How does justice matter in online retailers’ reputation and purchase intentions: an empirical study of China

In recent years, online shopping has been proliferated around the world. Online retailers’ reputation and purchase intentions are critical for survival and profitability of any online store. Thus, this study proposes a research framework to examine the perceived justice effects on customers purchase intention and online retailers’ reputation. A confirmatory factor analysis was conducted to demonstrate the reliability and validity of the measurement model, and the structural equation modelling technique was used to test the research model. The hypothesised model was validated empirically using data collected from 383 online shopping customers in China. The results indicated that perceived procedural, distributive and interactional justice components were strong predictors of customers purchase intention and online retailers’ reputation while online retailers’ reputation had significant effects on purchase intentions. Finally, theoretical and managerial implications are also presented in the paper.     

Keep your bots to yourself [software law]

There are now a variety of Web bots or spiders available to scour the Web to automatically gather information. Bots, sometimes called agents when they are instructed to perform particular searches, are useful in performing the time-consuming and repetitive searches required to gather comparison data or to find an elusive item. In some cases, agents can not only find information but can make a purchase or take another action based on the data they collect. Although bots are great for consumers, some Internet retailers hate them. Just as a brick and mortar store wants potential customers to walk in the door, Web retailers want potential customers to personally view their sites. Once there, consumers might purchase the initial products they were seeking and then see advertisements or buy accessories, upgrades, or unrelated additional items as well. Because bots can reduce or eliminate these possibilities, some Internet retailers are trying to stop them. It's one thing to be upset about bots, spiders, agents, and data miners, but it is quite another to put an end to the practice. It should come as no surprise that federal and state laws in the US do not expressly mention bots. Those seeking to challenge their use must resort to a variety of more generic laws, none of which fit very well     

Internet channel entry: retail coverage and entry cost advantage

In this research we study how existing market coverage affects the outcome of the Internet channel entry game between an existing retailer and a new entrant. A market is not covered when some consumers with low reservation prices are priced out by existing retailers and do not purchase. In a model with multiple existing retailers and a potential new entrant, we demonstrate that when entry costs are equal, one of the existing retailers enters the Internet channel first. However, if the market is covered by existing retailers before entry, then because of the threat of Internet channel entry by the potential new entrant, retailer entry cannibalizes existing retail profits—cannibalizing at a loss. In addition, if a potential new entrant has a slight advantage in Internet channel entry costs and the market is not covered by existing retailers, then the new entrant enters the Internet channel first. If the market is covered by existing retailers, then the new entrant must have a larger Internet channel entry cost advantage to be first to enter the Internet channel.

Price versus privacy: an experiment into the competitive advantage of collecting less personal information

In previous privacy studies, consumers have reported their unease with online retailers that collect a lot of personal data. Consumers claim they will switch to alternative providers or cancel transactions if data collection is deemed excessive. Therefore, privacy appears to be a competitive factor in electronic commerce. This paper describes a study which quantifies the degree to which privacy is a competitive advantage for online retailers. In an experiment, we offered 225 participants the option to purchase one DVD from one of two online stores. Throughout the study, one online shop asked for more invasive personal data—as confirmed by an exit-questionnaire. In the test treatment, the privacy-invasive store sold DVDs for one Euro less than the other, and in the control treatment, both stores sold DVDs for the same price. Across both treatments, 74 participants made a purchase and had the DVD they bought delivered. In our study we found that, when the price of DVDs was the same between both stores, the shop asking for less personal data did not amass the entire market. When consumers were offered a trade-off between price and privacy, the vast majority of customers chose to buy from the cheaper, more privacy-invasive, firm; this firm got both a larger market share and higher revenue. The cheaper shop generated strong dissatisfaction with their privacy practises; in contrast, consumers of the more expensive store displayed only weak dissatisfaction with price. We established the validity of our analysis by checking users made informed choices, and did not select one firm over the other due to hasty decision-making or ordering effects. We found no support for either a materialistic lifestyle nor the quest for immediate gratification as to why customers chose the cheaper but privacy-unfriendly store.     

Fusing and mining opinions for reputation generation

The Internet provides a convenient platform for people to freely share their opinions on any entities. The opinions expressed in natural languages carry the subjective attitudes and preferences of humans. They represent the public perspectives on any entity, thus impact user decisions and behaviors in some way. Therefore, opinions have been recognized as useful and valuable pieces of information for reputation generation. Fusing and mining opinions offer a promising approach to extract reputation information and track public perspectives. However, the literature lacks studies on this topic. In this paper, we propose a novel reputation generation approach based on opinion fusion and mining. In our approach, opinions are filtered to eliminate unrelated ones, and then grouped into a number of fused principal opinion sets that contain opinions with a similar or the same attitude or preference. By aggregating the ratings attached to the fused opinions, we normalize the reputation of an entity. Meanwhile, various types of recommendations can be generated based on relationships among opinions. To offer sufficient reputation information to users, we also propose a new way of reputation visualization. It shows the details of opinion fusing and mining results, such as the normalized reputation value, principal opinions with popularity and other statistics. Experimental results coming from an analysis of big real-world data collected from several popular commercial websites in both English and Chinese demonstrate the generality and accuracy of the proposed approach, especially the effectiveness of opinion filtering for reputation generation. A small-scale real-world user study further quantifies the user acceptance of the developed reputation visualization method. In the sequel, this implies that the proposed approach can be applied in practice to generate reputation.     

