Oliver Hinz

Seeding Strategies for Viral Marketing: An Empirical Comparison

Seeding strategies have strong influences on the success of viral marketing campaigns, but previous studies using computer simulations and analytical models have produced conflicting recommendations about the optimal seeding strategy. This study compares four seeding strategies in two complementary small-scale field experiments, as well as in one real-life viral marketing campaign involving more than 200,000 customers of a mobile phone service provider. The empirical results show that the best seeding strategies can be up to eight times more successful than other seeding strategies. Seeding to well-connected people is the most successful approach because these attractive seeding points are more likely to participate in viral marketing campaigns. This finding contradicts a common assumption in other studies. Well-connected people also actively use their greater reach but do not have more influence on their peers than do less well-connected people.

The Impact of Information Diffusion on Bidding Behavior in Secret Reserve Price Auctions

The interactive nature of the Internet promotes collaborative business models (e.g., auctions) and facilitates information-sharing via social networks. In Internet auctions, an important design option for sellers is the setting of a secret reserve price that has to be met by a buyers bid for a successful purchase. Bidders have strong incentives to learn more about the secret reserve price in these auctions, thereby relying on their own network of friends or digital networks of users with similar interests and information needs. Information-sharing and flow in digital networks, both person-to-person and via communities, can change bidding behavior and thus can have important implications for buyers and sellers in secret reserve price auctions. This paper uses a multiparadigm approach to analyze the impact of information diffusion in social networks on bidding behavior in secret reserve price auctions. We first develop an analytical model for the effect of shared information on individual bidding behavior in a secret reserve price auction with a single buyer facing a single seller similar to eBays Best Offer and some variants of NYOP. Next, we combine the implications from our analytical model with relational data that describe the individuals position in social networks. We empirically test the implications of our analytical model in a laboratory experiment, and examine the impact of information diffusion in social networks on bidding behavior in a field study with real purchases where we use a virtual world as proxy for the real world. We find that the amount and dispersion of information in the individualized context, and betweenness centrality in the social network context, have a significant impact on bidding behavior. Finally, we discuss the implications of our results for buyers and sellers.

The Impact of Search and Recommendation Systems on Sales in Electronic Commerce

The Internet and related technologies have vastly expanded the variety of products that can be profitably promoted and sold by online retailers. Furthermore, search and recommendation tools reduce consumers’ search costs in the Internet and enable them to extend their search from a few easily found best-selling products (blockbusters) to a large number of less frequently selling items (niches). As a result, Long Tail sales distribution patterns emerge that illustrate an increasing demand in niches. We show in this article how different classes of search and recommendation tools affect the distribution of sales across products, total sales, and consumer surplus. We hereby use an agent-based simulation which is calibrated based on real purchase data of a video-on-demand retailer. We find that a decrease in search costs through improved search technology can either shift demand from blockbusters to niches (search filters and recommendation systems) or from niches to blockbusters (charts and top lists). We break down demand changes into substitution and additional consumption and show that search and recommendation technologies can lead to substantial profit increases for retailers. We also illustrate that decreasing search costs through search and recommendation technologies always lead to an increase in consumer surplus, suggesting that retailers can use these technologies as competitive advantage.

A decision support system for stock investment recommendations using collective wisdom

Previous research has shown that user-generated stock votes from online communities can be valuable for investment decisions. However, to support investors on a day-to-day basis, there is a need for an efficient support system to facilitate the use of the data and to transform crowd votes into actionable investment opportunities. We propose a decision support system (DSS) design that enables investors to include the crowds recommendations in their investment decisions and use it to manage a portfolio. A prototype with two test scenarios shows the potential of the system as the portfolios recommended by the system clearly outperform the market benchmark and comparable public funds in the observation period in terms of absolute returns and with respect to the Reward-to-Variability-Ratio.

Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs

The increasing consideration of behavioral aspects in operations management models has prompted greater use of choice-based conjoint (CBC) studies in operations research. Such studies can elicit consumers’ willingness to pay (WTP), a core input for many optimization models. However, optimization models can yield valid results only if consumers’ WTP is estimated accurately. A simulation study and two field studies show that extreme response behavior in CBC studies, such that consumers always or never choose the no-purchase option, harms the validity of WTP estimates. Reporting the share of consumers who always and never select the no-purchase option allows for detecting extreme response behavior. This study suggests an individually adapted design that avoids extreme response behavior and thus significantly improves WTP estimation accuracy.

