scholarly journals Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels

2013 ◽  
Vol 135 (6) ◽  
Author(s):  
Heidi Q. Chen ◽  
Tomonori Honda ◽  
Maria C. Yang
Keyword(s):  
Consumer Preferences ◽  
Stated Preference ◽  
Revealed Preference ◽  
Time Span ◽  
Solar Panels ◽  
Photovoltaic Panels ◽  
Machine Learning Methods ◽  
Technology Products ◽  
Technical Specifications

This paper investigates ways to obtain consumer preferences for technology products to help designers identify the key attributes that contribute to a product's market success. A case study of residential photovoltaic panels is performed in the context of the California, USA, market within the 2007–2011 time span. First, interviews are conducted with solar panel installers to gain a better understanding of the solar industry. Second, a revealed preference method is implemented using actual market data and technical specifications to extract preferences. The approach is explored with three machine learning methods: Artificial neural networks (ANN), Random Forest decision trees, and Gradient Boosted regression. Finally, a stated preference self-explicated survey is conducted, and the results using the two methods compared. Three common critical attributes are identified from a pool of 34 technical attributes: power warranty, panel efficiency, and time on market. From the survey, additional nontechnical attributes are identified: panel manufacturer's reputation, name recognition, and aesthetics. The work shows that a combination of revealed and stated preference methods may be valuable for identifying both technical and nontechnical attributes to guide design priorities.

An Approach for Revealed Consumer Preferences for Technology Products: A Case Study of Residential Solar Panels

2012 ◽  
Author(s):  
Heidi Q. Chen ◽  
Tomonori Honda ◽  
Maria C. Yang
Keyword(s):  
Machine Learning ◽  
Consumer Preferences ◽  
Solar Panels ◽  
Learning Methods ◽  
Photovoltaic Panel ◽  
Machine Learning Methods ◽  
Technology Products ◽  
Technical Specifications ◽  
S Curve

Consumer preferences can serve as an effective basis for determining key product attributes necessary for market success, allowing firms to optimally allocate time and resources toward the development of these critical attributes. However, identification of consumer preferences can be challenging, particularly for technology-push products that are still early on in the technology diffusion S-curve, which need an additional push to appeal to the early majority. This paper presents a method for revealing preferences from actual market data and technical specifications. The approach is explored using three machine learning methods: Artificial Neural Networks, Random Forest decision trees, and Gradient Boosted regression applied on the residential photovoltaic panel industry in California, USA. Residential solar photovoltaic installation data over a period of 5 years from 2007–2011 obtained from the California Solar Initiative is analyzed, and 3 critical attributes are extracted from a pool of 34 technical attributes obtained from panel specification sheets. The work shows that machine learning methods, when used carefully, can be an inexpensive and effective method of revealing consumer preferences and guiding design priorities.

scholarly journals Effect of Dust and High Temperature on Photovoltaics Performance in the New Capital Area

2021 ◽  
Vol 17 ◽  
pp. 360-370
Author(s):  
Engy Elshazly ◽  
Ahmed Α. Abd El-Rehim ◽  
Amr Abdel Kader ◽  
Iman El-Mahallawi
Keyword(s):  
High Temperature ◽  
Negative Impact ◽  
Environmental Parameters ◽  
Electrical Performance ◽  
Solar Photovoltaic ◽  
Solar Panels ◽  
Photovoltaic Panels ◽  
Cost Of Energy ◽  
Dust Accumulation

The trend for integrating solar Photovoltaic Panels as an alternative renewable and sustainable energy source is growing in Egypt, North Africa and the Middle East. However, these efforts are not widely accepted by the society due to their lower efficiencies. The efficiency of the photovoltaic panels is affected by many environmental parameters, which have a negative impact on system efficiency and cost of energy, dust and increased panel temperatures being the most serious. This work presents the results of a case study conducted at The British University in Egypt at El-Sherouk city to study the effect of different parameters such as dust accumulation, water cooling and coating on their performance of both mono- and poly-crystalline panels at El-Sherouk City. The effects of high temperature and dust accumulation on different solar panels placed in natural outdoor conditions at El-Sherouk City were studied and the electrical performance of dusted, cleaned, and cooled PV panels is presented. The variation in the efficiency of mono-crystalline panels installed at different tilt angles, resulting from the accumulation of dust on their surface, was also studied. The results showed that the accumulation of dust on the surface of different types of solar panels can reduce the efficiency by 30%. While the high temperature can reduce the efficiency by up to 10 %. The results showed that the power reduction percentage was 17%, 20%, 25%, 27% and 30% for tilt angles 60°,45°,30°,15° and 0°; respectively. Tilt angles 15° and 30° showed to be optimal for the installation of the PV solar system, as they resulted the highest amount of output power

