Employing structural topic modelling to explore perceived service quality attributes in Airbnb accommodation

This study employs the structural topic model to extract service quality attributes from 242,020 Airbnb reviews in Malaysia. 22 service related topics were extracted from the corpus and four topics have not appeared in previous Airbnb studies. A widely used modified SERVQUAL questionnaire (MSQ) is cross-validated in this study by comparing its service quality attributes with the results of the topic modelling, which indicates that this MSQ can cover general Airbnb service quality attributes. This study also examines the different preferences of Malaysian and international Airbnb users and the changing patterns of the top six service quality attributes during a five-year period. The findings reveal that Malaysian Airbnb users care more about the appearance and location of the property, and international Airbnb users pay more attention to whether the property can accommodate a group of people. In addition, communication with the host is found to play an increasingly important role in Airbnb users’ lodging experiences.     

Assessing data quality: A managerial call to action

While awareness of data quality has increased in recent years, there have been very few studies on the actual level of data quality within organizations. This article outlines the analysis of 75 data quality assessments collected over the course of 2 years from a wide range of organizations, data sets, and business processes. The results reveal that data is in far worse shape than most managers realize. On average, 47% of recently created data records have at least one critical error. High-quality data is the exception, with only 3% of the DQ scores rated acceptable (≥97%). Indeed, the results suggest an unhealthy organizational tolerance of bad data and underscore the magnitude of improvement organizations need to make in order to be truly effective in the knowledge economy. By providing a unique insight and benchmark for data quality practitioners, this article serves as a call-to-action for all organizations—regardless of size and type—to determine their level of data quality. Finally, we set out a typology that presents a categorical scheme to promote preemptive actions against the most frequent types of data error.     

The market quality of commodity futures markets

To study the market quality of commodity futures markets, we construct a commodity futures market quality index from the perspective of liquidity, efficiency, and volatility. Based on the market quality index, the Chinese commodity futures market operates steadily. The metal futures market is more efficient and stable than the market for agricultural futures. The Chinese commodity futures market is less liquid and more volatile than the U.S. market. We examine the determinants of market quality and find that macroeconomic variables and futures market contracts are significantly related to the market quality of Chinese commodity futures.     

Using paradata to collect better survey data: Evidence from a household survey in Tanzania

Data are a key component in the design, implementation, and evaluation of economic and social policies. Monitoring data quality is an essential part of any serious, large‐scale data collection process. The purpose of this article is to show how paradata should be used before, during, and after data collection to monitor and improve data quality. To do this we use timestamps, global positioning system (GPS) coordinates, and other paradata collected from an 800‐household survey conducted in Tanzania in 2016. We demonstrate how key paradata can be used during each phase of a research project to identify and prevent issues in the data and the methods used to collect it. Our results corroborate the importance of collecting and analyzing paradata to monitor fieldwork and ensuring data quality for micro data collection in developing countries. Based on these findings we also make recommendations as to how researchers can make better use of paradata in the future to manage and improve data quality. We argue for an expansion in the understanding and use of varied paradata among researchers, and a greater focus on its use for improving data quality.     

Japan's FDI drivers in a time of financial uncertainty. New evidence based on Bayesian Model Averaging

In this paper we analyse the determinants of Japanese outward FDI stock for the period 1996–2017. This period is especially relevant as it covers a process of increasing economic globalization and two financial crises. To this aim, we consider a large set of candidate variables based on the theory as well as on previous empirical analysis. Our sample includes a total of 27 host countries. We select the covariates using a data-driven methodology, the Bayesian Model Averaging (BMA) analysis. Moreover, we also analyse whether these determinants change depending on the degree of development (emerging vs developed) or the geographical areas (EU vs East Asia) of the countries considered. We find that Japan's FDI can be explained by a wide variety of variables, that include not only the typical gravitational ones but also institutional and macroeconomic variables, including those that measure financial development. Moreover, Japanese FDI can be explained by both horizontal and vertical FDI motives in the groups of countries analysed. However, in developed, and more precisely, EU countries, horizontal FDI strategies are predominant, whereas for East Asian and emerging countries, there is more evidence in favour of vertical FDI.     

