Analyzing the modes of reasoning in design using the SAPPhIRE model of causality and the Extended Integrated Model of Designing

Literature suggests that people typically understand knowledge by induction and produce knowledge by synthesis. This paper revisits the various modes of reasoning – explanatory abduction, innovative abduction, deduction, and induction – that have been proposed by earlier researchers as crucial modes of reasoning underlying the design process. First, our paper expands earlier work on abductive reasoning – an essential mode of reasoning involved in the process of synthesis – by understanding its role with the help of the “SAPPhIRE” model of causality. The explanations of abductive reasoning in design using the SAPPhIRE model have been compared with those using existing models. Second, the paper captures and analyzes various modes of reasoning during design synthesis with the help of the “Extended Integrated Model of Designing”. The analysis of participants' verbal speech and outcomes shows the model's ability to explain the various modes of reasoning that occur in design. The results indicate the above models to provide a more extensive account of reasoning in design synthesis. Earlier empirical validation of both the models lends further support to the claim of their explanatory capacity.

Modeling Faking in the Multidimensional Forced-Choice Format: The Faking Mixture Model

The multidimensional forced-choice (MFC) format has been proposed to reduce faking because items within blocks can be matched on desirability. However, the desirability of individual items might not transfer to the item blocks. The aim of this paper is to propose a mixture item response theory model for faking in the MFC format that allows to estimate the fakability of MFC blocks, termed the Faking Mixture model. Given current computing capabilities, within-subject data from both high- and low-stakes contexts are needed to estimate the model. A simulation showed good parameter recovery under various conditions. An empirical validation showed that matching was necessary but not sufficient to create an MFC questionnaire that can reduce faking. The Faking Mixture model can be used to reduce fakability during test construction.

Exploring parallel formal verification of BIG-DATA systems

Software Engineering is trying to adapt its tools, mechanisms and techniques to cope with the challenges involved when developing BIG DATA software systems. In particular, formal verification in one of the areas that more urgently is required to step in. In this work we introduce two crucial aspects to consolidate the FVS tool to tackle this issue. For one side, FVS’s parallel algorithm is proved to be sound and correct. For the other side, we developed a compelling empirical validation of our approach, employing a communication protocol relevant in the industrial world within a context of parallel systems, introducing a load-balancer process and comparing several implementations.

Customer lifetime value models: do they predict actual behaviour?

<p>Accurate input information is the cornerstone of sound managerial decision making. Assessing the future lifetime value of customers is a key component in making accurate managerial decisions such as how to apply scarce organisation resources on retention or acquisition activities (Blattberg and Deighton, 1996). Additionally, accurate customer lifetime value (CLV) calculation can be used for effective segmentation of customers. Berger and Nasr (1998) recognised the need for an improved approach to customer lifetime valuation calculation. The model proposed by Berger and Nasr (1998) differed from historical approaches, such as the Recency, Frequency, and Monetary (RFM) method, by predicting the future state of existing customers and discounting the projected cash flow over time. Whilst the RFM model was popular as noted by Reinartz and Kumar, (2000), it was limited in accurately calculating the future value of a group of customers and was applied in segmentation classification. Berger and Nasr's (1998) model found favour in literature where subsequent contributions followed in areas; Managerial application of the model findings, alternative approaches to calculating the model inputs, and introducing alternative variables or techniques in the CLV calculation model itself. The literature confirmed Berger and Nasr's (1998) approach as suitable for examination in this study however also revealed a general lack of empirical validation for Berger and Nasr's (1998). A review of literature detailed several extensions to the theory and modelling literature on CLV and several propositions relating to this area of theory development. These were contributions mostly conceptual by nature and few supported their concepts with empirical validation. This empirical study provides an important contribution by examining the predictive accuracy of Berger and Nasr's (1998) CLV calculation model. The purpose of this research was to compare Berger and Nasr's (1998) CLV model's prediction of customer lifetime value against the actual value data over a specific period for a set cohort of residential segment consumers from a leading New Zealand energy retailer. This study goes further to examine the sensitivity of the model's calculation output to a change in input variables. The findings of this research challenge the predictive accuracy of Berger and Nasr's (1998) CLV model. The model was applied using both large (total cohort) and small (segments) customer groups to understand how what level of accuracy can be achieved in different contexts. The study identified a number of limitations such as the use of a constant retention rate, and not adequately accommodating the level of customer heterogeneity. The sensitivity of the model to change in the input variables supported Gupta, Lehmann and Stuart's (2004) research showing the retention variable was the critical input as it was the most influential on the model calculation. The marketing and discount rate variables had little to no influence on the model calculation outcome. Several propositions identified in literature on this subject were examined with many supported such as Reichheld and Sasser's (1990) observation that businesses lose 15% - 20% of their customers each year. Wyner's (1999) proposition was also supported in that the cohort when segmented demonstrated considerable different characteristics including patterns of attrition. This research presents empirical findings that will assist further theory development in the area of accurate measurement of Customer Lifetime Value (CLV) and promotes further examination of Berger and Nasr's (1998) CLV model.</p>

