政府监督与矿产业安全投入的经济分析

中国矿业事故多发的根本原因在于政府监督不足导致的矿产业生产安全低投入。本文研究表明:一定水平的政府监督是诱导矿业企业安全投入的必要条件。在政府监督者与当地政府合二为一的背景下,政府监督的目标为税收收入与监督投入和矿业企业安全事故补偿支出之差的最大化;政府监督和矿业企业安全投入之间存在正向强化关系,但出于对自身税收收入的考虑,导致政府对矿业安全投入的监督不足;政府监督的缺失使得矿工意外事故补偿在诱导矿业企业安全投入中发挥重要作用。政府监督不足的制度性根源在于监督者的败德行为、委托代理关系错位和监督者激励不足等。因此,如何摆脱政府监督和矿业企业安全投入低水平均衡是破解中国矿业生产事故多发难题的关键。     

Text mining-based four-step framework for smart speaker product improvement and sales planning

The smart speaker market, which is considered an early-stage market, is expected to grow rapidly as smart speakers become a part of daily life. Consequently, manufacturers are trying to dominate the market. To achieve this, they must analyze users' reactions to their products and find insights for product improvement through comparison with competitors. We propose a four-step methodological framework for identifying meaningful opinions from a large number of online user reviews. First, network analysis is conducted to compare differences between brands. Next, through topic modeling, the attributes of speakers that users consider the most crucial are extracted for each brand. Third, sentiment analysis is conducted to examine how users' emotional polarities differ for each attribute. Through this, product improvement and product sales plans can be derived. Finally, in order to clarify the strengths and weaknesses of each brand, brand positioning is conducted and user opinions that have changed along with the evolution of the speaker's generation are analyzed. Our study identified the factors that positively or negatively affect the experience of smart speaker users. In addition, the proposed method is highly useful because it can be used to derive insights from a large amount of user opinion data regardless of the search term.     

Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews

In recent years, there has been proliferation of grocery mobile apps as grocery shopping on mobile has found increasing acceptance among customers accelerated by multiple factors. Maintaining high level of customer satisfaction is important for grocery mobile apps in the highly competitive app market. Online reviews have been a rich source of information to analyze customer satisfaction with a product or service. This paper explores the determinants of customer satisfaction for grocery mobile apps using online reviews. Latent Dirichlet Analysis (LDA), which is a text mining technique, is used to analyze online customer reviews of 27,337 customers to identify determinants of customer satisfaction. The determinants identified were further analyzed using a series of analysis to understand the importance of each determinant. Dominance analysis examined the relative importance of the determinants of customer satisfaction based on the overall rating. Correspondence analysis identified determinants which cause satisfaction separately from the determinants which cause dissatisfaction. The results from this study will provide insights to business managers of grocery mobile apps for decision-making on customer satisfaction management.     

Assessing the implementation of sustainable public procurement using quantitative text-analysis tools: A large-scale analysis of Belgian public procurement notices

Public organizations are using sustainable public procurement (SPP) as a policy tool to address societal and environmental issues. Having a policy on SPP however does not guarantee implementation. Several barriers have for example been identified that prevent public procurers from implementing SPP in their procurement projects, such as financial constraints, lack of knowledge or motivation. The question therefore arises how much SPP public organizations actually implement in their procurement projects. However, existing studies often focus on the environmental part of SPP and often rely on using interviews or surveys to assess the perceived degree of SPP (which have been accused of being subject to social desirability bias and low response rates). Little is therefore known about what SPP is in practice, and how frequently it is implemented. In this study, we therefore provide a detailed operationalization of SPP that encompasses the full concept. We subsequently assess the implementation of SPP in practice using text mining techniques to analyse over 140.000 Belgian public procurement notices that were published between 2011 and 2016. The research shows that in more than 70% of the notices (with an annex) SPP is implemented, but there appears to be a downward trend. It seems that SPP is implemented less over time, rather than more. Environmentally friendly procurement was, relative to other types of SPP, prevalent over time and across regions. For SPP to live up to its potential there are thus still barriers to be overcome.     

