Water Conserving Message Influences Purchasing Decision of Consumers

Abstract

As more of the U.S. population urbanizes, freshwater resources will become more partitioned and scarcer. Live plants need water to become established and survive, but water demands vary by taxa. Additionally, outdoor household water use is becoming a greater target for watering restrictions, particularly landscape irrigation. Yet, how important is water conservation messaging in the context of a plant purchasing decision for outdoor plants? A ratings-based conjoint analysis of the water message, plant type, price, and plant guarantee was conducted using data from 288 subjects from three locales who rated their purchase intention to buy a plant from a retail merchandising display while using an eye-tracking device. Subjects were clustered by state of residency and, separately, their conjoint utility scores. Results indicate that water-related messaging does play a positive role in purchase intention. Residents of states who frequently experienced drought rated a water-saving message higher compared to residents of states who experienced relatively little drought. For some consumer groups, such as plant buyers, water savings are important and sought after. Green industry stakeholders should be aware of their region’s drought history and help develop point of purchase information to include water conservation messaging in retail displays.

Consumer Preferences of Ratibida columnifera (Nutt.) Wooton & Standl. Floral Characteristics

“…Conjoint clusters. The price conscious bicolor petal lovers fall into the age range of the Gen X generation (individuals born between 1965 and 1980), as opposed to the single color oval petal lovers and the knowledgeable marbled petal lovers who are both in the Gen Y population (individuals born between 1981 and 1995) (Behe et al, 2016;Knuth et al, 2020). This group is the most price conscious, having the greatest preference for lower prices and greatest aversion to higher prices.…”

“…Relative importance is defined as a measure of importance of the contribution of each attribute to overall preference, and is calculated by dividing the range of utilities for each attribute by the sum of all ranges and multiplying by 100 (Kuhfeld, 2010). Principal component analysis describes the strength and direction of correlated variables in terms of their potential to quantify unobservable constructs (Knuth et al, 2020). Principal component analysis was performed using SAS and retained 10 items of 19 possible items due to their factor loadings being $0.500 (Knuth et al, 2020).…”

“…Principal component analysis describes the strength and direction of correlated variables in terms of their potential to quantify unobservable constructs (Knuth et al, 2020). Principal component analysis was performed using SAS and retained 10 items of 19 possible items due to their factor loadings being $0.500 (Knuth et al, 2020). This solution was unrotated.…”

“…Kaiser-Meyer-Olkin measure was 0.875, and Bartlett's test of sphericity x 2 was 3974.252. The loading value is used in principal component analysis to indicate the mean value for each item (question), being highest among all of the mean values for that item when testing for linear combinations (Knuth et al, 2020). Statistical analysis used in this study was as follows: principal component analysis on native plant questionnaire using IBM SPSS Statistics 25 (IBM SPSS Statistics for Windows, Version 25.0.…”

Landscape Irrigation and Water Conservation in Urban Areas: An Analysis of Information-based Strategies

“…However, the effectiveness of using education tools to change water use behaviors is mixed (Landon et al, 2016). Some researchers have found water-saving signage at the point of purchase can impact consumers' landscape plant purchasing decisions (Knuth et al, 2020). Some argue that education campaigns are likely necessary but not sufficient to promote behavior changes on their own (Beattie, 2010;Dolnicar and Hurlimann, 2010).…”