Does Accessibility Affect Retail Prices and Competition? An Empirical Application

This paper attempts to link the concepts of accessibility and the firm’s conduct in the regional retail market in Spain. We use a database that includes sale price, service station location, level of traffic and type of road. We show that accessibility has two main effects on final prices. The accessibility of own-brand gas stations increased their prices while the accessibility of rival gas stations causes price reductions. If we include the value of time, then no rational consumer should travel further than his nearest petrol station in search of lower prices. Finally, our paper shows that service stations can establish a dominant position if consumers do not have access to other retailers within a 17-min radius.     

Interorganizational information sharing in the airline industry: An analysis of stock market responses to code-sharing agreements

This paper addresses the issue of inter-organizational information sharing alliances and their impacts on firm values from the perspective of inter-organizational coordination between partners in the airline industry setting. We investigate the shareholder wealth effects of inter-organizational information-sharing alliance arrangements, using 131 code-sharing agreements in the airline industry during 1984–1997. Employing event study methodology we found that the information sharing alliances between similar partners did create positive value in terms of stock returns at the time of alliance announcements to major US airlines. However, alliances between dissimilar partners resulted in significant losses of shareholder value to the major airlines. These results strongly support our main hypotheses that information-sharing alliances are successful and the benefits of such alliances are realized only when the coordination difficulties can be effectively dealt with.     

Intelligent product search with soft-boundary preference relaxation

This paper proposes a novel method for preference relaxation in online product search, which enables consumers to make quality choices without suffering from the commonly experienced information overload. In online shopping scenarios that involve multi-attribute choice tasks, it can be difficult for consumers to process the vast amounts of information available and to make satisfactory buying decisions. In such situations consumers are likely to eliminate potentially good choices early on, using hard-constraint filtering tools. Our approach uses edge sets to identify the alternatives on the soft boundary and the principle of alternative domination to suppress the alternatives on this boundary that are irrelevant. We demonstrate how our approach outperforms existing methods for product search in a set of simulations using two sets of 2650 car advertisements and 1813 digital cameras gathered from a popular online store.     

Simulating Sequential Search Models with Genetic Algorithms: Analysis of Price Ceilings,Taxes, Advertising and Welfare

This paper studies advertising, price ceilings and taxes in a sequential search model with bilateral heterogeneities in production and search costs. We simulate equilibria using a genetic algorithm (GA) applied to over 100 market scenarios, each differing based on the number of firms, number of consumers, existence of price ceilings or taxes, costs of production, costs of advertising, consumers’ susceptibility to advertising and consumers’ search costs. We study the interaction of advertising and search and analyze the welfare effects of advertising, price ceilings and sales taxes. Despite advertising being uninformative, we find that firms charging lower prices tend to advertise more intensely and that price ceilings and advertising can improve welfare if search costs are high.     

Supplying Web 2.0: An empirical investigation of the drivers of consumer transmutation of culture-oriented digital information goods

This paper describes an empirical study of behaviors associated with consumers’ creative modification of digital information goods found in Web 2.0 and elsewhere. They are products of culture such as digital images, music, video, news and computer games. We will refer to them as “digital culture products”. How do consumers who transmute such products differ from those who do not, and from each other? This study develops and tests a theory of consumer behavior in transmuting digital culture products, separating consumers into different groups based on how and why they transmute. With our theory, we posit these groups as having differences of motivation, as measured by product involvement and innovativeness, and of ability as measured by computer skills. A survey instrument to collect data from Internet-capable computer users on the relevant constructs, and on their transmutation activities, is developed and distributed using a web-based survey hosting service. The data are used to test hypotheses that consumers’ enduring involvement and innovativeness are positively related to transmutation behaviors, and that computer self-efficacy moderates those relationships. The empirical results support the hypotheses that enduring involvement and innovativeness do motivate transmutation behavior. The data analysis also supports the existence of a moderating relationship of computer self-efficacy with respect to enduring involvement, but not of computer self-efficacy with respect to innovativeness. The findings further indicate that transmutation activities should be expected to impact Web 2.0-oriented companies, both incumbents and start-ups, as they make decisions about how to incorporate consumers into their business models not only as recipients of content, but also as its producers.     

