Search results

The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales.

The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales.

This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume.

This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.

The majority of research on the implementation and use of Building Information Models (BIM) have focused on building design and pre-construction planning. There is only limited research on the usage of BIM in the construction phase, especially by site workers. The purpose of this paper is to analyze the use of BIM by site workers through so-called “BIM-kiosks”. The kiosks allow workers to access BIM models on-site. The aim of this paper is to take a closer look on the introduction of BIM-kiosks and scrutinize how and for what purpose site workers use the kiosks.

This is a single case study of an ongoing real-world construction project. The case study is based on qualitative data which stems from observational studies, interviews as well as document studies.

Site workers find opportunities for 3D visualization useful and the BIM models are appraised for efficiently handling complex elements. The findings also indicate that the use of BIM-kiosks lead to a greater level of face-to-face collaboration between workers on-site. This may happen because workers meet, both planned and randomly, to discuss in front of the kiosks while using it for visualization.

The research is limited to a single case. However, some of the recommendations may be relevant to other projects. In addition, the findings demonstrate some of the potential of using BIM on-site in upcoming construction projects.

Only a few studies have addressed the use of BIM among site workers. Thus, the findings provide practitioners and researchers insight into how current practices may be improved, as well as areas where more research is needed.

This paper aims to understand the implication of night soil selling at the public toilets for the shared interests between colonial state and business in nineteenth-century Hong Kong. More specifically, this paper attempts to look at the ways the toilets were sustained by the sharing interests over night soil profits between state and business sector.

It is argued from the political economy perspective that the night soil profit determined the public toilet development.

The successful emergence of the modern state of colonies was generally attributed to colonial modernization, a force that was widely recognized for having introduced hygienic modernity. It was easily assumed that the public toilets would be provided by colonial government. Instead, sanitary problems during the early colonization of this colony were addressed by the privately-owned public pail toilets provided by big Chinese landowners through the selling of night soil. Based on this quasi-commercial mode, these toilets, which served as night soil collection points, were certainly inefficient; they however survived for half a century into the early twentieth century.

The paper challenges the long-established assumptions of binary relations and hierarchical public roles that put them into zero-sum competition of capacity. It rather argues that the interests aligned with each other.

The aim of this paper is to examine how crises can be triggered online, how different social media tools escalate crises, and how issues gain credibility when they transit to mainstream media.

This exploratory study uses the multiple case study method to analyze five crises, generated online, throughout their life-cycles, in order to build analytic generalizations (Yin).

Crises are often triggered online when stakeholders are empowered by social media platforms to air their grievances. YouTube and Twitter have been used to raise issues through its large user base and the lack of gatekeeping. Facebook and blogs escalate crises beyond the immediate stakeholder groups. These crises are covered by mainstream media because of their newsworthiness. As a result, the crises gain credibility offline. Mainstream media coverage ceases when traditional news elements are no longer present.

If crises are increasingly generated online, this study aims to apply a framework to manage the impact on organizations.

How practitioners can use different new media tools to counter crises online and manage the transition of crises to mainstream media.

This is one of the first few studies that analyses how organizational crises originate online, gain traction and get escalated onto mainstream media. Understanding what causes crises to trigger online and gain legitimacy offline will enable practitioners to engage in effective crisis management strategies.

The US-China trade war has brought forth the problem of balance of trade not only for them, but also for many other economies in the world. However, all the commodity segments are not equally affected and thus, the segment-wise trade analysis of commodities can bring up many valuable insights, vital for policy formulation process. Despite this, existing literature barely covered this aspect as a focal research. Therefore, this chapter has carried out segment-wise analysis of commodity classes popular in international trade discussions for the United States and China since the trade dispute intensified between them. In this chapter, we have built an argument around three commodity-segments which are popular in international trade studies namely, the raw material segment, semi-finished goods segment, and finished goods segments. While doing this analysis, we majorly focused on monopolistic power of economies in different commodity segments. We found that while in the segment of raw material, mostly cost is driving the trade, in the finished goods segment, variety and innovations are the key drivers that can boost trade by discovering new consumption spaces.

Failures of public–private partnership (PPP) projects are often ascribed to the agency problem, which arise under conditions of inadequate and asymmetric information when a principal (the client) hires an agent (the contractor). This paper aims to identify the causes and effects of moral hazard and adverse selection on PPP construction projects using a synthesis of extant literature (to determine key variables) and analysis of survey questionnaire data collected.

Mean score ranking was used to rank the causes and effects of moral hazard and adverse selection problems in PPP construction projects. One sample t-test was conducted to establish the relative significance of these variables.

