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The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

Currently, there is a conflict in developing countries between the requirements for the self-development of forestry and the insufficient investment in the forestry sector, and the forest ticket system is an innovative forestry management method to solve this contradiction. In the research on the forest ticket system, the study of its price formation mechanism is relatively important. The key issues of the forest ticket system are how to form the forest ticket price and whether the forest ticket pricing methods are reasonable. Solving these problems is the purpose of this study.

This study will use three methods, namely the forest ecosystem service value evaluation index method, the ecosystem service value based on per unit area evaluation method and the contingent valuation method, to study the forest ticket price formation mechanism, filling the gap in the current research on forest ticket pricing methods. It will analyze how these three pricing methods specifically price the forest ticket and evaluate whether these pricing methods are reasonable. This study will then summarize and comprehensively study the forest ticket price formation mechanism and provide policy recommendations for decision-making departments.

The contingent valuation method and the forest ecosystem service value evaluation index method should be mainly used and given priority in the forest ticket pricing process. When the forest ticket is mainly issued for local residents' willingness to compensate for the forestry ecological value, the contingent valuation method should be mainly considered; when the forest ticket is mainly issued for compensating for the ecological value of local used forest land, the forest ecosystem service value evaluation index method should be mainly considered. The ecosystem service value based on per unit area evaluation method does not need to be the focus.

Compared with existing research studies, which focus more on the forest ticket system itself and the definition of forest ticket, this study mainly focuses on the forest ticket price formation mechanism, emphasizing how to form the forest ticket price and whether the forest ticket pricing methods are reasonable, which has a certain degree of innovation and research value and can partially fill the gap in related fields. At the same time, this study has certain help for the enrichment of the forest ticket system and the extension of related research studies.

In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.

The authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.

The results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.

To the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.

Understanding employee knowledge hiding behavior can serve organizations in better implementing knowledge management practices. The purpose of this study is to investigate how personality and work climate influence knowledge hiding, by examining the respective roles of openness to experience and relational (specifically, communal sharing and market pricing) climates.

Multilevel modeling was used with two distinct samples, one from Vietnam with 119 employees in 20 teams and one from The Netherlands with 136 employees in 32 teams.

In both samples, the hypothesized direct relationship between openness and knowledge hiding was not found. In the Vietnamese sample, only the moderating effect of market pricing climate was confirmed; in the Dutch sample, only the moderating effect of communal sharing climate was confirmed. The findings of the Vietnamese sample suggest that people with a high sense of openness to experience hide knowledge less under low market pricing climate. In the Dutch sample, people with high openness to experience hide knowledge less under high communal sharing climate. The authors conclude that, in comparison with personality, climate plays a stronger role in predicting knowledge hiding behavior.

Small sample size and self-reported data might limit the generalizability of this study’s results.

The paper highlights how organizational context (relational climate) needs to be taken into account in predicting how personality (openness to experience) affects knowledge hiding.

This paper contributes to a better understanding of the knowledge hiding construct by extending the set of known antecedents and exploring the organizational context in which such phenomena happen.

The economic and administrative conditions of countries normatively have an effect on the economy and level of market development. Moreover, it is of great importance for a healthy economy whether the public institutions and organizations are transparent and functioning in accordance with their purpose. The aim of this study is to show whether there is a relationship between transparency and market efficiency.

Correlation analysis has been conducted between prediction accuracy rates, which are obtained by seven different machine learning algorithms and Corruption Perception Index (CPI) levels.

It has been statistically shown that the indices of countries with low corruption levels are harder to predict, which, in turn, can be interpreted as having higher weak-form market efficiency. According to that, an intermediate negative correlation has been found between CPI scores and predictability levels of stock indices. Considering the findings, it can be interpreted that the markets of countries with relatively more transparent and well-functioning public sector have more weak-form market efficiency.