Explaining the adoption of grid computing: An integrated institutional theory and organizational capability approach

Grid computing can meet computational demands and offers a promising resource utilization approach. However, little research details the drivers of and obstacles to adoption of this technology. Institutional and organizational capability theory suggests an adoption model that accounts for inter- and intra-organizational influences. An empirical study with 233 high-ranking IT executives reveals that adoption results from social contagion, while organizational capabilities such as trust, firm innovativeness, tendency to outsource, and IT department size, influence adoption from an intra-organizational perspective. The findings show that mimetic pressures and trust play major roles in adoption processes, which differentiates grid computing from other inter-organizational systems.

Grid Economics in Departmentalized Enterprises

The application of Grid technology is finally spreading from engineering and natural science-related industrial sectors to other industries with a high demand for computing applications. However, the diffusion of Grid technology within these sectors is often hindered by a lack of the incentive to share the computational resources across departments or branches even within the same enterprise. A promising way of overcoming these barriers is the introduction of a pricing mechanism for the use of Grid-based resources. This work introduces such a pricing approach to Grid computing and provides three simulation scenarios to illustrate the effectiveness of such an economized Grid solution. The simulation results indicate that the pooling of information technology resources can produce a reduction of 33% in cost compared to individual and dedicated servers. However, with a price-based allocation of computing resources, a further 10% of cost reduction can be achieved by introducing an auction mechanism. Therefore we claim that there is huge cost reduction potential in departmentalized enterprises beyond the savings that can be achieved by a utility-based allocation of computing resources, if economically measured allocation methods are combined with advanced refining and learning methods in the allocation process.

The health information seeking and usage behavior intention of Chinese consumers through mobile phones

Purpose – Although the health information seeking behavior of consumers through the internet has received great attention, limited attempt has been made to integrate both the health information seeking behavior and the usage behavior in a mobile online context. The purpose of this paper is to explore the factors that influence consumer mobile health information seeking (MHIS) and usage behavior based on information quality, perceived value, personal health value, and trust. Design/methodology/approach – A survey was conducted to collect data. A two-step approach of structure equation modeling based was used to test the measurement model and hypothesis model. Findings – Information quality, perceived value, and trust were found to have positive effects on both the intention to seek and to use health information, and that the intention to seek affects the intention to use. Among the three components of perceived value, the utilitarian and epistemic values were found to have significant effects on intention ...

The social embeddedness of decision making: opportunities and challenges

Sociologists have long recognized that economic decisions are socially embedded. Management sciences and business practices have gradually begun to incorporate this idea. With the rise of the Internet, large-scale data are available on friendships, recommendations, transactions and social interactions, which have led to a strong momentum for research in this area. The aim of this article is to inspire multidisciplinary research on the mechanisms and consequences of social embeddedness on decision making and to highlight opportunities and challenges by synthesizing findings from various fields, such as IS research, sociology, economics, marketing and other management disciplines. Key suggestions of this paper are to identify the causality between social embeddedness and decision making with small-scale experiments, and to learn more about network formation by analyzing the evolution of social networks.

Research Note—Can’t Buy Me Love…Or Can I? Social Capital Attainment Through Conspicuous Consumption in Virtual Environments

Conspicuous consumption affects anyone who cares about social status; it has intrigued sociologists and economists for more than 100 years. The idea that conspicuous consumption can increase social status, as a form of social capital, has been broadly accepted, yet researchers have not been able to test this effect empirically. In this work, we provide empirical evidence by analyzing the digital footprints of purchases and social interactions in different virtual worlds. We use a multimethod approach, such that we both analyze transactional data and conduct a randomized field experiment. Virtual worlds, as artificial laboratories, offer the opportunity to analyze the social capital of their inhabitants, subsequent to their purchase of virtual prestige goods, which provides a means to empirically test hypotheses that would be nearly impossible to test in real-world settings. Our results are consistent with the notion that conspicuous consumption represents an investment in social capital.