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With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions.

The authors collected a large-scale Kindle Books data set containing book descriptions and ratings, and calculated whether a specific book will receive a high rating. For this purpose, several feature representation methods (bag-of-words, term frequency–inverse document frequency [TF-IDF] and Word2vec) and machine learning classifiers (logistic regression, random forest, naive Bayes and support vector machine) were used.

The used classifiers show substantial accuracy in predicting reader satisfaction. Among them, the random forest classifier combined with the TF-IDF feature representation method exhibited the highest accuracy at 96.09%.

This study revealed that user satisfaction can be predicted based on book descriptions and shed light on the limitations of existing recommendation systems. Further, both practical and theoretical implications have been discussed.

Understanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of this study is to investigate customers' reviews on an online hotel reservation platform, and explores their postbehaviors from their reviews.

The authors employ two different approaches and compare the accuracy of predicting customers' post behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit the hotel again (referred to as Prediction), while the second subsection examines whether participants want to visit one of the particular hotels when they read other customers' reviews (dubbed as Decision).

The accuracy of the machine learning approaches (73.23%) is higher than that of the experimental approach (Prediction: 58.96% and Decision: 64.79%). The key reasons of users' predictions and decisions are identified through qualitative analyses.

The findings reveal that using machine learning approaches show the higher accuracy of predicting customers' repeat visits only based on employed sentimental features. With the novel approach of integrating customers' decision processes and machine learning classifiers, the authors provide valuable insights for researchers and providers of hospitality services.

The purpose of this paper is to examine the roots of police corruption, evaluate the effectiveness of anti-corruption measures, and suggest recommendations to prevent and control police corruption in South Korea.

This paper adopts a qualitative approach to identify the key causes and to assess the reform agenda of police corruption in South Korea. In doing so, it introduces a brief history and profile of the police force, explores changes in police corruption, identifies the roots of police corruption, assesses the effectiveness of anti-corruption measures and offers policy recommendations to curb corruption in the South Korean police.

This paper claims that conventional and current anti-corruption measures have not been effective in minimizing police corruption in South Korea. It identifies the scope of police work and duties without proper accountability, a code of silence in police organizations, the low ethical standards of police officers and weak punitive measures against corrupt police officers as the main causes of corruption. Strenuous reform efforts that directly target these causes are needed to reduce corruption in the South Korean police.

This paper will be a useful reference for readers who are interested in why corruption has not been effectively prevented and controlled in the South Korean police.

The purpose of this paper is to understand how the characteristics of low-volume manufacturing industries influence the product introduction process and factors which can facilitate that process in low-volume manufacturing industries.

A literature review and a multiple-case study were used to achieve the purpose of the paper. The multiple-case study was based on two product development projects in a low-volume manufacturing company.

The main identified characteristics of the product introduction process in low-volume manufacturing industries were a low number of prototypes, absence of conventional production ramp-up, reduced complexity of the process, failure to consider the manufacturability of the products due to an extensive focus on their functionality and increased complexity of resource allocation. It was determined that prior production of similar products could serve as a facilitator of the manufacturing process.

The main limitation of this study is that the identified characteristics and facilitating factors are confined to the internal variables of the studied company. A study of the role of external variables during the product introduction process such as suppliers and customers could be the subject of future studies.

This research will provide practitioners in low-volume manufacturing industries with general insight about the characteristics of the product introduction process and the aspects that should be considered during the process.

Whereas there is a significant body of work about product introduction process in high-volume manufacturing industries, the research on characteristics of the product introduction process in low-volume manufacturing industries is limited.

In many countries that allow unsolicited proposals (USPs) for public–private partnership (PPP) projects, incentives are awarded to the initial proponent of the USP projects during the tendering process as rewards for initially making a proposal. Because of such a reward system, including the bonus system, USPs are commonly known to involve fewer tender participants. This paper aims to investigate the empirical relationship between the number of tender participants and the institutional factors of PPPs. Specifically, two institutional factors are examined: the use of USPs and the bonus system for initial USP proponents.

The ordinary least squares (OLS) and Poisson regression analysis is used in this study to analyze PPP data in South Korea.

This paper demonstrated that USP projects have fewer bidders participating in tenders than solicited projects. Meanwhile, the analysis showed that the bonus system as another component of the institutional framework did not account for the number of bidders in tendering. In the analysis by three different facility types (“Roads,” “Environmental facilities” and “Other” types) of whether the bonus system discouraged participation in the bidding, the authors found heterogeneous responses among the types. For “Roads” and “Other” types of projects, the existence of the bonus system reduced the number of bidders for USP projects, while for “Environmental facilities,” there was no negative relationship between bonus points and the number of bidders. In the analysis of whether there were fewer bidders when no bonus points were awarded, there was no statistically significant difference in the number of bidders for “Roads” and “Environmental facilities.”

This study shows the possibility that other institutional factors apart from bonus points affect competition. The characteristic factors of USPs can affect the decision to participate in the tender from the perspective of potential bidders.

Recent studies on USPs have mainly focused on the strategies that ensure the effective management of USPs for PPP implementation. However, quantitative effects of USPs on the tendering process have not yet been addressed. The quantitative effect refers to something that may be estimated by quantity or that relates to the describing or measuring of quantity, such as the present attempt to account for the number of bidders.

