Investigating Food Packaging Elements from a Consumer’s Perspective

This study aims to identify and evaluate packaging elements in the food industry, taking into account various business areas/disciplines. The research was conducted with a sample of 1219 customers. An initial pool of 43 packaging items was developed, aiming to examine the elements that have a relationship with consumer behavior in buying food products. Exploratory factor analysis (EFA) was conducted on a random split-half sample of the data to examine the factor structure of these elements in the general population. Confirmatory factor analysis (CFA) was conducted in the holdout sample. The EFA of the packaging items resulted in seven factors: (1) Informational content, (2) Content protection and recognition, (3) Smart functioning, (4) Geometry, (5) Environmental friendliness (6) Endurance, and (7) Coloration. The CFA in the holdout sample supported this factor structure. The findings are informed by the consumer attitudes and predispositions towards packaging, thus having useful managerial applications.

Generating Technology Evolution Prediction Intervals Using a Bootstrap Method

Technology evolution prediction is critical for designers, business managers, and entrepreneurs to make important decisions during product development planning such as R&D investment and outsourcing. In practice, designers want to supplement point forecasts with prediction intervals to assess future uncertainty and make contingency plans accordingly. However, prediction intervals generation for technology evolution has received scant attention in the literature. In this paper, we develop a generic method that uses bootstrapping to generate prediction intervals for technology evolution. The method we develop can be applied to any model that describes technology performance incremental change. We consider parameter uncertainty and data uncertainty and establish their empirical probability distributions. We determine an appropriate confidence level to generate prediction intervals through a holdout sample analysis rather than specify that the confidence level equals 0.05 as is typically done in the literature. In addition, our method provides the probability distribution of each parameter in a prediction model. The probability distribution is valuable when parameter values are associated with the impact factors of technology evolution. We validate our method to generate prediction intervals through two case studies of central processing units (CPU) and passenger airplanes. These case studies show that the prediction intervals generated by our method cover every actual data point in the holdout sample tests. We outline four steps to generate prediction intervals for technology evolution prediction in practice.

Generating Technology Evolution Prediction Intervals With Bootstrap Method

Technology evolution prediction, or technological forecasting, is critical for designers to make important decisions during product development planning such as R&D investment and outsourcing. In practice, designers want to supplement point forecast by prediction intervals to assess future uncertainty and make contingency plans. Available technology evolution data is a time series but is generally with non-uniform spacing. Existing methods associated with typical time series models assume uniformly spaced data, so these methods cannot be used to construct prediction intervals for technology evolution prediction. In this paper, we develop a generic method that use bootstrapping to generate prediction intervals for technology evolution. The method we develop can be applied to any technology evolution prediction model. We consider parameter uncertainty and data uncertainty and establish their empirical probability distributions. We determine an appropriate confidence level α to generate prediction intervals through a holdout sample analysis rather than set α = 0.05 as is typically done in the literature. We validate our method to generate the prediction intervals through a case study of central processing unit transistor count evolution. The case study shows that the prediction intervals generated by our method cover every actual data point in a holdout sample test. To apply our method in practice, we outline four steps for designers to generate prediction intervals for technology evolution prediction.

Modeling Aggregate Choice for Form and Function Through Metaconjoint Analysis

In the previous work, meta-attributes have been used to model the relationship between two groups of disparate product attributes. There, preference for form, function, and the relationship between the two were modeled for individual consumers. However, this approach is limited as designers are often called on to choose a design that best appeals to a group of consumers, not individuals. This work expands on the concept and makes it more generally applicable by adapting metaconjoint to model aggregate choice for consumer groups. The results from this work show that a metaconjoint approach can be used to model aggregate choice for form and function and can yield better results on holdout sample predictions than form or function alone.

Estimating Minutes of Physical Activity From the Previous Day Physical Activity Recall: Validation of a Prediction Equation

Background:This study was designed to develop a prediction algorithm that would allow the Previous Day Physical Activity Recall (PDPAR) to be equated with temporally matched data from an accelerometer.Methods:Participants (n = 121) from a large, school-based intervention wore a validated accelerometer and completed the PDPAR for 3 consecutive days. Physical activity estimates were obtained from PDPAR by totaling 30-minute bouts of activity coded as ≥4 METS. A regression equation was developed in a calibration sample (n = 91) to predict accelerometer minutes of moderate to vigorous physical activity (MVPA) from PDPAR bouts. The regression equation was then applied to a separate, holdout sample (n = 30) to evaluate the utility of the prediction algorithm.Results:Gender and PDPAR bouts accounted for 36.6% of the variance in accelerometer MVPA. The regression model showed that on average boys obtain 9.0 min of MVPA for each reported PDPAR bout, while girls obtain 4.8 min of MVPA per bout. When applied to the holdout sample, predicted minutes of MVPA from the models showed good agreement with accelerometer minutes (r = .81).Conclusions:The prediction equation provides a valid and useful metric to aid in the interpretation of PDPAR results.

A psychometric investigation into the cross validation of an adaptation of the Ghiselli Predictability Index in personnel selection

Twigge, Theron, Steele and Meiring (2004) concluded that it is possible to develop a predictability index based on a concept originally proposed by Ghiselli (1956, 1960a, 1960b), which correlates with the real residuals derived from the regression of a criterion on one or more predictors. The addition of such a predictability index to the original regression model was found to produce a statistically significant increase in the correlation between the selection battery and the criterion. To be able to convincingly demonstrate the feasibility of enhancing selection utility through the use of predictability indices would, however, require the cross validation of the results obtained on a derivation sample on a holdout sample selected from the same population. The objective of this article consequently is to investigate the extent to which such a predictability index, developed on a validation sample, would successfully cross validate to a holdout sample. Encouragingly positive results were obtained. Recommendations for future research are made.