Optimizing sunk investments in e-commerce: a quality assurance challenge for small businesses

In a traditional retail environment, the reputation value of a brand name leads profitable firms with established brands to work hard to maintain quality. A store may rely on its reputation to signal that its goods are of high quality. A retailer that expects repeated purchases by a consumer – if it provides high-quality products – has a strong incentive not to provide defective products or poor service. Translating this type of quality assurance into digital sales media poses unique challenges, particularly for small businesses. Retailers that have only a local geographic reputation require innovative strategies to convince a potential global market that the products or services they provide are of appropriate quality when they make the move into electronic commerce retailing. This paper provides a model that identifies a number of unique factors that should be considered when estimating the optimal level of investment into an e-commerce initiative. Decisions of this type are particularly difficult for small businesses because they may lack expertise in digital marketing and sales and they may have insufficient resources required for ideal levels of investment. These constraints have led to some creative cost and risk minimizing solutions that have been adopted by certain small businesses in their effort to make the transition into the digital marketplace. Published online: 22 August 2001     

TRIMS,a privacy-aware trust and reputation model for identity management systems

Electronic transactions are becoming more important everyday. Several tasks like buying goods, booking flights or hotel rooms, or paying for streaming a movie, for instance, can be carried out through the Internet. Nevertheless, they are still some drawbacks due to security threats while performing such operations. Trust and reputation management rises as a novel way of solving some of those problems. In this paper we present our work TRIMS (a privacy-aware trust and reputation model for identity management systems), which applies a trust and reputation model to guarantee an acceptable level of security when deciding if a different domain might be considered reliable when receiving certain sensitive user’s attributes. Specifically, we will address the problems which surfaces when a domain needs to decide whether to exchange some information with another possibly unknown domain to effectively provide a service to one of its users. This decision will be determined by the trust deposited in the targeting domain. As far as we know, our proposal is one of the first approaches dealing with trust and reputation management in a multi-domain scenario. Finally, the performed experiments have demonstrated the robustness and accuracy of our model in a wide variety of scenarios.     

Price competition in e-tailing under service and recognition differentiation

The Internet has significantly increased the bargaining power of consumers. Many online shopping search engines allow consumers to find most retailers that sell a specific product, compare product prices, and review detailed store ratings. With competition just a click away, online retailers have little control over where consumers would shop. Offering the lowest price alone does not always guarantee that consumers will come and buy at your site. Other non-price attributes, such as service quality and a merchant’s brand recognition, also play important roles in helping online retailers to build competitive advantages. In this paper, we present a model of price competition that assumes e-tailers can mainly differentiate themselves by providing different levels of service and by establishing a different online recognition. Closed-form equilibrium solutions are obtained for the different scenarios that may arise in this model. Based on such solutions, we give managerial insights on how e-tailers should position themselves when parameters such as service cost, service levels, and recognition are varied.     

Lessons Learned from an Initial E-Commerce Failure by A Catalog Retailer

As companies rushed into E-commerce in the late 1990s, many predicted that existing catalog retailers would have a natural advantage over physical store retailers. However, confusing market signals and incorrect management assumptions led to some poor decisions. This article explores what went wrong with a successful catalog retailer, Fingerhut Inc., as it initially embraced E-commerce in the late 1990s, and some of the lessons learned.     

Is paid search overrated? When bricks-and-mortar-only retailers should not use paid search

While bricks-and-mortar-only retailers do not offer online purchasing, they often take advantage of multi-channel management strategies to reach consumers in a pre-purchase phase. We investigate whether paid search can increase the sales of brick-and-mortar retailers who promote their offers via an informational website. Although a sizeable one third of all retailers still trade without an online-shop, previous work has been silent about the effects of paid search for them. We make use of a randomized field experiment and an end-to-end tracking mechanism to investigate the cross-channel behavior of individual consumers. Our empirical results suggest that, whilst paid search increases the number of potential customers through enhancing the reach of marketing initiatives, store sales are not increased. We conclude that customers who search online to buy offline primarily use paid search as a navigational shortcut to the retailer’s website. Consequently, bricks-and-mortar-only retailers seeking to increase store purchases should approach paid search with caution.