THE EFFECT OF SOCIAL REPUTATION ON RETENTION: DESIGNING A SOCIAL REAL-TIME DELPHI PLATFORM

Forecasting with high uncertainty and long-time horizons still challenges researchers and practitioners. A widely adopted method in knowledge sharing and forecasting based on experts is the Delphi method and its offspring, the Real-Time Delphi. While the traditional Delphi method already is intensely investigated, the Real-Time Delphi is still evolving, and no dominant design has been found yet. A problem arising in both variants of the Delphi method, are high drop-out rates between rounds. This paper applies a design science research approach to motivate the need for social design elements from literature and derives design principles for Real-Time Delphi platform. Based on the design, we implement and evaluate a prototype in an online experiment as well as an IT artifact in a field study. We find significant supporting evidence, that (the promise of) positive social reputation increases commitment, and therefore subsequent platform engagement of our Real-Time Delphi survey. Our findings, therefore, contribute valuable design knowledge for Real-Time Delphi platforms. Moreover, we provide advice on how to raise retention in knowledge sharing systems

Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making

This article explores how a collaboration technology called Artificial Swarm Intelligence (ASI) addresses the limitations associated with group decision making, amplifies the intelligence of human groups, and facilitates better business decisions. It demonstrates of how ASI has been used by businesses to harness the diverse perspectives that individual participants bring to groups and to facilitate convergence upon decisions. It advances the understanding of how artificial intelligence (AI) can be used to enhance, rather than replace, teams as they collaborate to make business decisions

Engineering Delphi-Markets for Crowd-based Prediction - The FAZ.NET-Orakel and other Cases

Reliable forecasting is a key success factor of most organizations and companies. Where historical data is not available, the forecasts address questions in the far future, information is dispersed regarding location and form, or conflicting goals or values have to be considered, judgmental forecasting methods based on experts or the crowd are typically applied. However, several judgmental forecasting methods exist and each method has some individual weaknesses. Delphi-Markets are an integrated approach of prediction markets and Real-Time Delphi studies. Depending on their implementation, they allow to combine several properties of both approaches in order to overcome individual weaknesses. Three different ways to integrate the method are presented and discussed in this work. In order to better understand challenges and potentials of Delphi-Markets, the FAZ.NET-Orakel was instantiated and made publicly available for evaluation and improvement of an exemplary Delphi-Market under real-world conditions. In this context, four proposed improvements for the integrated approach were evaluated in four research projects. These projects correspond to the four sources of forecasting error according to the Judgmental Forecasting Improvement Model, introduced and derived in this dissertation as well. On the one hand, these improvements deal with common problems of prediction markets: Cognitive errors, such as partition dependence, and motivational errors, such as manipulation and fraud. On the other hand, these include common problems of Real-Time Delphi studies: The selection of experts for Delphi studies and retention during the surveys. As contributions to the overall IS research derived from the examinations of the Delphi-Markets and this dissertation, design principles for two extensions (social Real-Time Delphi and a crowd-based approach for manipulation and fraud detection) are formulated, implemented, tested, and suggested for application. Further, the role of complexity and expertise in the occurrence of the partition dependence bias is examined and a selection approach for experts for Delphi studies based on trading data is suggested and evaluated

The relevance of prediction markets for corporate forecasting

Prediction markets (PMs) are virtual stock markets on which shares are traded taking advantage of the wisdom-of-crowds principle to access collective intelligence. It is claimed that the accumulation of information by groups leads to joint group decisions often better than individual participants’ approaches to solutions. A PM share represents a future event or a market condition (e.g. expected sales figures of a product for a specific month) and provides forecasts via its price which is interpreted as the probability of the event occurring. PMs can be used in competition with other forecasting tools; when applied for forecasting purposes within a company they are called corporate prediction markets (CPMs). Despite great praise in the (academic) literature for the use of PMs as an efficient instrument for bringing together scattered information and opinions, corporate usage and applications are limited. This research was directed towards an examination of this discrepancy by means of focusing on the barriers to adoption within enterprises. Literature and reality diverged and neglected the important aspect of corporate culture. Screening existing research and interviews with business executives and corporate planners revealed challenges of company hierarchy as an inhibitor to the acceptance of CPM outcomes. Findings from 55 interviews and a thematic analysis of the literature exposed that CPMs are useful but rarely used. Their lack of use arises from senior executives’ perception of the organisational hierarchy being taxed and fear of losing power as CPMs (can) include lower rungs of the corporate ladder in decision-making processes. If these challenges can be overcome the potential of CPMs can be released. It emerged – buttressed by ten additional interviews – that CPMs would be worthwhile for company forecasting, particularly supporting innovation management which would allow idea markets (as an embodiment of CPMs) to excel. A contribution of this research lies in its additions to the PM literature, explaining the lack of adoption of CPMs despite their apparent benefits and making a case for the incorporation of CPMs as a forecasting instrument to facilitate innovation management. Furthermore, a framework to understand decision-making in the adoption of strategic tools is provided. This framework permits tools to be accepted on a more rational base and curb the emotional and political influences which can act against the adoption of good and effective tools

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Continuous Market Engineering - Focusing Agent Behavior, Interfaces, and Auxiliary Services

Electronic markets spread out amongst business entities as well as private individuals. Albeit numerous approaches on developing electronic markets exist, a unified approach targeting market development, redesign, and refinement has been lacking. This thesis studies the potential of continuously improving electronic markets. Thereby, the experiments? design focuses on Agent Behavior, Interfaces, and Auxiliary Services and thus unveils the potential of continuously improving electronic markets

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice