The role of consumer preferences in reducing material ...
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DOI: 10.1111/jiec.13052
R E S E A RCH AND ANA LY S I S
The role of consumer preferences in reducingmaterial intensityof electronic products
GoldContribution
AccessibilityGold
Barbara Kasulaitis1,2 CallieW. Babbitt2 Anna Christina Tyler3
1 Finger Lakes Community College,
Canandaigua, New York
2 Golisano Institute for Sustainability,
Rochester Institute of Technology, Rochester,
New York
3 Thomas H. Gosnell School of Life Sciences,
Rochester Institute of Technology, Rochester,
New York
Correspondence
CallieW.Babbitt,Golisano Institute for Sus-
tainability, Rochester InstituteofTechnology,
190LombMemorialDr, Rochester,NY14623.
Email: [email protected]
Funding information
This researchwas supportedby theNational
ScienceFoundation (CBET-1236447) and
theGolisano Institute for Sustainability at
Rochester InstituteofTechnology.
EditorManagingReview:EricMasanet
Abstract
Advances in electronic technologies have the potential to enable energy efficiency and
climate mitigation but may also create climate impacts due to resource and energy
use across the product life cycle. These tradeoffs revolve around the way electron-
ics are designed, manufactured, purchased, used, and disposed and the ability to shift
these systems toward resource efficiency. Apromising strategy for consumer electron-
ics is to facilitate adoption of lightweight, energy-efficient, multi-functional devices as
replacements for themany single-function electronic products currently owned. How-
ever, consumer preferences and willingness to make this shift remain unclear. Here,
a survey of 1,011 adults across the United States demonstrated theoretical potential
for material efficiency gains, as respondents indicated willingness to accept a smaller
number of multi-functional devices, such as smartphones, as replacements for spe-
cialized electronics, including digital cameras, camcorders, andMP3 players. However,
when actually choosing electronics to be used for common functions, such as watching
TV, getting directions, surfing the Internet, or writing an email, consumers indicated
strong preferences for products with the highest perceived quality for those tasks.
Multi-functional devices such as tabletswere only reported to be used for a small num-
ber of the functions they can provide and were typically redundant complements to
existing products, rather than substitutes. Findings suggest a limit to material inten-
sity reductions via device convergencealone.Dematerializationof this sectorwill likely
require coupled efforts to designmulti-functional products for improved performance
while also improving thematerial and climate footprint of products that consumers are
unwilling to replace. This article met the requirements for a gold-gold JIE data open-
ness badge described at http://jie.click/badges.
KEYWORDS
consumerbehavior, dematerialization, electronics, industrial ecology,material efficiency, technol-ogy and environment
1 INTRODUCTION
Innovations in consumer electronics have led to widespread diffusion of new technology across society, transforming the way people work, com-
municate, and access news, education, and entertainment. These innovations also present an opportunity for environmental co-benefits if their use
leads to systems-level material efficiency that is achieved by decoupling economic activity from environmental impacts (Bundgaard, Mosgaard, &
Remmen, 2017). For example, using electronic products to enable telework in place of commuting (Atkyns, Blazek, & Roitz, 2002; Kitou &Horvath,
Journal of Industrial Ecology 2020;1–13. © 2020 by Yale University 1wileyonlinelibrary.com/journal/jiec
2 KASULAITIS ET AL.
2003) and videoconferencing in place of long-distance travel (Coroama, Hilty, & Birtel, 2012) may avoid the resource demand and greenhouse gas
emissions that would be associated with conventional fossil-fuel-powered transportation. Embedding and integrating electronics into household
appliances and energy systems via the Internet of Things may reduce home energy consumption and facilitate adoption of energy-efficient tech-
nologies (Koomey,Matthews, &Williams, 2013; Zhou et al., 2016).
There are two primary pathways by which electronics may contribute to material efficiency, defined here as the delivery of goods and ser-
vices with reduced material and resource demand. Perhaps most widely studied is digitization, wherein electronic products are used to facilitate
digital services as a substitute for resource-intensive physical goods, including music delivery (Weber, Koomey, & Matthews, 2010), magazines
(Achachlouei & Moberg, 2015), books (Moberg, Borggren, & Finnveden, 2011), and bill delivery (Kim & Rohmer, 2012). In most cases, digitization
offers material, energy, and climate impact reductions over traditional products, but these benefits depend heavily on the way that consumers pur-
chase, use, and discard electronic products (Coroama, Moberg, & Hilty, 2015). In some cases, the material demand associated with consumption of
new electronics may actually offset or even exceed the resource efficiency associated with digitization (Hogg & Jackson, 2009).
