Ranking Models in Conjoint Analysis

In this paper we consider the estimation of probabilisticranking models in the context of conjoint experiments. By usingapproximate rather than exact ranking probabilities, we do notneed to compute high-dimensional integrals. We extend theapproximation technique proposed by \\citet{Henery1981} in theThurstone-Mosteller-Daniels model for any Thurstone orderstatistics model and we show that our approach allows for aunified approach. Moreover, our approach also allows for theanalysis of any partial ranking. Partial rankings are essentialin practical conjoint analysis to collect data efficiently torelieve respondents' task burden.conjoint experiments;partial rankings;thurstone order statistics model

Confidence intervals for maximal reliability of probability judgments

Subjective probabilities play an important role in marketingresearch, for example where individuals rate the likelihood thatthey will purchase a new to develop product. The tau-equivalentmodel can describe the joint behaviour of multiple test itemsmeasuring the same subjective probability. It improves thereliability of the subjective probability estimate by using aweighted sum as the outcome of the test rather than an unweightedsum. One can choose the weights to obtain maximal reliability.In this paper we stress the use of confidence intervals to assessmaximal reliability, as this allows for a more critical assessmentof the items as measurement instruments. Furthermore, two newconfidence intervals for the maximal reliability are derived andcompared to intervals derived earlier in \\citet{YuanBentler2002,RaykovPenev2006}. The comparison involves coverage curves, amethodology that is new in the field of reliability. The existingYuan-Bentler and Raykov-Penev intervals are shown to overestimatethe maximal reliability, whereas one of our proposed intervals, thestable interval, performs very well. This stable interval hardlyshows any bias, and has a coverage for the true value which isapproximately equal to the confidence level.confidence intervals;subjective probability;coverage curves;maximal reliability;measurement scales;tau-equivalent model

Smart technology for healthcare: Exploring the antecedents of adoption intention of healthcare wearable technology

© The Author(s), 2019. Technological advancement and personalized health information has led to an increase in people using and responding to wearable technology in the last decade. These changes are often perceived to be beneficial, providing greater information and insights about health for users, organizations and healthcare and government. However, to date, understanding the antecedents of its adoption is limited. Seeking to address this gap, this cross-sectional study examined what factors influence users’ adoption intention of healthcare wearable technology. We used self-administrated online survey to explore adoption intentions of healthcare wearable devices in 171 adults residing in Hong Kong. We analyzed the data by Partial least squares – structural equation modelling (PLS-SEM). The results reveal that perceived convenience and perceived irreplaceability are key predictors of perceived useful ness, which in turn strengthens users’ adoption intention. Additionally, the results also reveal that health belief is one of the key predictors of adoption intention. This paper contributes to the extant literature by providing understanding of how to strengthen users’ intention to adopt healthcare wearable technology. This includes the strengthening of perceived convenience and perceived irreplaceability to enhance the perceived usefulness, incorporating the extensive communication in the area of healthcare messages, which is useful in strengthening consumers’ adoption intention in healthcare wearable technology

Reliability and Rankings

Questionnaires are an important way to gather information about large populations for both qualitative and quantitative research. Hence, the value of a good questionnaire design and the quality of questionnaire data cannot be emphasized enough. This thesis discusses some aspects of the statistical analysis of measurement data obtained via questionnaires. In the first part of this thesis we focus on maximizing scale reliability. We derive the asymptotic distribution of maximal reliability measures to construct confidence intervals in order to assess the adequacy of the measure. We stress the use of confidence intervals accompanying single measures that summarize the parameters to assess the adequacy of the measure. The results can lead to better designs of questionnaires, which in turn lead to more precise survey outcomes. The second part of this thesis proposes methodologies to perform statistical analysis of stated consumer preferences measured as rankings data, especially in the context of conjoint measurements. Our statistical models allow for the

Analyzing preferences ranking when there are too many alternatives.

Consumer preferences can be measured by rankings of alternatives. When there are too many alternatives, this consumer task becomes complex. One option is to have consumers rank only a subset of the available alternatives. This has an impact on subsequent statistical analysis, as now a large amount of ties is observed. We propose a simple methodology to perform proper statistical analysis in this case. It also allows to test whether (parts of the) rankings are random or not. An illustration shows its ease of application

Ranking Models in Conjoint Analysis

In this paper we consider the estimation of probabilistic ranking models in the context of conjoint experiments. By using approximate rather than exact ranking probabilities, we do not need to compute high-dimensional integrals. We extend the approximation technique proposed by \\citet{Henery1981} in the Thurstone-Mosteller-Daniels model for any Thurstone order statistics model and we show that our approach allows for a unified approach. Moreover, our approach also allows for the analysis of any partial ranking. Partial rankings are essential in practical conjoint analysis to collect data efficiently to relieve respondents' task burden

Confidence intervals for maximal reliability of probability judgments

Subjective probabilities play an important role in marketing research, for example where individuals rate the likelihood that they will purchase a new to develop product. The tau-equivalent model can describe the joint behaviour of multiple test items measuring the same subjective probability. It improves the reliability of the subjective probability estimate by using a weighted sum as the outcome of the test rather than an unweighted sum. One can choose the weights to obtain maximal reliability. In this paper we stress the use of confidence intervals to assess maximal reliability, as this allows for a more critical assessment of the items as measurement instruments. Furthermore, two new confidence intervals for the maximal reliability are derived and compared to intervals derived earlier in \\citet{YuanBentler2002, RaykovPenev2006}. The comparison involves coverage curves, a methodology that is new in the field of reliability. The existing Yuan-Bentler and Raykov-Penev intervals are shown to overestimate the maximal reliability, whereas one of our proposed intervals, the stable interval, performs very well. This stable interval hardly shows any bias, and has a coverage for the true value which is approximately equal to the confidence level

Stress waves in composite laminates excited by transverse plane shock waves

Shock and Vibration36419-433SHVI

Analysis of cylindrical shells using generalized differential quadrature

Shock and Vibration43193-198SHVI

Visualizing attitudes towards service levels

To assess the attitudes with respect to the quality of banks’ service levels, we use survey data amongst more than 250 Chief Financial Officers (CFOs) of a range of Netherlands-based companies. These companies range from small to very large (including multinationals as Philips and Shell) companies. The survey was conducted in five subsequent years. In this paper, we explore the evaluations of the service levels of banks where, for all attributes considered, the ratings were accompanied by an importance rating. We propose a visualization method that incorporates the importance weights into correspondence analysis. The resulting maps exhibit the correlation structure of the different service items as well as the variances for each item. Moreover, the results are linked to different banks over time, thus exposing the development of the attitudes over time