Planning for equal transit-based accessibility of healthcare facilities: A case study of Shenzhen,China

Recent location-allocation studies have made considerable progress in optimizing the equality of facility accessibility but are focused on automobile transport to facilities. In cities, however, the transit-based accessibility of essential services is crucial for social equality and sustainable development. In this study, we develop a modified transit-based maximal accessibility equality (MAE) model for optimizing the equality of the transit-based accessibility of healthcare facilities. In this model, equality is quantified as the weighted mean absolute deviation (WMAD) of accessibility across locations. Two scenarios are set up to reallocate resources or allocate newly added resources. The results reveal that the equality of transit-based healthcare accessibility can be significantly improved in both scenarios. A dispersed planning strategy for facilities is suggested to achieve equal accessibility. However, the transit-based optimization results significantly differ from the car-based optimization results, with more supply allocated to facilities close to transit corridors. This finding implies that the traditional car-based MAE model might generate unequal healthcare accessibility for transit-dependent populations and thus lead to biased recommendations for healthcare planning. Furthermore, it shows that traditional car-based optimization may engender a misallocation of healthcare supply, exacerbating the inequality in healthcare accessibility. The necessity of incorporating public transit into public facility planning is highlighted. The improved MAE model can be applied in cities where the supply of public services is relatively adequate and public transit plays an important role in daily mobility.     

A conceptual framework for utilising VGI in land administration

Land administration systems assist citizens in identifying their rights, restrictions and responsibilities (RRRs) associated with land and property. However, delineating the spatial extent of all RRRs regulated in legislation remains a challenge. Approaches that have been used so far are mainly based on accurate surveying processes, which are untimely and too expensive. Previous studies proposed Volunteered Geographical Information (VGI) as a practical and low-cost method for fast acquisition of spatial data from volunteered contributions, mostly for countries where a limited number of land rights records exist. However, VGI could be beneficial for advanced and developing land administration systems in terms of collecting a range of RRRs which are not spatially well recorded and represented. A multidisciplinary review of the literature on VGI and land administration reveals the lack of a comprehensive conceptual framework for utilising VGI in land administration for the purpose of collecting a range of RRRs related to land and property. This paper investigates the main aspects of utilising VGI in land administration based on qualitative content analysis of available resources. The outcome of this research suggests that using VGI in land administration requires a comprehensive understanding of the complexities and interconnections of legal, institutional, technical and social aspects within each jurisdiction. These findings are merged to formulate a generic and innovative conceptual framework which serves as a basis for the process of collecting land and property RRRs from volunteer contributions.     

Adapting to the gender order: Voluntary conservation by forest owners in Finland

In this article we explore the hidden gender order of the biodiversity policy and, in particular, the social and societal positions offered to male and female forest owners in voluntary conservation. Two studies were conducted in the region where the National Biodiversity Program for Southern Finland (“METSO”) has been implemented. Study 1 focused on forest owners’ discursive practices that construct different actor positions to female and male forest owners; 27 interviews with forest owners were analyzed. Three discourses – “conservation threatens forestry,” “forestry threatens conservation,” and “good forestry is conservation” – were identified. Forest owners typically described themselves as “loggers” who were explicitly described as masculine. This was paired with the feminine position of “bystander.” The other two positions found, “protectors” and “political agents”, by contrast, were discussed as non-gendered positions. In Study 2 we examined quantitatively how individual forest owners’ nature conservation preferences and willingness to conserve forests is mediated by gender. The responses of 965 owners were analyzed with structural equation modeling. The endorsement of nature conservation preferences was found to increase willingness to conserve forests only among male owners. The results illustrate how individual forest owners adapt their nature conservation preferences to forestry's masculine socio-cultural context.     

What Are You Saying? Using topic to Detect Financial Misreporting

We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of a large collection of 10-K narratives spanning 1994 to 2012. We find that the algorithm produces a valid set of semantically meaningful topics that predict financial misreporting, based on samples of Securities and Exchange Commission (SEC) enforcement actions (Accounting and Auditing Enforcement Releases [AAERs]) and irregularities identified from financial restatements and 10-K filing amendments. Our out-of-sample tests indicate that topic significantly improves the detection of financial misreporting by as much as 59% when added to models based on commonly used financial and textual style variables. Furthermore, models that incorporate topic significantly outperform traditional models when detecting serious revenue recognition and core expense errors. Taken together, our results suggest that the topics discussed in annual report filings and the attention devoted to each topic are useful signals in detecting financial misreporting.     

Joint dynamic modeling and option pricing in incomplete derivative-security market

In this study, we evaluate the option prices on two assets under stochastic interest rates when the stochastic process that underlying asset prices follow is depending on a correlated bivariate Markov-modulated geometric Brownian motion model with jump risks. More specifically, we conduct the joint dynamic modeling by identifying two independent compound Poisson processes with the log-normal jump sizes to describe both individual jumps and systematic cojumps. Facilitating the cojumping behavior this way with the time-inhomogeneity of the volatility, option pricing expressions are readily obtainable since the Gerber–Siu’s approach is employed to determine a pricing kernel. The empirical results and numerical illustrations are provided to show the impact of cojumps and stochastic volatilities on option prices.     

