Multimodal and nested preference structures in choice-based conjoint analysis: a comparison of bayesian choice models with discrete and continuous representations of heterogeneity

Die Choice-Based Conjoint-Analyse (CBC) ist heutzutage die am weitesten verbreitete Variante der Conjoint-Analyse, einer Klasse von Verfahren zur Messung von Nachfragerpräferenzen. Der Hauptgrund für die zunehmende Dominanz des CBC-Ansatzes in jüngerer Zeit besteht darin, dass hier das tatsächliche Wahlverhalten von Nachfragern sehr realistisch nachgestellt werden kann, indem die Befragten wiederholt ihre bevorzugte Alternative aus einer Menge mehrerer Alternativen (Choice Sets) auswählen. Im Rahmen der CBC-Analyse ist das Multinomiale Logit- (MNL) Modell das am häufigsten verwendete diskrete Wahlmodell. Das MNL-Modell weist jedoch zwei wesentliche Einschränkungen auf: (a) Es impliziert proportionale Substitutionsmuster zwischen den Alternativen, was als Independence of Irrelevant Alternatives- (IIA) Eigenschaft bezeichnet wird, und (b) es berücksichtigt keine Nachfragerheterogenität, da per Definition Teilnutzenwerte für alle Konsumenten als homogen angenommen werden. Seit den 1990er-Jahren werden hierarchisch bayesianische (HB) Modelle für die Teilnutzenwertschätzung in der CBC-Analyse verwendet. Solche HB-Modelle ermöglichen zum einen eine Schätzung individueller Teilnutzenwerte, selbst bei einer beschränkten Datenlage, zum anderen können sie aufgrund der Modellierung von Heterogenität die IIA-Eigenschaft stark abmildern. Der Schwerpunkt der vorliegenden Thesis liegt auf der Verwendung von HB-Modellen mit unterschiedlichen Darstellungen von Nachfragerheterogenität (diskret vs. kontinuierlich) für CBC-Daten sowie außerdem auf einem speziellen HB-Modell, das die IIA-Eigenschaft durch Berücksichtigung von unterschiedlichen Ähnlichkeitsgraden zwischen Teilmengen von Alternativen (Nestern) zusätzlich abschwächt. Insbesondere wird die statistische Performance von einfachen MNL-, Latent Class- (LC) MNL-, HB-MNL-, Mixture-of-Normals- (MoN) MNL-, Dirichlet Process Mixture- (DPM) MNL- und HB-Nested Multinomialen Logit- (NMNL) Modellen (unter experimentell variierenden Bedingungen) hinsichtlich der Recovery von Präferenzstrukturen, der Anpassungsgüte und der Prognosevalidität analysiert. Dazu werden zwei umfangreiche Monte-Carlo-Studien durchgeführt, ferner werden die verschiedenen Modelltypen auf einen empirischen CBC-Datensatz angewandt. In der ersten Monte-Carlo-Studie liegt der Fokus auf dem Vergleich zwischen dem HB-MNL und dem HB-NMNL bei multimodalen und genesteten Präferenzstrukturen. Die Ergebnisse zeigen, dass es keine wesentlichen Unterschiede zwischen beiden Modelltypen hinsichtlich der Anpassungsgüte und insbesondere hinsichtlich der Prognosevalidität gibt. In Bezug auf die Recovery von Präferenzstrukturen schneidet das HB-MNL-Modell zunehmend schlechter ab, wenn die Korrelation in mindestens einem Nest höher ist, während sich das HB-NMNL-Modell erwartungsgemäß an den Grad der Ähnlichkeit zwischen Alternativen anpasst. Die zweite Monte-Carlo-Studie befasst sich mit multimodalen und segmentspezifischen Präferenzstrukturen. Um Unterschiede zwischen den Klassen von Modellen mit unterschiedlichen Darstellungen von Heterogenität herauszuarbeiten, werden hier gezielt die Grade der Heterogenität innerhalb von Segmenten und zwischen Segmenten manipuliert. Unter experimentell variierenden Bedingungen werden die state-of-the-art Ansätze zur Modellierung von Heterogenität (einfaches MNL, LC-MNL, HB-MNL) mit erweiterten Wahlmodellen, die sowohl Nachfragerheterogenität zwischen Segmenten als auch innerhalb von Segmenten abbilden können (MoN-MNL und DPM-MNL), verglichen. Das zentrale Ergebnis dieser Monte-Carlo-Studie ist, dass sich das HB-MNL-Modell, welches eine multivariate Normalverteilung zur Modellierung von Präferenzheterogenität unterstellt, als äußerst robust erweist. Darüber hinaus kristallisiert sich der LC-MNL-Segmentansatz als der beste Ansatz heraus, um die „wahre“ Anzahl von Segmenten zu identifizieren. Abschließend werden die zuvor vorgestellten Wahlmodelle auf einen realen CBC-Datensatz angewandt. Die Ergebnisse zeigen, dass Modelle mit einer kontinuierlichen Darstellung von Heterogenität (HB-MNL, HB-NMNL, MoN-MNL und DPM-MNL) eine bessere Anpassungsgüte und Prognosevalidität aufweisen als Modelle mit einer diskreten Darstellung von