The study can be extended with cutting-edge machine learning and deep learning techniques in future studies. There are very few studies which try to explain factors related to market efficiency. Thus, the authors claim that there is still room for further research in order to determine the factors related to market efficiency, implying that current literature is still far from explaining the causation behind the inefficiencies.

According to findings, the markets of countries with relatively more transparent and well-functioning public sector have more weak-form market efficiency. Based on these findings, in practice, it can be said that more successful predictions can be made using machine learning algorithms in countries with relatively lower CPI scores.

In literature, the factors related to market efficiency are still far from explaining the causation behind the inefficiencies. Thus, it has been investigated whether transparent and well-functioning public institutions and organizations have any relation with market efficiency.

The purpose of this paper is to identify low-costbus business models from different parts of the world and check their applicability in the Brazilian market. It also identifies crucial factors for the development of that kind of business and investigates the relationship between low-cost buses and other modes of transport. This research analyzes every relevant aspect to the applicability of low-cost business models in Brazil, driving to discussions and conclusions. The gains on the development of low-cost bus systems in Brazil may have a wide reach, from personal to general public benefits.

Business models for low-cost bus systems are used to analyze in a qualitative approach. The data are collected through semi-structured interviews, direct observations and documental basis. In addition, innovations over the previous five years are evaluated in order to establish a comparative pattern between companies.

There is a great potential in the Brazilian passenger market for the entrance of low-cost bus companies. The only question is just when it is the right time to enter that market. Most of the negative points presented for the implementation of a low-cost company are related to the current economical and political crisis in Brazil. It was identified as a potential cause for the overall decrease of the passengers market in recent years, and specifically of the bus passengers market.

The recent regulation changes, the high demand for passengers and even the similarity of possible routes in Brazil to the ones in Europe and in the USA make Brazil a fertile soil for the development of that kind of business. A similar price mechanism to the ones applied worldwide was also identified as doable in Brazil.

This study aims to reveal the impact of consumers’ price sensitivity on their purchase intention within the scope of supermarkets. Besides, the study aims to examine the impact of consumers’ price sensitivity on their price perception level and emotions and the impact of consumers’ price level perception and emotions toward supermarkets on their purchase intention. It also aims to detect the mediating effects of consumers’ price level perception and emotions toward supermarkets between their price sensitivity and purchase intention.

The quota sampling method was used to form the study sample. The population was 20–69-year-old consumers. The study sample included 513 consumers, 276 of whom were men, and 237 of whom were women. Data were collected via a questionnaire by the researchers in Mersin’s (Turkey) five central counties. Explanatory and confirmatory factor analyses and structural equation models were used to analyze data.

Consumers’ price sensitivity, perception of cheapness, perception of expensiveness and positive emotions toward supermarkets affect their purchase intention. Besides, price sensitivity affects their perception of cheapness while it does not affect their perception of expensiveness. It influences negative emotions, but not on positive emotions. Consumers’ perception of cheapness and perception of expensiveness have impacts on positive emotions toward supermarkets. It was additionally discovered that perception of cheapness and perception of expensiveness affected negative emotions toward supermarkets. A contributed finding was that perception of cheapness had a partial mediating role between price sensitivity and purchase intention.

The study provides managerial implications in terms of understanding consumers’ behavioral changes, developing effective pricing strategies and achieving competitive advantages over the other retailing companies.

The study illustrates that consumer behavior can be explained by a theoretical construct considering the price perception levels and emotions toward supermarkets in examining the effect of consumers’ price sensitivity on their purchase intention. Therefore, it contributes to explain consumers’ behavior by bringing the stimulus–organism–response (SOR) model into a theoretical construct.