Customer lifetime value models: do they predict actual behaviour?

<p>Accurate input information is the cornerstone of sound managerial decision making. Assessing the future lifetime value of customers is a key component in making accurate managerial decisions such as how to apply scarce organisation resources on retention or acquisition activities (Blattberg and Deighton, 1996). Additionally, accurate customer lifetime value (CLV) calculation can be used for effective segmentation of customers. Berger and Nasr (1998) recognised the need for an improved approach to customer lifetime valuation calculation. The model proposed by Berger and Nasr (1998) differed from historical approaches, such as the Recency, Frequency, and Monetary (RFM) method, by predicting the future state of existing customers and discounting the projected cash flow over time. Whilst the RFM model was popular as noted by Reinartz and Kumar, (2000), it was limited in accurately calculating the future value of a group of customers and was applied in segmentation classification. Berger and Nasr's (1998) model found favour in literature where subsequent contributions followed in areas; Managerial application of the model findings, alternative approaches to calculating the model inputs, and introducing alternative variables or techniques in the CLV calculation model itself. The literature confirmed Berger and Nasr's (1998) approach as suitable for examination in this study however also revealed a general lack of empirical validation for Berger and Nasr's (1998). A review of literature detailed several extensions to the theory and modelling literature on CLV and several propositions relating to this area of theory development. These were contributions mostly conceptual by nature and few supported their concepts with empirical validation. This empirical study provides an important contribution by examining the predictive accuracy of Berger and Nasr's (1998) CLV calculation model. The purpose of this research was to compare Berger and Nasr's (1998) CLV model's prediction of customer lifetime value against the actual value data over a specific period for a set cohort of residential segment consumers from a leading New Zealand energy retailer. This study goes further to examine the sensitivity of the model's calculation output to a change in input variables. The findings of this research challenge the predictive accuracy of Berger and Nasr's (1998) CLV model. The model was applied using both large (total cohort) and small (segments) customer groups to understand how what level of accuracy can be achieved in different contexts. The study identified a number of limitations such as the use of a constant retention rate, and not adequately accommodating the level of customer heterogeneity. The sensitivity of the model to change in the input variables supported Gupta, Lehmann and Stuart's (2004) research showing the retention variable was the critical input as it was the most influential on the model calculation. The marketing and discount rate variables had little to no influence on the model calculation outcome. Several propositions identified in literature on this subject were examined with many supported such as Reichheld and Sasser's (1990) observation that businesses lose 15% - 20% of their customers each year. Wyner's (1999) proposition was also supported in that the cohort when segmented demonstrated considerable different characteristics including patterns of attrition. This research presents empirical findings that will assist further theory development in the area of accurate measurement of Customer Lifetime Value (CLV) and promotes further examination of Berger and Nasr's (1998) CLV model.</p>