Heterogenität (einfaches MNL, LC-MNL). Weiterhin zeigt sich, dass das HB-MNL-Modell für Prognosezwecke sehr gut geeignet ist und im Vergleich zu den anderen (erweiterten) Modellen mindestens ebenso gute, wenn nicht sogar wesentlich bessere Vorhersagen liefert, was für Manager eine zentrale Erkenntnis darstellt.Choice-Based Conjoint (CBC) is nowadays the most widely used variant of conjoint analysis, a class of methods for measuring consumer preferences. The primary reason for the increasing dominance of the CBC approach over the last 35 years is that it closely mimics real choice behavior of consumers by asking respondents repeatedly to choose their preferred alternative from a set of several offered alternatives (choice sets), respectively. Within the framework of CBC analysis, the multinomial logit (MNL) model is the most frequently used discrete choice model. However, the MNL model suffers from two major limitations: (a) it implies proportional substitution rates across alternatives, referred to as the Independence of Irrelevant Alternatives (IIA) property and (b) it does not account for unobserved consumer heterogeneity, as part-worth utilities are assumed to be equal for all respondents by definition. Since the 1990s, Hierarchical Bayesian (HB) models have been used for part-worth utility estimation in CBC analysis. HB models are able to determine part-worth utilities at the individual respondent level even with little individual respondent information on the one hand and, as a result of addressing consumer heterogeneity, can strongly soften the IIA property on the other hand. The focus of the present thesis is on CBC analysis using HB models with different representations of heterogeneity (discrete vs. continuous) as well as using a HB model which mitigates the IIA property to a further extent by allowing for different degrees of similarity between subsets (nests) of alternatives. In particular, we systematically explore the comparative performance of simple MNL, latent class (LC) MNL, HB-MNL, mixture-of-normals (MoN) MNL, Dirichlet Process Mixture (DPM) MNL and HB nested multinomial logit (NMNL) models (under experimentally varying conditions) using statistical criteria for parameter recovery, goodness-of-fit, and predictive accuracy. We conduct two extensive Monte Carlo studies and apply the different types of models to an empirical CBC data set. In the first Monte Carlo study, the focus lies on the comparative performance of the HB-MNL versus the HB-NMNL for multimodal and nested preference structures. Our results show that there seems to be no major differences between both types of models with regard to goodness-of-fit measures and in particular their ability to predict respondents’ choice behavior. Regarding parameter recovery, the HB-MNL model performs increasingly worse when correlation in at least one nest is higher, while the HB-NMNL model adapts to the degree of similarity between alternatives, as expected. The second Monte Carlo study deals with multimodal and segment-specific preference structures. More precisely, to carve out differences between the classes of models with different representations of heterogeneity, we specifically vary the degrees of within-segment and between-segment heterogeneity. We compare state-of-the-art methods to represent heterogeneity (simple MNL, LC-MNL, HB-MNL) and more advanced choice models representing both between-segment and within-segment consumer heterogeneity (MoN-MNL and DPM-MNL) under varying experimental conditions. The core finding from our Monte Carlo study is that the HB-MNL model appears to be highly robust against violations in its assumption of a single multivariate normal distribution of consumer preferences. In addition, the LC-MNL segment solution proves to be the best approach to recover the “true” number of segments. Finally, we apply the previously presented choice models to a real-life CBC data set. The results indicate that models with a continuous representation of heterogeneity (HB-MNL, HB-NMNL, MoN-MNL and DPM-MNL) perform better than models with a discrete representation of heterogeneity (simple MNL, LC-MNL). Further, it turns out that the HB-MNL model works extremely well for predictive purposes and provides at least as good if not considerably better predictions compared to the other (advanced) models, which is an important aspect for managers

