the service context, i.e., whether the prior service experience satisfies the customer, can moderate the relationship between personal innovativeness (service heterogeneity) and perceived risk. This study also draws related theoretical and practical implications.
How much climate policy has cost for OECD countries?
(Elsevier BV, 2020-01) Kuosmanen, Timo; Zhou, Xun; Dai, Sheng; Department of Information and Service Management; School Services, BIZ
High economic cost of climate policy has attracted critical debate since the Kyoto Protocol. However, reliable empirical evidence of the abatement cost of green-house gases across countries remains scant. In this study we estimate the average yearly green-house gas abatement costs per capita for a panel of 28 OECD countries in years 1990–2015. The marginal abatement costs are estimated using a novel data-driven approach based on convex quantile regression. Compared to traditional frontier estimation methods, the quantile approach takes into account a broader set of abatement options and is more robust to inefficiency, noise, and heteroscedasticity in empirical data. The comparison of OECD countries shows that the actual abatement cost per capita has been very modest, much lower than predicted in the late 1990s. This result has profound policy implications, calling for more ambitious climate change mitigation strategy in the future.
Is winning the draft lottery a viable solution to subpar attendance in the NHL?
(2019) Kuure, Otto; Zhou, Xun; Tieto- ja palvelujohtamisen laitos; Kauppakorkeakoulu; School of Business
Low-Carbon Transition: How Successful and at What Cost?
(Aalto University, 2019) Zhou, Xun; Kuosmanen, Timo, Prof., Aalto University, Department of Information and Service Economy, Finland; Tieto ja palvelujohtamisen laitos; Department of Information and Service Management; Kauppakorkeakoulu; School of Business; Kuosmanen, Timo, Prof., Aalto University, Department of Information and Service Economy, Finland
Global climate change is widely recognized as the most severe threat facing humankind. Combating the climate threat depends critically on the transition from unsustainable high-carbon economic growth to sustainable low-carbon development. This dissertation presents three scientific essays that endeavor to provide methodological innovations for evaluating and monitoring the low-carbon transition. The first essay focuses on the evaluation of the marginal abatement costs for greenhouse gases, which is fundamental to effective climate policy. The motivation of this essay stems from the observation that most empirical studies using frontier estimation methods grossly over-estimate the marginal abatement costs due to three sources of upward bias from ignoring input-side abatement options, inefficiency, and noisy data. To address the overestimation issue, we clarify the conceptual distinction between the shadow price and marginal abatement cost and develop a novel data-driven estimation approach based on convex quantile regression. Compared to the traditional approaches, convex quantile regression is more robust to random noise, the choice of the direction vector, and heteroscedasticity. An empirical application to U.S. electric power plants provides empirical evidence and demonstrates the advantages of the proposed approach. The second essay is motivated by a surprising paucity of well-developed studies investigating environmental productivity growth in consumption (a necessary condition for a smooth transition toward low-carbon consumption). This essay seeks to develop an environmental total-factor productivity index specific to the use of consumer durable goods. We first analyze the particular features of consumer durables (taking the passenger car as an example) compared to conventional production units, based on which we elaborate how to model the production activity during the use phase of consumer durables. Then, we present an overview of the existing approaches to measuring environmental productivity change and describe how they can be applied in the current context. Finally, we use a unique data set that covers all registered passenger cars in Finland to illustrate the interpretation of the proposed index. The third essay extends the second by further examining the driving factors behind decreasing CO2 emissions of new passenger cars. This essay develops a novel decomposition method to break down the change in the average CO2 emissions of new passenger cars into underlying factors such as technology, efficiency, and policy measures. Our decomposition draws insights from the traditional index decomposition analysis and frontier-based decomposition of productivity growth and satisfies several desirable properties. An empirical application to the Finnish register data sheds light on why and how the CO2 emissions of new cars decreased from year 2002 to 2014.
Performance and wages: Empirical research from the English Premier League
(2019) Sauramaa, Matias; Zhou, Xun; Tieto- ja palvelujohtamisen laitos; Kauppakorkeakoulu; School of Business
Revenue generating in the NHL: Multiple regression analysis on generating revenue in the National Hockey League
(2019) Mattinen, Onni; Zhou, Xun; Tieto- ja palvelujohtamisen laitos; Kauppakorkeakoulu; School of Business
This thesis aims to explain the differences between different National Hockey League teams generated revenues. This is done by analyzing the previous researches on different major sports leagues in the Northern America and comparing them with the findings from the NHL. By the data available, different regression models are constructed and further evaluated to analyze the different factors affecting revenue generating in the NHL. Scope of this research is from season 2005/2006 to season 2014/2015. From recent seasons, only a relatively small amount of data was available, thus the season 2014/2015 is the latest season of interest in this research. The data used to build the regression models was first collected in Excel, and then imported to Stata, which was used to build the models. After testing and analysis, a fixed effects model proved out to be the preferred model. Further analysis of the results pointed out the importance of competitive balance in the league to maximize the profitability between teams. In addition, the geographical features of a team, such as population in the area a team is located, proved out to be important when analyzing the generated revenues. Compared with the previous researches, the results had similarities, but as expected, some differences appeared as well. The most noticeable difference from the previous researches was that the regular season success of a team did not appear as a significant factor in any of the models presented. In addition, some earlier studies suggested that pricing of the tickets would be a significant factor in generating revenue, but surprisingly models used in this research did not provoke similar results. Overall, the results indicate that modeling the revenue generating of a NHL team is complicated, but definitely achievable. Further research on the topic should be done, as the financial side of the NHL still remains underexplored.
Shadow Prices and Marginal Abatement Costs: Convex Quantile Regression Approach
(Elsevier Science B.V., 2021-03-01) Kuosmanen, Timo; Zhou, Xun; Department of Information and Service Management
Marginal abatement cost (MAC) is a critically important concept for efficient environmental policy and management. In this paper we argue that most empirical studies using frontier estimation methods such as data envelopment analysis (DEA) over-estimate MACs. The first methodological contribution of this paper is to clarify the conceptual distinction between the shadow price and MAC in order to analyze three sources of upward bias due to the limited set of abatement options, inefficiency, and noisy data. Our second methodological contribution is to develop a novel MAC estimation approach based on convex quantile regression. Compared to the traditional methods, convex quantile regression is more robust to the choice of the direction vector, random noise, and heteroscedasticity. Empirical application to the U.S. electric power plants demonstrates that the upward bias of DEA may be a serious problem in real-world applications.
What Drives Decarbonization of New Passenger Cars?
(Elsevier Science B.V., 2020-08-01) Zhou, Xun; Kuosmanen, Timo; Department of Information and Service Management
Transition towards a low-carbon transport sector fundamentally depends on decarbonization of the passenger car fleet. Therefore, it is critically important to understand the driving factors behind decreasing CO2 emissions of new passenger cars. This paper develops a new decomposition method to break down the change in the average CO2 emissions of new passenger cars into components representing changes in available technology, carbon efficiency of consumer choices, vehicle attributes, fuel mix, and the gap between type-approval and on-road CO2 emissions of passenger cars. Our decomposition draws insights from the traditional index decomposition analysis and frontier-based decomposition of productivity growth. It satisfies such desirable properties as factor reversal, time reversal, and zero-value robustness. An empirical application to a unique data set that covers all registered passenger cars in Finland sheds light on why and how the CO2 emissions of new cars decreased from year 2002 to year 2014.

Browsing by Author "Zhou, Xun"

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