Forecasting election results by studying brand importance in online news

This study uses the semantic brand score, a novel measure of brand importance in big textual data, to forecast elections based on online news. About 35,000 online news articles were transformed into networks of co-occurring words and analyzed by combining methods and tools from social network analysis and text mining. Forecasts made for four voting events in Italy provided consistent results across different voting systems: a general election, a referendum, and a municipal election in two rounds. This work contributes to the research on electoral forecasting by focusing on predictions based on online big data; it offers new perspectives regarding the textual analysis of online news through a methodology which is relatively fast and easy to apply. This study also suggests the existence of a link between the brand importance of political candidates and parties and electoral results.     

Improving text summarization of online hotel reviews with review helpfulness and sentiment

The considerable volume of online reviews for today's hotels are is difficult for review readers to manually process. Automatic review summarizations are a promising direction for improving information processing of travelers. Studies have focused on extracting relevant text features or performing sentiment analysis to compile review summaries. However, numerous reviews contain nonspecific or nonsentimental content, hindering the ability of sentiment-based techniques' to accurately summarize useful information from hotel reviews. This paper proposes a systematic approach that first constructs classifiers to identify helpful reviews and then classifies the sentences in the helpful reviews into six hotel features. Finally, the sentiment polarities of sentences are analyzed to generate the review summaries. Experiment results indicated that the performance of the proposed approach was superior to other methods.     

Long distance commuting: A tool to mitigate the impacts of the resources industries boom and bust cycle?

Western Australia experienced a prolonged resources boom for more than a decade commencing in 2001. The majority of mining industry employees commute long distances from their homes, living onsite in company accommodation and working compressed rosters for a prescribed period before commuting home again for furlough and recommencing the work and commute cycle. Many community leaders, politicians and businesses complain that company policies and industrial relations arrangements, which enabled long distance commuting (LDC), undermine regional economic development. They argue that the host communities closest to mining operations bear the brunt of globally driven boom and bust markets and experience many of the disadvantages but few of the opportunities associated with booms or busts, while source communities, particularly large cities, reap the benefits from repatriated salaries, increased populations and investment derived from mining activities in the host communities.This paper examines the role of long distance commuting as a tool for mitigating the impacts of the boom and bust cycles in the resources industries of Western Australia, focusing on the resources-rich region of the Pilbara. The paper will also speculate the social and economic impact on the mining communities and the state more broadly if government had capitulated and restricted long distance commuting.     

Leadership research in the root of hospitality scholarship: 1960–2020

This study documents the intellectual structure and progress of hospitality leadership research over the past sixty years by presenting a perspective and conspectus. The authors use text mining analysis to explore topic highlights and how they have shifted over time. Leadership development has been the predominant feature followed by contemporary leadership behaviors. Of the eight leadership domains revealed by the co-citation and text mining analyses, four are associated with leadership styles, which in their turn impact on employees. LMX is the leading theme in hospitality leadership research, with growing attention to the darker side of leadership, particularly abusive supervision. The current study contributes to the hospitality literature by considering coverage of leadership issues since the 1960 s. Furthermore, it encompasses the evolution of hospitality leadership research from the development of theory to a focus on impacts, thereby providing crucial insights that may allow industry professionals to lead their organizations more effectively and efficiently.     

Crude oil price forecasting incorporating news text

Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long text to discover knowledge from them. Nonetheless, some of the existing research about text-based crude oil forecasting employs LDA to explore topics from news headlines, resulting in a mismatch between the short text and the topic model and further affecting the forecasting performance. Exploiting advanced and appropriate methods to construct high-quality features from news headlines becomes crucial in crude oil forecasting. This paper introduces two novel indicators of topic and sentiment for the short and sparse text data to tackle this issue. Empirical experiments show that AdaBoost.RT with our proposed text indicators, with a more comprehensive view and characterization of the short and sparse text data, outperforms the other benchmarks. Another significant merit is that our method also yields good forecasting performance when applied to other futures commodities.     

A data-driven approach to measure restaurant performance by combining online reviews with historical sales data

Restaurant management requires customer responsiveness to deal with increasingly higher expectations and market competitiveness. This study proposes an approach to simplify the decision-making process of restaurant managers by combining both live social media customer feedback and historical sales data in a sales forecast model (based on TripAdvisor data and the Bass model).Our approach was validated with internal and external (i.e., online reviews) data gathered from six restaurants. The collected data was processed using data analytics for developing a dashboard that provides value for restauranteurs by taking advantage of online reviews and sales forecast. Such dashboard was evaluated by restaurant management experts, which provided positive feedback, highlighting in particular the time saved in the decision-making process.