There is also potential for material efficiency due to technological progress in electronics design and manufacturing that leads to product-level
dematerialization, defined here as the absolute or relative reduction in the quantity of materials used and/or waste generated per unit of economic
output produced (van der Voet, van Oers, & Nikolic, 2004; von Weizsäcker, 1998; Cleveland & Ruth, 1998; Wernick, Herman, Govind, & Ausubel,
1996). For example, the evolution of televisions from heavy, lead-containing cathode ray tube (CRT) technology to lighter flat screen liquid crystal
diode (LCD) technology demonstrates the potential for the sameoutput (watching television) to be achievedwith a significantly lowermaterial foot-
print (Althaf, Babbitt, & Chen, 2019). However, harnessing the dematerialization potential of technological progress also depends on consumers,
whomay actually increase consumption as products become smaller, cheaper, andmore efficient.
In fact, consumer ownership of electronic devices, such as TVs, phones, cameras, and computers, has rapidly increased in theUnited States, from
less than eight products per household in 1990 to over 20 products by 2010 (Ryen, Babbitt, Tyler, & Babbitt, 2014), estimates that are likely to
be even higher when considering parallel growth in electronics embedded in household appliances and automobiles. As a result, dematerialization
by technological progress has been partially offset by a significant expansion of the product footprint of the electronics industry (Kasulaitis, Bab-
bitt, & Krock, 2019). Growth and diversification in digital infrastructure has led to new concerns over environmental tradeoffs, including increased
dependence on critical raw materials (Ayres & Peiró, 2013), life cycle energy and climate impacts of product manufacturing and use (Deng, Bab-
bitt, &Williams, 2011; Ryen, Babbitt, &Williams, 2015), the changing carbon footprint of the information and communication sector (Malmodin &
Lundén, 2018), declining electronic product lifespan (Babbitt, Kahhat, Williams, & Babbitt, 2009), and challenges associated with end-of-life reuse
and recycling for the diversemix of products andmaterials entering the electronic waste stream (Chancerel & Rotter, 2009;Wagner et al., 2019).
For electronics to achieve their promise of material efficiency through both digitization and dematerialization, these complex devices must be
designed andmanaged to providemaximum functionalitywhileminimizing unintended resource and climate impacts. To this end, industrial ecology
research has advanced strategies aimed at achieving material efficiency in electronics through eco-design (Tecchio et al., 2018), energy efficiency
(Scott, Roelich, Owen, & Barrett, 2018), resource conservation (Wang, Kuo, Song, Hu, & Zhang, 2017), life cycle management (Mancini et al., 2015),
product leasing alternatives (Intlekofer, Bras, & Ferguson, 2010), lifespan expansion (Pérez-Belis, Bakker, Juan, & Bovea, 2017), and end-of-life
management to close material loops and advance a circular economy (Clemm et al., 2019; Ryen, Gaustad, Babbitt, & Babbitt, 2018). Despite these
efforts, a recent review highlighted a number of factors, such as increased use-phase energy as lifetimes are extended and only modest green-
house gas emission reductions through material recycling, which are likely to limit the extent to which material efficiency strategies can actually
reduce the overall climate impact of electronics (Hertwich et al., 2019).
While environmental impacts span the entire life cycle of electronic products, recent literature has pointed to consumer behavior as a key barrier
to realizingmaterial efficiency. For example, consumers presentedwith a choice of purchasing or leasing cell phones were unlikely to prefer leasing
unless they sawa significant price advantageorwere among the small percentage thatwere accustomed to leasing in thepast (Mashhadi, Vedantam,
& Behdad, 2019). Consumers also indicate unwillingness to undertake the repair of broken electronic products, particularly if the repair price is
high relative to purchasing a new product (Sabbaghi & Behdad, 2018), and often store unused or end-of-life electronics in their homes (Saphores,
Nixon, Ogunseitan, & Shapiro, 2009), limiting the flow of recentmodel devices into reuse and resale avenues. For those devices that are repaired or
remanufactured, the environmental benefits of avoiding themanufacture of a new product may be diminished if consumers only reuse the product
for a fraction of its functional lifespan (Quariguasi-Frota-Neto & Bloemhof, 2012) or if they respend the savings from purchasing a used device to
consume other goods and services that have added environmental impacts (Makov &Vivanco, 2018).