Search product and experience product online reviews: An eye-tracking study on consumers' review search behavior

This study uses eye-tracking method to investigate consumers' online review search behavior by suggesting that it needs to consider the type of product reviewed. A review-product congruity proposition was testified through a self-report survey and an eye-tracking experiment. The proposition states that consumers of search products expect to seek attribute based reviews, while consumers shopping for experience products tend to seek experience based reviews. Two experiments were conducted in the human factors & ergonomics laboratory of Beihang University, China and all subjects are college students. The results of our first empirical experiment support our hypotheses by showing consumers' more active and positive responses to attribute based reviews when shopping for search products and to experience based reviews when purchasing experience products. The second experiment was conducted with eye tracking method to gain further insights. We found that consumers of search products are attracted and engaged more deeply by attribute based reviews. However, when they browse experience products, the difference of their fixations on experience based reviews and attribute based reviews is not significant, and thus the proposition is partially supported. This study extends our current understanding of consumers' online review search behavior by subsuming product type, which is necessary and helpful, and provides references on the classification and presentation of reviews to facilitate consumers' product judgement and decision making. Moreover, comparison of traditional empirical method and eye-tracking method can help deepen our understanding of complex consumer online shopping behavior.     

Simulation Exercise on Time Perception Parameters in Discrete-time Dynamic Assignment Models

In order to assess the performance of information systems to road users and of demand management measures, like staggered and/or flexible work times and road pricing, with respect to congestion prevention problems, it is necessary to simulate dynamics of traffic flows on the transport network and users' behaviour. This can be accomplished by taking into account several aspects such as users' information level, travel demand distribution over time and the expected smoothing effect of adopted measures and information systems. Dynamic assignment and arrival time choice models and computational procedures to perform this assessment for various traffic scenarios and hypotheses about users' behaviour are presented in this paper. Moreover, the indicators needed to quantify information systems and demand management effectiveness are identified together with parameters defining traffic scenarios. The main results obtained by applying these models and techniques as deterministic and probabilistic simulation tools are also presented and discussed.     

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.     

Online information privacy and price: A theoretical model and empirical tests

Information privacy has become an increasingly critical concern for many individuals, especially in the context of the increased collection, analysis, and visualization of personal information. Despite the importance of privacy in online transactions, most studies investigated the effects of privacy concerns on stated intentions. This study theoretically analyzes and empirically evaluates the link between privacy and price. In the proposed model, consumers are differentiated not only by their preferences for privacy but also by search costs. For simplicity, stores in the proposed model differ in terms of the price charged and the privacy protection level offered. As derived from the theoretical model and empirical validation, better privacy protection does not necessarily lead to the highest price, though it does allow stores to charge a premium price. Moreover, the price premium is higher if more consumers remain uninformed, indicating that uninformed consumers exert negative impacts on informed consumers.     

Capture,Annotate, Browse,Find, Share: Novel Interfaces for Personal Photo Management

The vision of ubiquitous digital photos has arrived. Yet, despite their broad popularity, significant shortfalls remain in the tools used to manage them. We believe that with a bit more creativity and effort, the photo industry can solve many of these problems, offering tools which better support accurate, rapid, and safe shared annotations with comfortable and efficient browsing and search. In this article, we review a number of projects of ours and others on interfaces for photo management. We describe the problems that we see in existing tools and our vision for improving them.     

Smart CloudBench—A framework for evaluating cloud infrastructure performance

Cloud migration allows organizations to benefit from reduced operational costs, improved flexibility, and greater scalability, and enables them to focus on core business goals. However, it also has the flip side of reduced visibility. Enterprises considering migration of their IT systems to the cloud only have a black box view of the offered infrastructure. While information about server pricing and specification is publicly available, there is limited information about cloud infrastructure performance. Comparison of alternative cloud infrastructure offerings based only on price and server specification is difficult because cloud vendors use heterogeneous hardware resources, offer different server configurations, apply different pricing models and use different virtualization techniques to provision them. Benchmarking the performance of software systems deployed on the top of the black box cloud infrastructure offers one way to evaluate the performance of available cloud server alternatives. However, this process can be complex, time-consuming and expensive, and cloud consumers can greatly benefit from tools that can automate it. Smart CloudBench is a generic framework and system that offers automated, on-demand, real-time and customized benchmarking of software systems deployed on cloud infrastructure. It provides greater visibility and insight into the run-time behavior of cloud infrastructure, helping consumers to compare and contrast available offerings during the initial cloud selection phase, and monitor performance for service quality assurance during the subsequent cloud consumption phase. In this paper, we first discuss the rationale behind our approach for benchmarking the black box cloud infrastructure. Then, we propose a generic architecture for benchmarking representative applications on the heterogeneous cloud infrastructure and describe the Smart CloudBench benchmarking workflow. We also present simple use case scenarios that highlight the need for tools such as Smart CloudBench.     

