Typically, a firm's objectives include establishing consumer confidence, preserving its brand image, and developing a profitable business strategy. Consumers now place greater emphasis on the sustainability and transparency of their purchases. Given environmental and economic limitations, firms are often compelled to implement sustainable production methods. This is especially a struggle for small- and medium-sized enterprises (SMEs) with new technology as it can increase their risk of failure. This has led to a problem for consumers, who must cross-check the sustainability-related claims of firms they buy from. This is challenging because of limited process trace data and restricted enforcement capabilities. This study expands the knowledge of product sustainability and transparency. It aids our understanding of how SMEs develop prototypes and pilot solutions using blockchain and the Internet of Things (IoTs). To fulfill this aim, we discuss the experiences of three SMEs in two design domains: home decor and fashion goods. During the prototype and pilot phases, we used an iterative approach to match the firms' solution specifications and ensured that they did not replace but instead complemented the SME's legacy systems. Our pilot solutions supported SMEs to achieve their desired sustainability goals. Users could see the amount of recycled plastic used and the total C O 2 reduction after scanning a QR code. Upon scanning a QR code for a particular item, its origins and associated geographical details are displayed. The implementation of transparency involved monitoring the mobile application's popularity, where more than 10,000 clicks were documented for each product upon its introduction.

Consumer behavior patterns related to home digital media use are changing due to technological innovations. We examine them in the presence of two-way cable television (CATV) set-top boxes. They permit viewers to change channels, switch to the Internet, and order paid programming, among other functions. We focus on micro-level data that are generated from consumer CATV viewing behavior. We capture clickstreams of channel-changing behavior when consumer use a remote control handset to interact with the set-top box in their home. We explore a variety of data analytics results that characterize patterns of consumer channel-switching behavior, as a basis for suggesting different clusters of observed behavior. We also probe the explanatory elements that give rise to what we see.

We discuss a two-stage mechanism for e-procurement operations, which implements a multi-attribute combinatorial auction in first stage, followed by bargaining in the second stage. We find that an important difference between single-unit and heterogeneous e-procurement auctions is the existence of different types among the winning suppliers. In the auction stage, we discuss incentive-compatible bidding strategies for the procurement suppliers, and how the buyer should go about solving the winner determination problem. In the bargaining stage, the buyer can implement a strategy that views the winning suppliers as though they are in two different groups. We derive the decision conditions for the buyer's different procurement strategies. The most important finding is that, compared with classical Vickery-Clarke-Groves mechanism, the proposed mechanism improves social welfare.

We use hedonic valuation to establish a basis for valuing the time a user participates in an online gaming community. We distinguish between utilitarian and hedonic value. Our application involves elements of both utilitarian and hedonic value, however, the latter is much more important. We use an extensive data set involving a massive multi-player online role-playing game (MMORPG) from Korea. We specify and test econometric models that reflect the appropriate empirical structure for the setting, based on information about the players and the activities they engage in. The results permit us to estimate the hedonic value in monetary terms for the use of this mechanism per minute of participant use. We explore how such information can be used to create the appropriate incentives via optimal participation fees and subsidies to maximize consumer value.

The recent launch of Google Wallet has brought the issue of technology solutions in the mobile payment (m-payment) area to the forefront. In deciding whether and when to adopt m-payment technology, senior managers in banks are naturally concerned about uncertainties regarding future market conditions, technology standards, and consumer and merchant responses, especially their willingness to adopt. This study applies economic theory and modeling for decision-making under uncertainty to bank investments in mobile payment technology. We assess the projected benefits and costs of investment as a continuous-time stochastic process to determine optimal investment timing. We find that the value of waiting to adopt jumps when the related business environment experiences relevant shocks. Our analysis shows that when the volatility of the expected payoff, the time horizon for decision-making, and timeframe of the choice changes, the recommended investment timing will change too. We also consider how network effects influence managerial decision-making for this IT investment analysis context.

Information technology (IT) services are often subject to down-ward price pressures due to improvements in technology and business processes in a competitive market. When clients enter into IT services contracts, they are faced with the future risk that their services will be overpriced relative to the broader IT services market. To mitigate this risk, clients often add benchmark provisions, whereby a neutral third party assesses the prevailing market price for services. It will support fair price adjustments if the market prices are lower than the current prices. We model the decision to benchmark in order to provide managerial information on the value of benchmark provisions. We ground the model empirically with data from a leading IT service provider.

Consumers in e-commerce acquire information through search engines, yet to date there has been little empirical study on how users interact with the results produced by search engines. This is analogous to, but different from, the ever-expanding research on clickstreams, where users interact with static web pages. We propose a new network approach to analyzing search engine server log data. We call this searchstream data. We create graph representations based on the web pages that users traverse as they explore the search results that their use of search engines generates. We then analyze the graph-level properties of these search network graphs by conducting cluster analysis. We report preliminary evidence the presence of heterogeneity among users in terms of how they interact with search engines. This suggests that search engine users may not all benefit from the same functionality in the search engines they rely upon. We also offer additional evidence on the empirical regularities associated with a variety of relevant issues that arise in the business-to-business (B2B) e-market context that we have studied.

Technological advancements have transformed the auction markets, where investing in collectibles, like rare coins, is more akin to buying and selling stocks. There can be market prices established, with investors scouring the online auctions, such as eBay, where millions of transactions occur daily. Just as with stocks, investors have the ability to purchase these collectibles without leaving their home or office, and can search, compare, buy, and sell collectibles. Also similar to the stock market, investors can look for inefficiencies to exploit. This multi-year exploratory study investigates the level of efficiency in online auctions over five years. We find evidence that efficiency is tied to the ratio of bidders to auctions, and that with a sufficient number of auctions, the online auction market can approach efficiency, affording purchasers some protection from overpaying. Our findings also suggest when investors can best search online auction markets looking for profitable inefficiencies to exploit.