Return policy,No joke: An investigation into the impact of a retailer's return policy on consumers' decision making

This study investigates the important role of a retailer's return policy in consumers' decision making. Utilizing signaling theory and the Cue Diagnosticity framework, this study posits that return policy, as a cue from a retailer, interacts with other cues (product, price, and product quality) to reduce perceived purchase risk and increases store image and patronage intention. Employing factorial experiment design, the authors conduct two studies and find support for several hypotheses. Results show that consumers value return policies depending upon the situation. A lenient return policy drives down purchase risk and leads consumers to have positive store images and higher patronage intentions. Both theoretical and managerial implications of the findings are provided.     

环境库兹涅茨曲线假说检验研究进展与展望

[目的]EKC曲线描述的是生态环境质量随着经济增长呈现先恶化后改善的趋势,即存在倒“U”型关系。为了了解EKC假设检验目前研究现状与存在的主要问题,深入探讨未来研究重点和方向。[方法]系统梳理经济发展对生态环境质量影响的EKC假说检验文献,以期丰富EKC检验的理论基础,进一步促进经济、生态环境健康持续发展。[结果]各种污染物指标与人均GDP间呈现出倒“U”型、正“U”型、倒“N”型、线型(或“~”型)等多种关系。[结论]未来研究重点和方向是:(1)研究形成EKC曲线背后的作用机理;(2)合理构建反映整体生态环境质量综合指标及影响经济发展的多因素指标;(3)合理构建经济发展与生态环境相互作用的理论模型,并在研究中引入综合方法;运用空间计量模型实证检验EKC假说;(4)国内对小城镇和欠发达地区经济发展对生态环境影响的EKC假说检验。     

服务质量、私人关系对顾客忠诚的影响研究

服务质量和顾客忠诚是企业获得竞争优势的法宝,而私人关系作为中国企业管理中独特的要素受到越来越多的关注,揭示三者之间的内在联系对于提升服务企业绩效及丰富相关管理理论十分重要。本文以服务行业为研究对象,研究服务质量、私人关系对顾客忠诚的影响。数据分析结果表明,企业的服务质量和顾客与服务人员私人关系都正向影响顾客对企业的忠诚,企业的服务质量有助于提升顾客与服务人员的私人关系,在企业一致性高的情况下,顾客与服务人员私人关系对顾客对企业忠诚有更大的影响。这些发现对于提升顾客忠诚有一定的借鉴意义,也有助于丰富中国情境下的本土化理论。     

Masterful Meisters? Voluntary Certification and Quality in the German Crafts Sector

Voluntary certification systems potentially reduce search costs in markets with imperfect information. Service markets with many heterogeneous firms – such as the crafts market – are prone to information imperfections and can therefore potentially benefit from such informational mechanisms. We examine if the Meister qualification in the German crafts sector improves service quality as perceived by consumers. We find a significant and positive relationship between the Meister title and consumer ratings and conclude that the certificate is a credible sign of quality.     

The Evolving Structure of Chinese R&D Funding and its Implications for the Productivity of Agricultural Biotechnology Research

China’s research and development (R&D) policy has changed considerably over recent decades, and great changes occurred in 2006 when the main programme objective of China’s R&D changed from the 863 Programme and 973 Programme to the National Science and Technology Major Project. One topic that has drawn extensive attention is whether the investment reform improved R&D productivity in China. Using a unique panel dataset from 160 universities, this paper examines the effect of the investment reform on productivity improvement in China’s agricultural biotechnology sector. We use a panel count data model with a dynamic feedback mechanism to model the knowledge production process. Strong evidence indicates that the investment reform greatly contributes to knowledge output production in China’s agricultural biotechnology sector. We also find that the input quality is more important than the absolute quantity; human research capacity exhibits the greatest contribution to the output of patents; past knowledge accumulation helps produce more patents; and entry barriers to patent production exist in China’s agricultural biotechnology sector. Moreover, the patent explosion in China may have been largely caused by improvements in the human capital input quality.