Given these challenges, it is clear that material efficiency strategies must holistically address resource and climate impacts, evolving technolog-
ical trends, and consumer behaviors. One potential solution is device convergence, which combines aspects of both digitization and dematerial-
ization, whereby consumers have access to all of the information, communication, productivity, and entertainment functionality they desire, but
by owning a smaller set of highly multi-functional electronic products rather than a larger portfolio of single- or few-function devices. Given the
exponential rate at which computing performance has increased over time, measured by processing speed or computations per unit energy input
(Denning& Lewis, 2016), it stands to reason that newproductsmay be able to offer constant or improved performancewith lessmaterial input than
previous generations (Kasulaitis, Babbitt, Kahhat,Williams, & Ryen, 2015).
KASULAITIS ET AL. 3
Device convergence is theoretically possible, as consumers currently own products with a high degree of functional redundancy, defined as
the overlap in functions available, such as the ability to record and play media across multiple products: smartphone, tablet, digital camera,
computer, television (Ryen et al., 2014). However, enacting this strategy in reality depends both on manufacturers being willing to design and sell
multi-functional products that potentially erode conventional device markets and on consumers being willing to change their electronic product
purchasing and use behaviors. It is unclear if consumers are likely to adoptmulti-functional devices as substitutes for products that have been opti-
mized for specific tasks, given the importance of task–technology fit and technology performance in consumer decisions (Goodhue & Thompson,
1995; Hsiao, 2017).
Therefore, this study aims to analyze consumer perceptions and preferences that may influence convergent device adoption and perceived abil-
ity ofmulti-functional products to fill functional “niches.” To this end, a national surveywas developed and deployedwith three goals: 1) understand
how consumers approach electronic product consumption, 2) quantify willingness to acceptmulti-functional over single- or few-function products,
and 3) identify explanatory variables underlying consumption preferences. The novelty of this research lies in the collection and analysis of empir-
ical data linking consumption drivers to product choices, as well as the evaluation of material reductions due to alternate consumption scenarios.
Results are intended to provide insight into eco-design, consumer education, informed purchasing, and other strategies aimed at achievingmaterial
efficiency across the electronics sector.
2 METHODS
2.1 Consumer Survey
Theprimarymethodusedwas a surveydeveloped to collect dataon consumerpreferences regardingproduct adoption, use, and functional substitu-
tion. The Internet-based surveywas developed by the authorswith the support ofMeliora Research, LLC, amarket research and survey design firm,
andadministered inFebruary2015. The surveywasadministered toapanel ofUnitedStates adults thatwas recruitedandmaintainedbyLightspeed
GMI, using their sampling and panel management platform. The survey protocol and informed consent process were reviewed and approved by the
Rochester Institute of Technology Institutional Review Board. The respondents’ identities remained anonymous, and researchers had no contact
with the respondents. The panel was designed to be a nationally representative population, and survey results included responses from 1,011 US
adults, aged 18 years and older. This sample size, relative to the total population of US adults, provided amargin of error of+/– 3%with a 95% con-
fidence level. Demographic characteristics of panel members were collected at the time of sampling. Statistical weighting was provided byMeliora
Research to correct distribution of respondents to alignwith theUSCensus’ Population 2014March Supplement. In determiningweighting factors,
socio-demographic characteristics, including education, age, gender, race/ethnicity, household income, and geographic region were evaluated.
The survey consisted of 40 questions designed to gain insight into the consumption and use of, attitudes toward, and relationships between
personally owned electronic products, plus 10 demographic and screening questions. The survey included a variety of question types, including
multiple choice, yes/no, and rating scalequestions. In the casewhere respondentswereoffered “Other” as ananswer choice, theywerealsoprovided
an opportunity for free response. The full text of the survey questionnaire and all raw data collected are provided as an online dataset (Kasulaitis,
Babbitt, & Tyler, 2020).Questionswithin the surveywere grouped into six thematic sections (numbered100-600), each ofwhich is described below.
One major part of the survey was understanding what products consumers currently own, how frequently they use them, and how their use of
these products has changed over time, to gauge whether multi-functional products are naturally displacing single- or few-function devices (Survey
Section 100). Respondents were asked about 14 of the most commonly owned electronics determined by past studies to comprise the typical con-
sumer electronic product "ecosystem" in the United States (Kasulaitis et al., 2019; Ryen et al., 2015). Product selection focused on digital devices
used in households (not those provided bywork or school) for personal entertainment, communication, and home–office productivity. Automobile,
appliance, and analog products were excluded, with the exception of analog CRT televisions, as they continue to be a significant contributor to total
mass of electronics in households and remain a persistent challenge for e-waste management (Althaf et al., 2019). The studies mentioned above
included a "hard-copy device" category (printers, scanners, and fax machines), which was simplified here to be called "printers." Full details of the
products selected are included in Table 1.