Effort dimensions (which are not verifiable), low transfer of risk, lack of accurate information about project conditions, wrong party chosen to execute project and renegotiation of contracts were the most significant causes of moral hazard and adverse selection problems in PPP construction projects. In addition, reduction of competition, high transaction costs, consequences on profitability of project, siphoning of funds and negative implications on enforceability of contract were the most significant effects of moral hazard and adverse selection problems in PPP construction projects.

Application of these findings will help mitigating moral hazard and adverse selection problems occurring when undertaking PPP construction projects.

Research findings provide guidance to construction stakeholders in the PPP sector on the different causes and effects of adverse selection and moral hazard. This pioneering study is the first to conduct an empirical assessment of the causes and effects of moral hazard and adverse selection of PPP construction projects in a developing country.

COVID-19’s influence on the teaching profession will be felt for many years as teachers faced experiences that they have never encountered. The pandemic forced already taxed teachers to assume additional responsibilities, many of which they were not prepared to deal with. The result was an exodus of teachers from the profession, and those who remained reported challenges that impacted their personal and professional lives. The authors describe the effects on teachers and the impact that this had on them, including reasons why many departed from teaching. In closing, the authors offer recommendations to teacher preparation programs, districts, and schools.

This paper aims to explore how born global firms (BGs) in business to business (B2B) markets balance the mix of physical and virtual networking through social media to gain access to resources throughout their internationalization process. Further, the research seeks to understand these firms’ network dynamics in their continual adaptation to changing resource requirements throughout the internationalization process.

This research adopts a multiple case study approach based on in-depth interviews with founders and key informants in BGs in the software industry, supplemented with extensive secondary data.

Results reveal that networking through social media is efficient and can be used to access vital resources and to build trusting relationships in a B2B context, yet in combination with physical networking. This research found several boundary conditions related to the mix of physical and virtual networking, i.e. the relationship culture in the industry, strategic importance of partner/customer, type of product complexity and managers’ perceptions of the usefulness of social media in a B2B context. These conditions could evolve over time.

This research contributes to enhanced understanding of how resource-constrained BGs balance the mix of physical and virtual networking through social media in a B2B context, influenced by boundary conditions, achieving a network dynamic to advance internationalization.

This paper aims to explore the specific causes of failure among Malaysian female entrepreneurs who were provided with financial services by the microfinance institution: Amanah Ikhtiar Malaysia (AIM) to start up their own businesses.

This paper adopts a qualitative-based case study design approach, with data collected from a total of 18 female entrepreneurs who had failed to develop their businesses. In-depth personal interviews were conducted, coupled with personal observation via purposive cum snowball sampling.

Thematic analysis revealed a pattern-based outcome which discloses a variety of causes affecting the failure of Malaysian female entrepreneurship. These causes ranged from inter-related external factors which were perceived as beyond their control, such as personal life events, intensive competition and loan inflexibility to internal causes, which were related to lack of resources, poor financial management and personal dissatisfaction with their own business performance.

The findings of this study provide valuable information for Malaysian economic policymakers in how to practically address the objectives of the National Women's Policy (NPW) and improve the innovative quality of their products and services. A thorough understanding of the specific obstacles facing female entrepreneurs in Malaysia is essential if policymakers are to improve opportunity exploitation efficiency and assist in mitigating the external and internal causes of business failure among Malaysian females.

Studies in this field have demonstrated that most new “start-ups” fail within three years of their establishment. While determinist, emotive and voluntarist theories can often provide an adequate explanation for the causes of business failure, it is clear that no single factor is usually responsible. Rather, multiple interrelated factors are found to be at play. This study, therefore, provides an integrative model for causes of business failure among small-business female entrepreneurs. It also represents one of only a few such studies in the literature and, to the best of knowledge at the time of writing, is the first such study that used an integrative approach to explain the causes of business failure in the Malaysian context.

This paper develops a robust hospital readmission prediction framework by combining the feature selection algorithm and machine learning (ML) classifiers. The improved feature selection is proposed by considering the uncertainty in patient's attributes that leads to the output variable.

First, data preprocessing is conducted which includes how raw data is managed. Second, the impactful features are selected through feature selection process. It started with calculating the relational grade of each patient towards readmission using grey relational analysis (GRA) and the grade is used as the target values for feature selection. Then, the influenced features are selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method. This proposed method is termed as Grey-LASSO feature selection. The final task is the readmission prediction using ML classifiers.

The proposed method offered good performances with a minimum feature subset up to 54–65% discarded features. Multi-Layer Perceptron with Grey-LASSO gave the best performance.

The performance of Grey-LASSO is justified in two readmission datasets. Further research is required to examine the generalisability to other datasets.

In designing the feature selection algorithm, the selection on influenced input variables was based on the integration of GRA and LASSO. Specifically, GRA is a part of the grey system theory, which was employed to analyse the relation between systems under uncertain conditions. The LASSO approach was adopted due to its ability for sparse data representation.