Representation of time in digital calendars: An argument for a unified,continuous and multi-granular calendar view

Digital calendars have been heavily influenced by the design of the physical calendar and have invariably adopted their grid representation of days in the month. We argue that the alternative of a continuous list representation of successive days would offer several advantages such as faster calendar search, a more natural linear view of time, a scrollable and zoomable interface and better scalability for devices of different size. This alternative, linear calendar appears to be well suited to modern touch-centric platforms with their refined support for scrolling and zooming.We tested search performance and navigation with digital calendars in a comparison of grid and list representations by employing a remote, web-based method. On their personal computers, participants performed a series of search tasks in a fictitious calendar. The results show that calendar search is faster in list view when searching for dates, between month breaks and in the next month (with and without navigation). Searching for days is faster in grid view, however, highlighting days in list view eliminates this difference. The results indicate substantial promise for the list view digital calendar and we describe a high fidelity rendering of the user interface for a digital calendar with a list view.     

Finding consensus in speech recognition: word error minimization and other applications of confusion networks

We describe a new framework for distilling information from word lattices to improve the accuracy of the speech recognition output and obtain a more perspicuous representation of a set of alternative hypotheses. In the standard MAP decoding approach the recognizer outputs the string of words corresponding to the path with the highest posterior probability given the acoustics and a language model. However, even given optimal models, the MAP decoder does not necessarily minimize the commonly used performance metric, word error rate (WER). We describe a method for explicitly minimizing WER by extracting word hypotheses with the highest posterior probabilities from word lattices. We change the standard problem formulation by replacing global search over a large set of sentence hypotheses with local search over a small set of word candidates. In addition to improving the accuracy of the recognizer, our method produces a new representation of a set of candidate hypotheses that specifies the sequence of word-level confusions in a compact lattice format. We study the properties of confusion networks and examine their use for other tasks, such as lattice compression, word spotting, confidence annotation, and reevaluation of recognition hypotheses using higher-level knowledge sources.     

The role of online seller reviews and product price on buyers' willingness-to-pay: a risk perspective

Third-party interpersonal communications such as online seller reviews play an important role in buyers’ purchase decisions in online markets. Although it is empirically clear that seller reviews (volume and valence) and product price contribute to buyers’ willingness-to-pay (WTP) differently across various studies, it is theoretically less understood why such effects qualitatively differ (e.g., positive vs negative), rendering unclear managerial implications for online marketers. In this paper, we study the role of online seller reviews and product price in buyers’ WTP. We offer a conceptual framework from a risk perspective in which we argue that the different effects of seller reviews and product price on buyers’ WTP may emerge simultaneously in an online market. We highlight two important drivers for such qualitatively different effects: a difference in buyers’ risk attitudes (averse, neutral, or seeking) and a difference in WTP measures (absolute or relative). We test our hypotheses and find good support for them both internally (an experimental study) and externally (an empirical study). Our research enhances the understanding of the relationship between online user reviews and online price dispersions while shedding light on better management of online user reviews for market makers.     

Pricing local search engines for company websites

Ever since the dramatic boom of the Internet, web search engines have appealed to both researchers’ and developers’ attention to a noticeable pitch. In order to enhance the website efficiency, a number of companies rent the company-wide local search engines from the search engine providers. Since web service renting serves as a promising alternative of web service purchase within the web industrial framework, how to effectively price such kind of search engines becomes an important as well as impending issue, with which practitioners and researchers confront. In this paper, we present a pricing model, which is based on the discrete-time independent incremental process, for the local search engines of the company website. The stopping time is defined in this work and the expected revenue for the web-search-engine providers over the rental horizon is also derived. Due to the considerable complexity and difficulty to obtaining an analytical solution for estimation of the expected revenue, the optimal monthly rental is discussed and exemplified through empirical experiments. By maximizing the revenue, two different strategies are investigated by allowing different initial lock-in periods and offering a coupon for waiving certain amount of fee for initial use. The experiments illustrate the best rental and sale price scenarios.     