本研究旨在揭示在超級市場的研究範疇內，消費者的價格敏感度對其購買意圖的影響。此外，本研究亦擬探討消費者的價格敏感度對其價格水平感知及情緒的影響，以及消費者的價格水平感知及對超級市場的情感對其購買意圖的影響。本研究亦旨在檢測在消費者的價格敏感度與購買意圖之間，其價格水平感知及對超級市場的情感兩者的仲介效果。

本研究用了配額抽樣法來建立研究樣本。研究的對象為20嵗至69嵗的消費者。研究樣本包括513名消費者，其中276人為男性，237人為女性。數據由研究人員透過一項問卷調查在梅爾辛省 (土耳其) 的五個中心縣取得。使用探索性因素分析，驗證性因素分析及結構方程模型來分析數據。

研究結果顯示，消費者的價格敏感度，廉價感，對昂貴的看法及對超級市場的正面情緒，均影響其購買意圖。此外，消費者的價格敏感度會影響其廉價感，唯其對昂貴的看法則不受影響。價格敏感度對負面情緒帶來影響，但正面情緒則不受影響。消費者對廉價的看法及對昂貴的看法均影響他們對超級市場的正面情緒。研究亦發現，廉價感及對昂貴的看法均影響消費者對超級市場的負面情緒。貢獻的發現是：廉價感於價格敏感度與購買意圖之間扮演部分仲介角色。

本研究對管理有其作用。研究結果幫助管理人員更了解消費者行為的改變，發展有效的定價策略，以及比其它零售公司更能發展競爭優勢。

本研究闡明消費者行為是可以利用一個理論構建來說明的，而這個理論構建是透過考慮價格水平感知及對超級市場的情感來探討消費者的價格敏感度對其購買意圖的影響。因此，本研究透過把「刺激 – 機制 – 反應」模型變成為一個理論構建來說明消費者的行為， 在這方面，本研究是有其貢獻的.

This paper aims to examine and compare the export performance and competitiveness of Indian and Chinese textile and clothing industry in post-multifibre arrangement (MFA) era.

Balassa’s revealed comparative advantage Index is used to assess the competitiveness of Indian and Chinese textile and clothing exports.

The results indicate that China’s textiles and garments sector holds a greater proportion of the global market compared with India. India has a robust comparative advantage in silk, carpets and cotton post-MFA. Vegetable textile fibers, paper yarn and woven fabrics of paper yarn are also competitive. China had a strong comparative advantage in silk and fabrics; special woven fabrics, tafted textile fabrics, lace, tapestries, trimmings and embroidery in 2005. China also recorded comparative advantage in silk, man-made filaments: strip and the like of man-made textile materials, fabrics; special woven fabrics, tafted textile fabrics, lace, tapestries, trimmings and embroidery and fabrics; knitted or crocheted in 2021.

This study’s results and recommendations could assist the Indian and Chinese Governments develop policies to upgrade their garment industries.

Though vast literature reviews are available for textile and apparel export performance in India and China separately, there are few studies on comparisons. This study is a significant attempt to evaluate India and China’s competitiveness in the global market.

This paper examines how the macroeconomic environment affects the determinants of prepayment of mortgage loans from October 2004 to February 2020. For more accurate analysis, the authors define the timing of prepayment not only before the loan maturity but also at the time when 50% or more of the loan principal is repaid. The results show that, during the global financial crisis as well as the recent period of low interest rates, macroeconomic variables such as interest rate spreads and housing prices have a different effect compared to the normal situation. Also, significant explanatory variables, such as debt to income (DTI) ratio, loan amount ratio and poor credit score, have different effects depending on the macroenvironment. On the other hand, in all periods, the possibility of prepayment increases as comprehensive loan to value (CLTV) increases, and the younger the age, the shorter the loan maturity. The results suggest that, in the case of ultralong (40 years) mortgage loans recently introduced to support young people purchasing houses, the prepayment risk can be, at least partially, migrated by offsetting the increase in prepayment by young people and the decrease in prepayment due to long loan maturity. In addition, this study confirms that the accelerated time failure model compared to the logit model and COX proportional risk model has the potential to be more appropriate as a prepayment model for individual borrower analysis in terms of the explanatory power.