Next, the survey exploredhowconsumers useddifferent products for specific tasks and theperceivedquality that eachproduct provided for that
task, such as taking photos, watching television, sending emails, or surfing the Internet (Survey Section 200). This section focused on those products
that best represented consumer decisions regarding multi-functionality: smartphones, tablets, laptop computers, and televisions (flat screen). The
subsequent Survey Section (300) built on the prior two by exploring theoretical scenarios of product replacement. First, respondentswere asked to
consider rebuilding their electronics ownership as if they had to purchase all new devices "from scratch." In this hypothetical scenario, respondents
were given a fixed budget (either $1,500 or $2,500) and a list of available products with set prices that could be “purchased” using that budget
(see Table S1 in the Supporting Information). The product prices were determined based on web searches at consumer points-of-sale (electronics
retailers) and consumer reference materials (primarily the Consumer Reports electronics buying guides). Most product prices were held constant,
but prices for the digital camera ($100 or $600) and laptop ($250 or $650) were varied randomly to reflect differentmarket offerings. For example,
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TABLE 1 Consumer electronic products included in the survey scope
Product Averagemass of product (g)
Digital camera 212
Digital camcorder 212
CRT television 34,094
Flat screen television 20,779
DVD player 3,692
Blu-ray player 3,150
Game console 2,719
Smartphone 137
Basic mobile phone 115
MP3 player 93
Tablet 580
Laptop computer 2,706
Desktop computer 8,792
Printer 6,138
Mass data adapted fromBabbitt et al. (2020).
cameraprices reflected either point-and-shoot or digital SLRoptions. Eachof the eight possible scenarios from thesebudget andprice combinations
were randomly offered to respondents in a factorial design, with each respondent seeing only one such scenario (e.g., $1,500 budget with a $100
camera and $650 laptop).
Toanalyzematerial implications, thepercentof all respondentswitheachproduct currently ownedand theoretically purchasedwasmultipliedby
average product mass data summarized in Table 1 (Babbitt, Madaka, Althaf, Kasulaitis, & Ryen, 2020). Survey Section 300 questions also explored
specific multi-functional product replacement scenarios. For example, respondents who reported owning both a laptop and a tablet were asked
about likely replacement behavior if one or both of these products needed to be replaced. The intent was to determine if the products had enough
overlap to be deemed perfectly substitutable in purchasing decisions. Subsequent survey questions evaluated consumer attitudes toward adopting
specificmulti-functional products (smartphones and tablets, Survey Section400) and toward technology in general (Survey Section500) andoverall
respondent demographics (Survey Section 600). The thematic grouping of questions is shown in Table 2, which also includes research goals for each
section and example questions that were included.
2.2 Data Analysis
Survey results were analyzed in JMP 14 and spreadsheets created by the authors using Microsoft Excel. Demographic analyses were conducted
relative to factors that have been shown to strongly influence consumer electronic adoption, including age, education, income, and technological
aptitude (Goodhue & Thompson, 1995; Im, Bayus, & Mason, 2003; Leung &Wei, 1999; Morrell, Mayhorn, & Bennett, 2000). Generational groups
were defined following existing definitions put forth by the Harvard Joint Center for Housing Studies (Masnick, 2017) and respondent ages at the
time of the survey: millennials (18–30), generation X (31–50), baby boomers (51–70), and the silent generation (71–90). The analysis incorporated
weights when calculating descriptive statistics of the target population, and unweighted results were used when estimating causal effects (Solon,
Haider, &Wooldridge, 2015). Where appropriate, both the weighted and unweighted results are reported. A complete set of all results is available
in an online dataset (Kasulaitis et al., 2020), and select results to support analyseswithin this paper are provided in Tables S2–S40 in the Supporting
Information. The impact of demographic factors on number of products owned and on number of products selected for purchase was analyzed for
each factor individually using one-way analysis of variance. When significant effects were found (p < 0.05), a Tukey’s HSD post hoc analysis was
performed. Full details of these statistical analyses are included in Tables S41–S44 in the Supporting Information.