IT is a proven driver of economic growth and social development. Recent large-scale adoption of digital wireless phones among developed and developing countries has driven the expectation that high penetration will eventually pay off in economic returns and social benefits. We examine growth models to discover diffusion patterns of analog and digital wireless phones in the global context. We ask: What models might characterize diffusion growth for analog and digital wireless phones? Do developed and developing countries experience different growth patterns? Do analog and digital technologies follow different diffusion patterns? We use a matched sub-sample of twenty countries, with an equal share of developed countries and other less well-developed countries. We fitted internal, external and mixed influence diffusion models, models that allow asymmetric diffusion patterns around an inflection point for growth, and also models with a flexible inflection point for growth. The results provide a rich source of insights on wireless phone diffusion across its two key technological generations

We study the impact of a set of industry, firm- and e-commerce-related factors on Internet firm survival. Through the use of one age-based and another calendar time-based Bayesian dynamic model, we are able to examine how the impact of these factors changes over time. Our results are based on data from 115 publicly-traded Internet firms and suggest that Internet firm survival depends on different factors at the different stages in their lifetimes. Early on, an Internet firm's likelihood of survival will be higher when the initial public offerings rate of Internet stocks in the market is high and when the firm has abundant financial capital. As firms grow, their survival is increasingly contingent on their financial capital and size. Our empirical results show that from the beginning of 2001 to the end of 2002, Internet firms experienced increasing pressure on survival from their debts and from labor expenses to pay its senior executives. In addition, Internet firms start to compete based on size in late 2002 as the sector grew. We obtained these findings using new econometric methods from Bayesian statistics.

This research explores the reasons why electronic commerce practices have diffused around the world at the national level of analysis, due to forces that are both internal and external to the countries in which diffusion is observed to occur. We propose two related growth theories that contribute contrasting viewpoints about the origins of the drivers of diffusion: endogenous growth theory and exogenous growth theory. We use the former theory to argue that the primary drivers of diffusion stem from economic forces that are internal to a country, including its government policies (e.g., tax on online stores), wealth, infrastructure, readiness, education levels, and so on. The latter theory suggests that the primary drivers are external to the economy, and may reflect the forces present in the regional economy, international drivers of technological change, trade flows and so on. We explore the efficacy of these alternative perspectives in a set of propositions for the drivers of national-level diffusion of e-commerce that spans developed and developing nations.

Online auctions allow the seller to remain anonymous and to easily change identities. Buyers must rely on the seller's description of a product and ability to deliver the product as promised. Internet auction environments make opportunistic behavior more attractive to sellers because the chance of detection and punishment is decreased. In this research, we show how fee structures at eBay, the largest online auction house, motivate shilling behavior. We distinguish between two different types of shilling that exhibit different motivation and behavior: shilling can be used to make the bidders pay more for an item, competitive shilling, and shilling that can be used to avoid paying auction house fees, reserve price shilling. We then use auction data gathered using an Internet-based data collection software agent to examine reserve price shilling using a probit model. We give evidence of reserve price shilling and then show factors that lead to this behavior.

Prior research has shown that achieving high levels of software reuse in the presence of repository and object-based computer-aided software engineering (CASE) development methods presents interesting human, managerial and technical challenges. This article presents research that seeks to enhanced software development performance through reuse. We propose automated support for developers who search large repositories for the appropriate reusable software objects. We characterize search for repository objects in terms of a multistage model involving screening, identification, and the subsequent choice between new object construction or reusable object implementation. We propose automated support tools, including ORCA, a software Object Reuse Classification Analyzer, and AMHYRST, an Automated HYpertext-based Reuse Search Tool, that are based on this model. ORCA utilizes a faceted classification approach that can be implemented using hypertext. We also describe an aspect of AMHYRST's architecture which can automatically create hypertext networks that represent and link objects in terms of a number of distinguishing features. We illustrate our approach with an example drawn from a real world object repository.

This paper focuses on a specific kind of interorganizational system — shared electronic banking networks — and identifies firm characteristics that are likely to influence the perceived business value of network membership and thus lead to early adoption. We undertake an exploratory econometric analysis of the adoption of Yankee 24, a large shared electronic banking network in the northeastern USA. Using Bass's analytical diffusion model, we categorize Yankee 24 members into earlier and later adopters. Probit models are estimated to assess the impact of explanatory variables on network adoption. We find that a bank's total demand deposits have a positive impact on early adoption, while the number of a bank's branch offices, a proxy for the size of its proprietary network, has a negative impact on early adoption.

Measurement of software development productivity is needed in order to control software costs, but it is discouragingly labor-intensive and expensive. Computer-aided software engineering (CASE) technologies/spl minus/especially repository-based, integrated CASE/spl minus/have the potential to support the automation of this measurement. We discuss the conceptual basis for the development of automated analyzers for function point and software reuse measurement for object-based CASE. Both analyzers take advantage of the existence of a representation of the application system that is stored within an object repository, and that contains the necessary information about the application system. We also discuss metrics for software reuse measurement, including reuse leverage, reuse value, and reuse classification that are motivated by managerial requirements and the efforts, within industry and the IEEE, to standardize measurement. The functionality and the analytical capabilities of state-of-the-art automated software metrics analyzers are illustrated in the context of an investment banking industry application that is similar to systems deployed at the New York City-based investment bank where these tools were developed and tested.>

The use and benefits of repository evaluation of software reuse are illustrated through an analysis of the evolving repositories of two large firms that recently implemented integrated CASE development tools. The analysis shows that these tools have supported high levels of software reuse, but it also suggests that there remains considerable unexploited reuse potential. The findings indicate that organizational changes will be required before the full potential of the new technology can be realized.>