Opinion Mining from Online Reviews: Consumer Satisfaction Analysis with B&B Hotels

Given the enormous growth and significant impact of user generated content in online hotel reviews, this study aims to mining the determinants of consumer satisfaction with B&Bs and build a hierarchical structure of these determinants. Content analysis was conducted based on the consumer review data from two well-known hotel booking websites. Ten determinants of customer satisfaction were identified. The interpretive structural modeling (ISM) technique was then used to develop a five-level hierarchical structural model based on these determinants to illustrate the influencing paths. Finally, the cross-impact matrix multiplication applied to classification (MICMAC) technique was used to analyze the driver and dependence power for each determinant. This study has the potential to make significant contributions from both the theoretical and practical perspectives in this research area

Implication of Production Tax Credit on Economic Dispatch for Electricity Merchants with Storage and Wind Farms

The production tax credit (PTC) promotes wind energy development, reduces power generation costs, and can affect merchants\u27 joint economic dispatch, particularly for electricity merchants with both energy storage and wind farms. Two common PTC policies are studied – in the first policy, a wind farm receives PTC by selling wind generation to the market and its storage can be used to store energy from the wind generation and energy purchased from the grid but the energy released from the storage cannot receive PTC; in the second policy, the energy released from the storage can also qualify for PTC but purchasing energy from the grid is not allowed. We then employ dynamic programming to study merchants\u27 optimal decision-making while considering PTC and the physical characteristics of storage systems. We analytically show that the state of charge (SOC) range can be segmented into different regions by SOC reference points under two PTC policies. The merchant\u27s optimal action can be conveniently and uniquely determined based on the region within which the current SOC falls. Moreover, this study illustrates that PTC could substantially alter the optimal scheduling policy structures by affecting reference points and their relationships. The results showed that the frequencies for charging and discharging storage decisions decreased with an increase in PTC subsidy. Last, we confirm that, although the first policy allows merchants to buy electricity from the market, the second policy can bring more profits when the PTC is large at the current PTC rates. The findings can provide multistage decision-making guidance to electricity merchants in the wholesale power market

The effect of dynamic information cues on sales performance in live streaming e-commerce: an IFT and ELM perspective

Compared with traditional e-commerce, the advanced technology of live streaming e-commerce provides more dynamic information cues to enable viewers to make better decisions. Drawing on the information foraging theory (IFT) and elaboration likelihood model (ELM), we construct a synthetic model by considering how live streaming information cues influence viewers’ decision making via two distinct routes (central and peripheral). Using the technology of web crawling and text mining, Douyin’s live streaming data were collected, transformed, and then analyzed by fixed-effect regression. The results indicated that product interpretation duration, popularity cue, and herding information are significantly associated with sales performance in live streaming e-commerce. This study enriches our knowledge about live streaming e-commerce, extends the application of IFT and ELM in the individuals’ information foraging and processing by using objective data in the live streaming e-commerce context, and offers practical suggestions to live streaming e-commerce practitioners

Examining consumers’ behavioral intention in O2O commerce from a relational perspective: an exploratory study

Online-to-offline (O2O) commerce is a new e-business model that is popular among consumers and profitable for e-vendors. However, limited studies have been conducted to understand consumer behavior in this context. Based on commitment–trust theory and trust transfer theory, we conducted an exploratory study to explore consumers’ repurchase intention and sharing intention in the O2O commerce context from the relational viewpoint. Two studies were conducted using a web-based survey and Partial Least Squares were used to analyze the data. In Study 1, the results indicated that various targets of trust and commitment have significant effects on repurchase intention and sharing intention. Trust can be transferred both inter-channel and intra-channel in O2O commerce. Moreover, the effect of trust in O2O platforms on commitment is mediated by trust in user community and trust in merchants. To demonstrate the generalizability and external robustness of the results, we replicated the research model in Study 2 using data from a more representative sample. The replicated study produced similar results. This research provides an initial understanding of consumer behavior in the O2O commerce context and contributes to trust transfer theory and commitment–trust theory. Further, this research benefits companies undertaking O2O business by enabling them to better understand how to improve consumer repurchasing intention and sharing intention to succeed in the e-business industry

Exploring the Potential Use of Near-Miss Information to Improve Construction Safety Performance

Construction project management usually has a high risk of safety-related accidents. An opportunity to proactively improve safety performance is with near-miss information, which is regarded as free lessons for safety management. The research status and practice; however, presents a lack of comprehensive understanding on what near-miss information means within the context of construction safety management. The objective of this study is to fill in this gap. The main findings enrich the comprehensive understanding of the near-miss definition, the near-miss causation model, and the process of near-miss management. Considering that near-misses are more tacit and obscure than accidents, the process for near-miss management involves eight stages: discovery, reporting, identification, prioritization, causal analysis, solution, dissemination, and evaluation. The first three stages aim to make near-misses explicit. The other five are adopted to better manage near-miss information, compiled in a well-designed near-miss database (NMDB). Finally, a case study was conducted to show how near-miss information can be utilized to assist in construction safety management. The main potential contributions here are twofold. Firstly, corresponding findings provide a knowledge framework of near-miss information for construction safety researchers who can go on to further study near-miss management. Secondly, the proposed framework contributes to the guidance and encouragement of near-miss practices on construction sites

Microblog Sentiment Analysis Using User Similarity and Interaction-based Social Relations

Microblog Sentiment Analysis has become a popular research topic extensively examined in the literature. However, microblogging messages are usually short, unstructured, contain less information and much noise, creating a significant challenge for the application of traditional content-based methods. In this study, we propose a novel method, MSA-USSR, where user similarity information and interaction-based social relations information are combined to build sentiment relationships between microblogging data. We employ these microblog–microblog sentiment relations to train the sentiment polarity classifier. The experimental results on two Sina-Weibo datasets show that our model has a better sentiment classification accuracy and F1-score than the Support Vector Machine method and the state-of-the-art supervised model called SANT. In addition, it was proven that the improvement in accuracy brought by interaction-based social relations information is greater than the user similarity information, but MSA-USSR achieved the best performance when incorporating both user similarity information and users’ interaction-based social relations