scholarly journals Unconstrained Estimation of Multitype Car Rental Demand

2021 ◽  
Vol 11 (10) ◽  
pp. 4506
Author(s):  
Yazao Yang ◽  
Avishai (Avi) Ceder ◽  
Weiyong Zhang ◽  
Haodong Tang
Keyword(s):  
Stated Preference ◽  
Revealed Preference ◽  
Repair Process ◽  
Discrete Choice Models ◽  
Difficult Problem ◽  
Average Error ◽  
Demand Forecast ◽  
Car Rental ◽  
Future Demand

The unconstrained demand forecast for car rentals has become a difficult problem for revenue management due to the need to cope with a variety of rental vehicles, the strong subjective desires and requests of customers, and the high probability of upgrading and downgrading circumstances. The unconstrained demand forecast mainly includes repairing of constrained historical demand and forecasting of future demand. In this work, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand. In addition, the linear Holt–Winters model and the nonlinear backpropagation neural network are combined to predict future demand and avoid excessive errors caused by a single method. In a case study, we take advantage of a stated preference and a revealed preference survey and use the variable precision rough set to obtain factors and weights that affect customer choices. In this case study and based on a numerical example, three forecasting methods are compared to determine the car rental demand of the next time cycle. The comparison with real demand verifies the feasibility and effectiveness of the hybrid forecasting model with a resulting average error of only 3.06%.

scholarly journals Choice Modeling for Usage Context-Based Design

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
Lin He ◽  
Wei Chen ◽  
Christopher Hoyle ◽  
Bernard Yannou
Keyword(s):  
Hybrid Electric Vehicle ◽  
Consumer Preferences ◽  
Stated Preference ◽  
Revealed Preference ◽  
Choice Modeling ◽  
Modeling Framework ◽  
Preference Data ◽  
Design Paradigm ◽  
Usage Context ◽  
The Impact

Usage context-based design (UCBD) is an emerging design paradigm where usage context is considered as a critical part of driving factors behind customers’ choices. Here, usage context is defined as all aspects describing the context of product use that vary under different use conditions and affect product performance and/or consumer preferences for the product attributes. In this paper, we propose a choice modeling framework for UCBD to quantify the impact of usage context on customer choices. We start with defining a taxonomy for UCBD. By explicitly modeling usage context’s influence on both product performances and customer preferences, a step-by-step choice modeling procedure is proposed to support UCBD. Two case studies, a jigsaw example with stated preference data and a hybrid electric vehicle example with revealed preference data, demonstrate the needs and benefits of incorporating usage context in choice modeling.

scholarly journals An Investigation on the Inconsistency of Consumer Preferences for Product Appearance: A Case Study of Residential Solar Panels

2014 ◽  
Author(s):  
Qifang Bao ◽  
Sami El Ferik ◽  
Mian Mobeen Shaukat ◽  
Maria C. Yang
Keyword(s):  
Conjoint Analysis ◽  
Consumer Preferences ◽  
Consumer Products ◽  
Solar Panel ◽  
The Other ◽  
User Needs ◽  
Significant Shift ◽  
Solar Panels ◽  
Product Appearance

The importance of the appearance of consumer products is widely understood. This paper considers an evaluation of the appearance of a technology-oriented product, the residential solar panel, from the perspective of individuals. This study uses a quantitative approach, visual conjoint analysis, to determine preferences for product appearance of solar panels, and further explores how presenting a solar panel in its context of use can influence the consistency of consumer preferences. Approximately 200 survey respondents were shown two kinds of images of solar panels, one of a standalone panel and the other of a panel installed on a roof. Results show a significant shift of preferences when first showing the non-contextualized image and then showing the contextualized image. Such preference inconsistency provides insights with which to inform the process of user-needs revealing.

scholarly journals Market value of the view restriction

2019 ◽  
Vol 11 (1) ◽  
pp. 1925-1932
Author(s):  
István Hajnal
Keyword(s):  
Stated Preference ◽  
Revealed Preference ◽  
Market Value ◽  
Evaluation Study ◽  
Building Construction ◽  
Delphi Approach ◽  
Negative Effect ◽  
The Impact ◽  
Creative Construction