Effects of perennial and cover crops on hydrology in Iowa

Since European settlement, and beginning in the 1940\u27s, two dramatic shifts in land use have occurred in Iowa - the first from prairie and forest to tile-drained farmland, and the second from diverse rotations to a heavier concentration of corn-soybean rotations and continuous corn. These shifts in land use and management have altered hydrological and biogeochemical cycles in the Upper Midwest, but perennial and cover crops have the potential to assist in mediating changes in these cycles. The first study in this thesis examines how the perennial forage (PF) crop orchardgrass (Dactylis glomerata) affects subsurface drainage as compared to a corn-soybean rotation or continuous corn (row crops, or RC). Over the entire drainage season (March -November) over 22 years, PF did not reduce subsurface drainage, but during May, PF reduced subsurface drainage by 32% (p \u3c 0.05). May is a critical period for drainage in Iowa, as wet field conditions and a lack of vegetative cover contribute to a majority of drainage and leaching of NO3-N from row crop fields during this period. The second study investigates how cereal rye (Secale cereale L. ssp. cereal) cover crop influences soil water dynamics in two fields in Iowa. During the spring growth period of rye, at a site in central Iowa, rye plots to be planted to soybeans significantly increased the rise in magnitude of soil moisture following rainfall events in the top 0-20 cm of soil as compared to fallow plots. Different types of rainfall events caused differing responses in soil water redistribution. In the third study, the effect of a rye cover crop on soil water content and soil water storage during the spring and early summer in a drought year is examined. In one field in central Iowa, rye was able to conserve water in the top soil layers (0-20 cm) and increase soil water storage in a corn-soybean rotation. Because of public health and ecological concerns, and in light of economic and ecological uncertainties posed by climate change, more research should be directed toward perennial and cover crops because of their beneficial contributions to hydrological processes and biogeochemical cycling

IT strategy Implementation Framework – Bridging Enterprise Architecture and IT Governance

It’s increasingly acknowledged that firms cannot be competitive if their IT strategies are not implemented methodically. A number of proposals have been made to prioritize strategic IT plan executions and determine the most appropriate models and architectures. While IT governance primarily focuses on day-to-day IT operations, enterprise architecture primarily focuses on designing the future state of architecture in support of business. Despite still being a major concern for business and IT executives, there is little published research that bridges both and therefore attempts to give methodological support from a holistic perspective. Additionally it seems that governance needs to be investigated in terms of implementing IT strategies on different levels of abstraction. This article therefore proposes a framework to analyze institutions and guide ITstrategy implementation in order to analyze, monitor and control the desired results. Due to the underlying theories and applied interviews the model is potentially generalizable

Identification of Business Services Literature Review and Lessons Learned

Business-driven identification of services is a precondition for a successful implementation of service-oriented architectures (SOA). This article compares existing identification methods retrieved from related work and discusses the shortcomings. In particular, a lack of economic aspects constitutes a problem and leaves space for improvements. Finally, the paper proposes a process-oriented method of service identification. This approach incorporates the business point of view, strategic and economic aspects as well as technical feasibility

PARADIGMS AND INFORMATION SYSTEMS AS AN APPLIED DISCIPLINE – A MODEL-BASED REPRESENTATION, PROBLEMS, AND SUGGESTED SOLUTIONS