Does financial development stimulate environmental sustainability? Evidence from a panel study of 115 countries

This paper investigates the moderation effect of financial development (FD) on the environmental Kuznets curve (EKC). Using comprehensive panel data of 115 countries spanning the 1990–2016 period and dividing the countries into different income groups, the researcher found systematic differences in the relationship between significant indicators of environmental degradation and economic growth. More specifically, the interaction effect of FD on the relationship between gross domestic product (GDP) growth and CO₂ emissions are positive; nevertheless, the interaction effect of FD on the relationship between GDP squared and CO₂ emissions become negative for all income groups and therefore supports the EKC. Moreover, the interaction effect of FD is negative on the relationship of GDP with both CH₄ and PM_2.5 emissions in the middle‐income groups, whereas the interaction effect of FD on the relationship between GDP squared and PM_2.5 emissions are negative for the high‐income group. Our results suggest that FD can reduce environmental degradation without adversely impacting on growth.     

Exploring customer sentiment regarding online retail services: A topic-based approach

User-generated content is a valuable source for understanding online shoppers' emotions. Using text-mining techniques, this study identifies seven topics regarding online retail services in online posts: product, retailer promotion, delivery, payment, communication, return/refund, and price. The topics are associated with the sentiment polarity of online shoppers' posts. This study further explores whether the emotional responses from domestic and cross-border online shoppers differ with regard to these topics. The results show that differences exist in these two groups' sentiments concerning product and payment. Furthermore, there are differences in the two groups’ respective negative emotions (i.e., anger, sadness, and fear) concerning delivery, communication, and return/refund. The findings of this study provide online retailers with important managerial implications.     

The Value of Service Robots from the Hotel Guest’s Perspective: A Mixed-Method Approach

Service robots (SR) are increasingly valued and embraced; they are here to stay. Research on collaborative intelligence to better understand robotic-human partnerships is scarce. To bridge that gap this study aimed to examine the value of SR from the guest’s perspective, thus gain a deeper understanding of the co-value creation process in the context of full-service hotels. A mixed-method design was used to capture the depth and breadth of perceived value of SR. Study 1 is a qualitative study probing consumers’ sense making regarding SR. Study 2 used structural equation modeling to test the hypotheses derived from Study 1. Results indicate that perceived privacy, functional benefits of SR, and robot appearance positively influence consumers’ attitude towards adoption of SR. Functional benefits and novelty had an impact on the individuals’ anticipated overall experience. Attitude and anticipated overall experience, in turn, enhanced consumers’ acceptance of SR. Implications, limitations, and future research are discussed.     

BayesRec: Personalize search ranking with customer attribute-level willingness-to-pay using heterogeneous booking choice data

The ongoing travel restrictions owing to the COVID-19 shutdown continue to impact the hospitality sector. This pause on travel has led to a bigger regime shift in the customer's preferences for hotel selection. Therefore, this study aims to help understand customers’ evolving preferences by assessing the customers’ attribute-level willingness-to-pay (WTP) to tailor the booking experience. Furthermore, the study accounts for customers’ behavioral heterogeneity and decision choices while estimating WTP. The proposed approach utilizes multi-stage online modeling: (i) Segmentation to create behavioral customer cohorts; (ii) Hierarchical Bayesian modeling to estimate customer-level WTP; and (iii) Multi-criteria decision optimization to rank-optimize the hotel alternatives by leveraging the assessed attribute-level WTP. This continuous assessment provides personalized hotel recommendations and enhances customer’s booking experience, thereby allowing hotels to accelerate revenue recovery and aid in proactive demand generation for the next wave of travelers in a post-COVID economy.     

Why do retail consumers buy green apparel? A knowledge-attitude-behaviour-context perspective

Consumers' increased knowledge and awareness of environmental issues have not translated into a pervasive rise in purchasing green apparel, resulting in a phenomenon known as the ‘attitude-behaviour gap’. The current study seeks to explicate this gap by examining the drivers of green apparel buying behaviour. Towards this end, the study examines the association of environmental knowledge, green trust, and environmental concern with environmental attitude and green apparel buying behaviour. It further investigates the association of labelling desire and labelling satisfaction with this type of buying behaviour as well. In addition, the study uses the theoretical lens of the knowledge-attitude-behaviour model and attitude-behaviour-context theory to anchor its hypotheses. Cross-sectional data from 387 Japanese consumers analysed to test the conceptual model revealed that green trust, environmental attitude, and labelling satisfaction are positively associated with green apparel buying behaviour. Furthermore, green trust, environmental concern, and environmental attitude partially mediate the proposed associations, while age and gender moderate the association between environmental knowledge and environmental concern. The study's empirical insights thus lay the foundation for future research in this area and provide strategically relevant inferences for green apparel marketers and retailers.