The present study aims to summarize different non-invasive techniques for continuous glucose monitoring (CGM) in diabetic patients using glucose-oxidase biosensors. In diabetic patients, the self-monitoring of blood glucose (BG) levels through minimally invasive techniques provides a quick method of measuring their BG concentration, unlike conventional laboratory measurements. The drawbacks of minimally invasive techniques include physical pain, anxiety and reduced patient compliance. To overcome these limitations, researchers shifted their attention towards the development of a pain-free and non-invasive glucose monitoring system, which showed encouraging results.

This study reviews the development of minimally and non-invasive method for continuous glucose level monitoring in diabetic or hyperglycemic patients. Specifically, glucose monitoring using non-invasive techniques, such as spectroscopy-based methods, polarimetry, fluorescence, electromagnetic variations, transdermal extraction-based methods and using body fluids, has been discussed. The various strategies adopted for improving the overall specificity and performance of biosensors are discussed.

In conclusion, the technology of glucose oxidase-based biosensors for glucose level monitoring is becoming a strong competitor, probably because of high specificity and selectivity, low cost and increased patient compliance. Many industries currently working in this field include Google, Novartis and Microsoft, which demonstrates the significance and strong market potential of self-monitored glucose-oxidase-based biosensors in the near future.

This review paper summarizes comprehensive strategies for continuous glucose monitoring (CGM) in diabetic patients using non-invasive glucose-oxidase biosensors. Non-invasive techniques received significant research interest because of high sensitivity and better patient compliance, unlike invasive ones. Although the results from these innovative devices require frequent calibration against direct BG data, they might be a preferable candidate for future CGM. However, the challenges associated with designing accurate level sensors to biomonitor BG data easily and painlessly needs to be addressed.

The study aims to examine whether a baseline trait-level characteristic, in this case propensity to trust, impacts peoples' perception of procedural justice, police legitimacy, trustworthiness, obligation to obey and cooperation.

Using Hamm and colleagues' (2017) integrated framework of legitimacy (IFL) as the theoretical framework, the current research explores whether individual trait characteristic differences matter. Using a Korean survey in 2019 with 2188 samples aged 19 to 28, this study conducted structural equation modeling to assess the impact of propensity to trust on the latent factors of the IFL.

The results support the findings of the original IFL. Specifically, when it comes to citizens' perception of police legitimacy, propensity to trust positively and significantly impacts latent factors such as procedural justice, trustworthiness, trust and obligation to obey. However, it fails to impact cooperation in any capacity.

Perception of police legitimacy has been researched extensively by various scholars. Specifically, Tyler's (1990) procedural justice model has been the main focus of police legitimacy research for the past 30 years. However, the current study aims to explore the possibility of trait-level characteristic that may influence peoples' perception of police legitimacy. Specifically, the authors aim to assess individual propensity to trust tendency and its impact on police legitimacy.

This chapter focuses on transforming cross-cultural conflict and misunderstanding into a learning opportunity, using a case study to illustrate an approach that has proven effective in tens of thousands of conflicts. This approach surfaces cultural values and approaches to work, toward improving intercultural management practices. It also supports employees to resolve their issues themselves, toward more sustainable solutions.

Transient climate sensitivity relates total climate forcings from anthropogenic and other sources to surface temperature. Global transient climate sensitivity is well studied, as are the related concepts of equilibrium climate sensitivity (ECS) and transient climate response (TCR), but spatially disaggregated local climate sensitivity (LCS) is less so. An energy balance model (EBM) and an easily implemented semiparametric statistical approach are proposed to estimate LCS using the historical record and to assess its contribution to global transient climate sensitivity. Results suggest that areas dominated by ocean tend to import energy, they are relatively more sensitive to forcings, but they warm more slowly than areas dominated by land. Economic implications are discussed.

This study aims to examine how science, technology, engineering, and mathematics doctoral students interact with postdocs within the research laboratory, identifying the nature and potential impacts of student–postdoc mentoring relationships.

Using a sample of 53 doctoral students in the biological sciences, this study uses a sequential mixed-methods design. More specifically, a phenomenological approach enabled the authors to identify how doctoral students make meaning of their interactions with postdocs and other research staff. Descriptive statistics are used to examine how emergent themes might differ as a product of gender and race/ethnicity and the extent to which emergent themes may relate to key doctoral student socialization outcomes.

This study reveals six emergent themes, which primarily focus on how doctoral students receive instrumental and psychosocial support from postdocs in their labs. The most frequent emergent theme captures the unique ways in which postdocs provide ongoing, hands-on support and troubleshooting at the lab bench. When examining how this theme plays a role in socialization outcomes, the results suggest that doctoral students who described this type of support from postdocs had more positive mental health outcomes than those who did not describe this type of hands-on support.

Literature on graduate student mentorship has focused primarily on the impact of advisors, despite recent empirical evidence of a “cascading mentorship” model, in which senior students and staff also play a key mentoring role. This study provides new insights into the unique mentoring role of postdocs, focusing on the nature and potential impacts of student–postdoc interactions.