Information Systems Discipline (ISD) is dominated by the two contrary paradigms of design science on the one hand and behavioral science on the other. Apart from that, research results are considered more or less relevant for practice depending on the respective paradigm. Conclusively, research communities following the paradigms are partly incompatible while, due to the notion of relevance for practice, the exchange between science and practice is hampered. Various “disconnects” hindering the collaboration both between design science and behavioral science and between science and practice emerged due to this. These aspects will be described and represented within a model-based analysis of the situation while suggestions from ISD literature on the topic will be presented and discussed. Considering that comparable challenges have recently been faced in the field of medicine, Evidence-based Medicine (EbM) emerged as a new paradigm to solve similar problems and is now well-established. We will present and discuss some attempts to transfer the evidence-based research approach from medicine and how they may apply to the equally application-oriented field of Information Systems

Model Driven Information Security Management - Evaluating and Applying the Meta Model of ISO 27001

Information technology has had a significant impact on business operations and allowed the emergence of new business models. These IT-enabled processes and businesses however depend on secure information systems which need to be managed. The management of information systems security (ISS) is a highly dynamic and complex task due to constant change in the information technology domain. In this paper we propose the use of a meta model to aid ISS managers in setting up a holistic information security management system (ISMS). For this we describe how an adapted meta model of ISO 27001, a security standard for ISMS, can be used to aid with general phases of ISS management. We demonstrate how models can support ISS managers in their endeavors. The paper concludes with a pragmatic evaluation by providing an example of how such a meta model can be operationalized for vulnerability identification, before discussing potential future research

Evidence-Based Structuring and Evaluation of Empirical Research in Requirements Engineering - Fundamentals, Framework, Research Map

The objective of the contribution is to develop and motivate an approach of structuring, evaluating, and representing empirical research results regarding requirements engineering. Therefore, the authors develop a framework in order to organize the area of interest. The use of this framework and an evidence-based classification system allows us to develop a research map which helps to structure identified empirical research while enabling the derivation of further research needs. Additionally, it supports the selection of methods, techniques, etc. in requirements engineering practice

Systematic Review and Meta-Analysis of IS Security Policy Compliance Research. First Steps towards Evidence-Based Structuring of the IS Security Domain

Given the short supply of empiricism in ISS research, existing empirical evidence needs to be processed further than the scope of a single paper may allow. Other fields of science have long recognized the need for higher level analyses of research results in order to make them accessible to practitioners and develop a knowledge base. In our paper we perform an exhaustive literature research in the realm of empirical ISS research. As one of the recent research hotspots, we perform a systematic review of research results in Information Security Policy (ISP) compliance. We analyze and discuss the heterogeneity of research results and suggest a presentation format that may allow ISS practitioners to base their ISP design decisions on

Combining System Dynamics and Multidimensional Modelling - A Metamodel Based Approach

Online analytical processing (OLAP) as a modern business intelligence (BI) concept provides support for representing vast amounts of data for supporting management‟s decisions. Though, there is no inherent support for the representation of causal structures which could provide a foundation for advanced analysis like what-if or scenario techniques. System Dynamics (SD) is an approach with a long tradition used for modelling and simulation of complex systems, which could provide a causal complement for OLAP. This paper aims at integrating OLAP and SD on a linguistic level. Therefore linguistic metamodels of the corresponding conceptual modelling languages are derived and related towards each other, creating a translational relationship between the languages

Enhancing spectral shifts of plasmon-coupled noble metal nanoparticles for sensing applications

Noble metal nanoparticles possess very large scattering cross-sections, which make them useful as tags in biosensing assays with the potential to detect even single binding events. In this study, we investigated the effects of nanoparticle size on the shift in the light scattering spectrum following formation of Au–Au, Ag–Ag or Ag–Au dimers using FDTD simulations. We discuss the use of a color camera to detect these spectral changes for application in a target-induced dimerization sensing assay. Dimerization of Au nanoparticles induced a larger shift in color compared to Ag nanoparticles. Heterodimers composed of 60 nm Ag and 40 nm Au demonstrated an even larger spectral shift and color response compared to the best homodimer pair (80–40 nm Au). The increased spectral shift of the Ag–Au heterodimer was subsequently observed experimentally for the DNA-induced dimerization of nanoparticles, showing that careful selection of nanoparticle size and composition can significantly enhance recognition of nanoparticle dimerization events for use in (color) sensing assays