Name-Your-Own-Price seller’s information revelation strategy with the presence of list-price channel

This study examines the Name-Your-Own-Price (NYOP) retailer’s information revelation strategy when competing with list-price channel. We propose an integrated economic framework focusing on the comparison of expected consumer surplus from bidding at NYOP auction and guaranteed consumer surplus from buying at a list price. We then conduct an empirical study to examine the effects of seller-supplied price information on NYOP bidding outcome (especially on expected winning probability and the number of bidders). The results of our study strongly indicate the effects of seller-supplied information on expected winning probability (as well as the expected consumer surplus) in a NYOP auction. We also illustrate the strategic implications of seller-supplied price information via a revenue simulation for the NYOP seller. Our results suggest that NYOP seller may increase his expected revenue by (1) provide only the upper bound of its threshold price when list price is high (low expected consumer surplus from buying at list price); (2) provide only the lower bound of its threshold price when list price is low (high expected consumer surplus from buying at list price); (3) provide both the upper and lower bound of its threshold price when consumer surplus of buying at list price is unknown.     

The study of perceived adverse effects of digital piracy and involvement: insights from adult computer users

In this study, we focus on two sets of expected negative consequences of engaging in digital piracy among the seldom studied adult computer users rather than student population. We delve into the role of perceived risk and moral intensity as drivers of consumers’ attitudes and behavioural intentions, and explore the rarely examined moderating effect of issue involvement on the relationship between the attitude and intention to pirate. The dominant attitude–behaviour theory is extended with an ethical decision-making theoretical perspective. The hypotheses are tested via mail survey data from a random sample of adult consumers using structural equations modelling. The results of this cross-sectional study show unfailing support for the relationships proposed in our model. Our findings suggest that, in addition to perceived risk, moral intensity (i.e. the expected consequences for others), has a particularly strong total effect on the intention to pirate, and that consumer involvement in illegally downloading files is a salient factor moderating the relationship between attitudes and behavioural intentions. Based on this pattern of results, we offer theoretical and practical implications.     

Searching for close alternative plans

Consider the situation where an intelligent agent accepts as input a complete plan, i.e., a sequence of states (or operators) that should be followed in order to achieve a goal. For some reason, the given plan cannot be implemented by the agent, who then goes about trying to find an alternative plan that is as close as possible to the original. To achieve this, a search algorithm that will find similar alternative plans is required, as well as some sort of comparison function that will determine which alternative plan is closest to the original. In this paper, we define a number of distance metrics between plans, and characterize these functions and their respective attributes and advantages. We then develop a general algorithm based on best-first search that helps an agent efficiently find the most suitable alternative plan. We also propose a number of heuristics for the cost function of this best-first search algorithm. To explore the generality of our idea, we provide three different problem domains where our approach is applicable: physical roadmap path finding, the blocks world, and task scheduling. Experimental results on these various domains support the efficiency of our algorithm for finding close alternative plans. A preliminary version of this paper appeared in the International Joint Conference on Autonomous Agents and Multiagent Systems (8). An erratum to this article can be found at     

Software reuse and competition: Consumer preferences in a software component market

Research on software reuse has traditionally focused on reuse among employees within the same organization. In contrast, our research investigates consumer preferences in a software component market with multiple producers and consumers of software components. We propose that empirically studying competition in software component markets is crucial to discovering success factors for improving software reuse. To facilitate such research, we have developed an experimental market laboratory called SofTrade; we report here the results of two empirical studies made possible by this environment. Our results support some existing reuse maxims, such as the concept that consumers prefer to reuse components that are general and flexible. Other established reuse beliefs are not supported by our results, such as the expectation that consumers will prefer objects with unique, complex features. Overall, the research reported here strongly supports the claim that the practice of exploring software reuse activity within real or artificial software component markets produces unique insights into how we can improve software reuse.