A sensemaking perspective on network pictures

This paper examines the concept of network pictures through the lens of the organizational sensemaking perspective. Essentially it develops the concept of network pictures by suggesting we think of them as exercises in sensemaking. It does so by providing an introduction to organizational sensemaking before establishing a degree of commensurability between network pictures and sensemaking. It suggests that what we may then see more clearly is that the concept of network pictures needlessly gets involved in reification when talking of ‘the’ network picture whereas a more dynamic approach leads to ideas of network picturing in which the complete discrediting or collapse of extant sensemaking and network pictures provides a research opportunity that could be jointly explored by both perspectives. The paper concludes that paradoxically ‘finding’ this new sense or new network picture appropriate to radically changed times is facilitated by a process that involves first ‘becoming lost’.     

Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic

Online reviews remain important during the COVID-19 pandemic as they help customers make safe dining decisions. To help restaurants better understand customers’ needs and sustain their business under current circumstance, this study extracts restaurant features that are cared for by customers in current circumstance. This study also introduces deep learning methods to examine customers’ opinions about restaurant features and to detect reviews with mismatched ratings. By analyzing 112,412 restaurant reviews posted during January-June 2020 on Yelp.com, four frequently mentioned restaurant features (e.g., service, food, place, and experience) along with their associated sentiment scores were identified. Findings also show that deep learning algorithms (i.e., Bidirectional LSTM and Simple Embedding + Average Pooling) outperform traditional machine learning algorithms in sentiment classification and review rating prediction. This study strengthens the extant literature by empirically analyzing restaurant reviews posted during the COVID-19 pandemic and discovering suitable deep learning algorithms for different text mining tasks.     

A conceptual model to imply a negative innovation assessment framework on consumer behaviors through the electronic business platforms

Online retail platforms significantly impact consumers' purchasing decisions. Satisfaction, purchase intent, and repeat online shopping drive the platform's purchasing decisions. In contrast, individuals do not purchase products due to dissatisfaction and lack of purchasing intent. It will cause hesitation and spread negative feedback to influence online consumer behaviors. This research proposes a conceptual model incorporating the Status Quo Bias (SQB) and the Negative Online Purchase Decision-Making Process (NOPDMP) to suggest a new framework for evaluating the maintained consumer behavior of online shopping platforms. The questionnaires following the proposed conceptual model were collected from the sample data from participants of 384 experienced respondents using online shopping platforms. Data were analyzed for the causal relationship using Structural Equation Model. The implications of the assessment framework that incorporates the influence of negative factors can weigh the decision to purchase products and improve and reduce shopping cart abandonment on e-commerce platforms. This framework can also describe instances of negative perspectives regarding incentive alignments with actual behaviors.     

TOOR: A Novel Product Title Optimization Method Based on Online Reviews in E-commerce

Titles of online products play an important role in attracting consumers and promoting product sales in e-commerce. However, current online product titles only cover basic features and cannot reflect the preferences of consumers exactly. To address this problem, this research proposed an online title optimization method based on the analysis of online reviews, which is called TOOR (Title Optimization based on Online Reviews). In this research, we analyzed and compared product features extracted from online product titles and online reviews from the point of view of consumers and applied features extracted from reviews to title optimization. In order to verify the effectiveness of the proposed method, two experiments were conducted in this paper, selecting four typical smartphones as experiment samples and Taobao.com as the data resources. The experimental results indicated that features extracted from online reviews can better reflect the consumers’ concern, and the titles optimized by the TOOR method are more appealing to consumers and have higher click-through rates.     

共享经济的现代发展及其潜在问题:以共享单车为例的分析

共享经济减少了物品的闲置而最大限度地发挥了物品的功效,不仅可以增进生产者剩余和消费者剩余,而且也有利于打造新型的低碳环保的经济形态。但同时,共享经济的无序发展也带来了两大严重问题：（1）准公共品性质滋生出大量的搭便车行为,进而使得运营商受损;（2）准公共品性质还滋生出强烈的负外部性,进而使得运营商获利。进而,第一个问题的解决有赖于有效的治理机制,其中最佳机制是"强互惠"机制,而这依赖于社会网络的建设和文化伦理的培育;第二个问题的解决有赖于明确的责任界定,其中可行的措施是设立"维安基金",而这有赖于新的制度安排和立法思考。一般地,只有解决上述两大问题,才能全面衡量新兴共享经济的真实经济价值,进而才能更好地引导共享经济的有序发展。很大程度上,共享泡沫的产生和破灭都体现出市场创新的盲目性,从而就需要政府的积极规划和引导。     

网络技术负向价值的表象、析因与消解

任何技术都有价值的二重性,网络技术也不例外。透过网络技术在生态、社会和人本三个层面负向价值的表象,分析了导致网络技术负向价值产生的主客体原因,并针对这些主客体原因探讨了综合运用技术、法律和伦理方法来消解网络技术的负向价值以及这些消解方法之间的关系。     

The influence of internet customer reviews on the online sales and prices in hotel industry

In this paper, the impact of two service quality metrics (star rating and customer rating) on hotel room sales and prices is investigated. Two of the most popular tourist destinations in the world, Paris and London, are chosen. It is found that a higher customer rating significantly increases the online sales of hotels. The study results show that a 1% increase in online customer rating increases Sales per Room up to 2.68% in Paris and up to 2.62% in London. Contrary to expectations, higher stars do not increase the sales. It is also shown that higher customer ratings result in higher prices of the hotel and the prices of high star hotels are more sensitive to online customer ratings.     

Do consumers still believe what is said in online product reviews? A persuasion knowledge approach

The objective of this paper is to examine the effects of knowledge about manipulated online product reviews on the effectiveness of such reviews in influencing consumers' product evaluations. The results of two empirical studies show that consumers clearly differ with regard to their knowledge that product reviews can be manipulated and that consumers who have such knowledge are less influenced in their product evaluations by reviews, specifically by negative ones. Furthermore, the effects of negative reviews are even weaker when consumers acquire their knowledge through a highly credible source (compared to a less credible source).     

区域经济活动中地方政府行为的负外部性——兼论环北部湾(广西)经济区建设

在区域经济活动中,地方政府追求自身利益最大化的行为会产生明显的外部性。地方政府行为的负外部性与市场缺陷、政府理性、行政区划和政府职能等密切相关,它将妨碍经济资源在空间上的合理配置,影响区域经济一体化进程。努力克服地方政府行为负外部性,是提高地方政府行为效率,加快地方市场化建设步伐的重要手段,也是促进环北部湾(广西)经济区区域经济协调发展的重要内容。     

评房地产价格对国民经济的影响

房地产价格应当与居民的收入和经济发展水平相适应。房地产的价格过高无疑会使国民经济的发展受到严重阻碍。本文在深入分析房地产价格过高将不利于社会稳定、引起资源浪费、形成经济泡沫和引发金融风险等负面影响的基础上,提出稳定房价的相应调控对策。