3 RESULTS AND DISCUSSION
Results are presented here relative to overall demographics of survey respondents as well as the relationships between these demographic fac-
tors and current product ownership, hypothetical product replacement preferences, and the implications on net dematerialization potential. The
KASULAITIS ET AL. 5
TABLE 2 Summary of themes, research goals, and example questions reflected in the survey design
Section theme Example questions
Survey Section 100
Ownership and frequency of product use
Indicate whether [the electronic product listed below] is available for your use.
How often do you use yourmost recently acquired version of each of the following
products?
How has use changed?
Survey Section 200
Functional use of products
Which tasks are performed on each product?
Which product is usedmost frequently for each task?
What is your general impression of the quality of experience provided by a product for an
activity?
Survey Section 300
Product interactions
Hypothetical purchasing scenario: Circumstances require you to purchase all new
electronic products up to a budget limit.Which products would you buy?
Prices for each product stated with limited variability.
If a product was broken, would you replace it, and if so, with the same or a different
product?
If replacing a product with a different product, what type?
If two products broke and could not be repaired, would you replace one, both, neither, or
purchase an entirely different product?
Survey Section 400
Ownership preferences
What reasons would prevent you from owning a tablet or smartphone if it were free?
Survey Section 500
Attitudes toward technology
How likely are you to adopt new technology?
How interested/knowledgeable are you about technology?
Survey Section 600
Demographics
Standard demographic questions including age, gender, education, and household
income.
theoretical potential for material efficiency through device convergence is explored relative to consumers’ stated preference for given products
with respect to specific tasks andwillingness to accept multi-functional devices even if quality is less than that of a specialized device.
3.1 Survey respondents
Among the 1,011 survey respondents, 189 (19%) weremillennials, 372 (37%) were generation X, 366 (36%) were baby boomers, and 84 (8%) were
from the silent generation. Respondents represented all regions of the United States. Average household income was just over $65,000 and 434
(43%) respondents completed a4year degreeor higher.Over60%of all respondents self-identified as being very interestedor extremely interested
in technology products for home use, while only about one third said that they were very or extremely likely to be the first among their friends to
adopt new products. Approximately one half indicated a belief that they were very or extremely knowledgeable about technology products for
home use. The complete analysis of respondent demographics is provided in Table S2 in the Supporting Information.
3.2 Current and minimum theoretical product ownership
On average, respondents reported owning 7.6 +/− 2.7 products (mean +/− standard deviation), which collectively represented a material inten-
sity of 41 kg, according to the average mass of each product. This estimate represents the number of unique types of products owned and does
not account for respondents who may own multiple products of each type (e.g., two smartphones). Product ownership was significantly higher for
younger generations, higher income demographics, and respondentswith the highest reported interest in technology (Figure 1). On the other hand,
respondents generally indicated that if theywere recreating their electronics ownership fromnothing, theywould only select an average of 5.7+/−
2.3 products to purchase, representing a material intensity of 27 kg. While these results suggest a theoretical potential of about 30% reduction in
netmass, therewas notable heterogeneity across respondent categories. Amaximumof about 25 kgmass reductionwas observed for respondents
with household incomegreater than$75,000whereas aminimumof less than2.5 kgmass reduction potentialwas seen for respondentswith lowest
interest in technology, although this group owned the fewest products initially (Figures S9–S12 in Supporting Information).
This variability was attributed to specific products that consumers currently own relative to ones they would choose if starting from scratch.
For example, those with higher incomes and greater overall interest in technology were more likely to choose tablets, laptops, and smartphones
to purchase, and less likely to select desktop computers, digital cameras, camcorders, dedicated MP3 players, audio visual equipment, and gaming
consoles, even though their current ownership included those products at high rates. About one third of the dematerialization potential stemmed
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(1 = none, 5 = extremely interested)
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a a
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Currently owned Would purchase
F IGURE 1 Total number of products currently owned and selected for hypothetical purchase (mean+/− standard error) for eachdemographic group. Unique letters over markers for product availability (black square) and under products selected for "purchase" (orange circle)indicate a significant difference among demographic groups based on Tukey’s HSD post hoc analysis. Data used to create this figure are available atKasulaitis et al. (2020) and summarized in Table S2 in the Supporting Information
from natural technological progress, as seen for respondents who currently owned a large CRT television but were not offered that option in the
theoretical purchase scenario because it was no longer sold on themarket at the time of the survey.