Abstract Based on the international literature, the effect of an existing panoramic view on the market value of properties is positive and significant. This value-adding factor varies by location and by type of view. In Central Europe, no such evaluation study has been elaborated until now. New building construction may restrict the existing panorama, and this is the other side of the same phenomenon. View restriction may result in stigmatization, which is a negative effect on the property. There are two major methodologies to observe the effect: revealed preference method (RPM) and stated preference method (SPM). One SPM approach is contingent valuation (CV), wherein well-informed stakeholders give their opinion about the impact caused by the investigated effect. The CV methodology, using the Delphi approach, was employed to observe the market value decrease in the cases of several restricted panorama situations in Budapest. Based on the research, this effect in Budapest is in line with the published western results. The result of the study can be used to support real estate developers and architects in their development decisions. This is an extended version of the article titled “The impact of view-restriction: a Delphi case study from Budapest”, presented at Creative Construction Conference 2018, CCC 2017, 30 June to 3 July 2018, Ljubljana, Slovenia.

scholarly journals Mobility surveys beyond stated preference: introducing MyTrips, an SP-off-RP survey tool, and results of two case studies

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Christian Rudloff ◽  
Markus Straub
Keyword(s):  
Case Studies ◽  
Latent Class ◽  
Stated Preference ◽  
Revealed Preference ◽  
Hypothetical Bias ◽  
Rural Setting ◽  
Daily Routine ◽  
Survey Tool ◽  
First Case

AbstractWhen introducing new mobility offers or measures to influence traffic, stated preference (SP) surveys are often used to assess their impact. In SP surveys, respondents do not answer questions about their actual behaviour, but about hypothetical settings. Therefore, answers are often biased. To minimise this hypothetical bias, so-called stated preference-off-revealed preference (SP-off-RP) surveys were developed. They base SP questions on respondents’ revealed behaviour and place unknown scenarios in a familiar context. Until now, this method was applied mostly to scenarios investigating the willingness to pay. The application to more complex mode or route choice problems, which require the calculation of routes, has not yet been done. In this paper, the MyTrips survey tool for the collection of SP-off-RP data based on respondents’ actual mobility behaviour is presented. SP questions are based on alternatives to typical routes of respondents, which are calculated on the fly with an intermodal router. MyTrips includes a larger survey and collects mobility diaries for one day representing respondents’ daily routine, calculates alternative routes and creates SP questions based on a Bayesian optimal design. Results from two case studies investigating behaviour changes are presented. The first case study investigated the extension of a subway line in Vienna, Austria. The second case study focused on the introduction of micro transit vehicles in a rural setting, replacing infrequent bus services. Results of the two case studies show a difference in response behaviour between SP and RP settings and suggest a reduction of hypothetical bias. For the latter study, a Latent Class SP-off-RP model was estimated. It shows that availability and accessibility of public transport are the main influences on the willingness to use it, independent of other household characteristics.

Consumer preferences for low-salt foods - a Danish case study based on a comprehensive supermarket intervention

2021 ◽  
pp. 1-26
Author(s):  
Sigrid Denver ◽  
Tove Christensen ◽  
Jonas Nordström
Keyword(s):  
Consumer Preferences ◽  
Salt Content ◽  
Dietary Advice ◽  
Scanner Data ◽  
Statistical Regression ◽  
Salt Consumption ◽  
Dietary Choices ◽  
Low Salt ◽  
Reduced Salt

Abstract Objective: The objective is to analyze Danish consumers’ attitudes to buying food with reduced salt content. Design: The study is based on a comprehensive store intervention that included 114 stores belonging to the same supermarket chain. Three different salt claims were tested for eight weeks on six test products within the categories bread, cornflakes and frozen pizzas. Scanner data were supplemented with 134 brief interviews with consumers in nine selected stores. Setting: Stores spread across Denmark. Participants: Consumers who buy food in the stores. Results: Statistical regression analyses of the scanner data indicated that none of the three claims significantly affected demand for any of the test products. The interviews confirmed that many consumers were more focused on other elements of the official dietary advice than reduced salt consumption, such as eating plenty of vegetables, choosing products with whole grains and reducing their intake of sugar and fat. Conclusions: Overall, both the scanner data and the interviews pointed in the same direction, toward the conclusion that salt content is often a secondary factor when Danish consumers make dietary choices.

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