Another factor that may influence potential dematerialization is product cost relative to overall economic conditions. The hypothetical purchas-
ing question was presented as a scenario in which all of the consumer’s existing electronics products were lost or destroyed, and they had a fixed
budget (randomized at $1,500 or $2,500) to purchase new products. Most product prices were fixed, with the exception of the laptop ($250 or
$650) and digital camera ($100 or $600). While most respondents selected fewer products than currently available to them, the majority chose a
smartphone, flat screen television, and laptop computer, regardless of budget and product prices (Figure 2, see Tables S18 and S19 in the Support-
ing Information for a full product breakdown). While these products have a strong degree of theoretical convergence (e.g., media can be viewed
on all of them), consumers signaled preference for independent devices. In fact, consumers selected the laptop first in five of the eight randomized
scenarios, including those where the budget wasmost constrained (survey results reported the order in which respondents “purchased” products).
The television and the smartphone were each chosen first in two cases (television and laptop ‘tied’ in one instance). Despite the multi-functional
potential of the tablet, consumers weremore likely to choose it later in their selections, especially in cases with the greatest budget constraint.
Although these findings suggest that future consumption could tend toward reduced material use through device convergence, the results do
have limitations. First, products included in the survey were relatively mature. In other words, all had been on themarket throughmultiple product
generations and were widely recognized. As a result, information available for these products would make them accessible, while at the same time,
technological progress would make them affordable. Further, research has shown that attitudes impact prediction of behavior (Ajzen, 2001), and
self-prediction of behavior is generally optimistic (Vietri, Chapman, & Schwartz, 2009). Finally, the scenario presented (replacing all products at
once) is unlikely to be experienced by most consumers, who would instead typically replace products individually. Because of these factors, the
purchasing scenario shouldbe consideredas amaximumtheoretical dematerialization threshold,with realistic reduction inmaterial intensity falling
somewhere below this estimate.
While climate impacts are not quantified directly here, it is expected that potential dematerialization would correlate with reduced greenhouse
gas emissions, as the products predominantly selected are those with typically lower manufacturing and use-phase energy consumption profiles
KASULAITIS ET AL. 7
150 100 50 0 50 100 150
Digital Camera
Basic Mobile Phone
Tablet
Smartphone
Flat Screen TV
Laptop
Number of respondents
$1,500 Budget $2,500 Budget
Camera $600, Laptop $650 Camera $600, Laptop $250
Camera $100, Laptop $650 Camera $100, Laptop $250
*
****
* *
* *
**
F IGURE 2 The number of respondents who selected each product in the hypothetical purchasing scenarios (with randomized available budgetand product prices). *Denotes that product was the first option selected by the largest number of respondents in that scenario (**laptop and flatscreen television tied for first choice). Data used to create this figure are available at Kasulaitis et al. (2020) and summarized in Tables S18 and S19in the Supporting Information
compared to larger, legacy alternatives (Ryen et al., 2015). Further, overall dematerialization of the electronic product ecosystem is expected to
reduce the net consumption of preciousmetals like gold (Kasulaitis et al., 2019), which have high embodied energy and carbon footprints associated
with their supply chain. On the other hand, the purchasing scenario is consistent with recent shifts to smaller mobile products containing lithium-
ion batteries, which rely on potentially scarce metals like cobalt that are challenging to recycle for resource recovery (Richa, Babbitt, Gaustad, &
Wang, 2014). Demand for these metals may worsen the bottlenecks faced by clean energy technologies (Fu et al., 2020), which require the same
materials to realize their systems-level climate mitigation potential. An additional conundrum is that consumers who are “extremely” interested
in technology products seem to be the most willing to adopt and use convergent products, but they are also likely to own more products in total,
limiting overall impact reduction potential. An area for future study is whether such technology-oriented consumers also spend more time using
their devices, which could result in higher use-phase climate impacts.
3.3 Task–technology fit for multi-functional products
To better understand consumer adoption decisions and begin to identify drivers of functional redundancy, respondents were asked about the tasks
they had performed in the month preceding the survey using three functionally overlapping products: smartphone, tablet, and laptop. Results sug-
gest thatmulti-functional products areonlyused for aportionof theactivities they support, a behaviorwhichmay limitmaterial efficiency (Figure3).
For example, those who owned all three of these products (n= 388) indicated strong tendencies toward assigning specific functional tasks to each
device.
In general, smartphones were used for activities benefiting from convenience andmobility while laptops were used for activities requiringmore
detail. More than half of respondents who made voice or video calls or looked up driving directions exclusively used the smartphone for those
applications. Similarly, almost 50% of respondents who typed or edited documents or conducted other productivity tasks, and almost one third of
respondents whomade online purchases or performed e-commerce transactions, did so exclusively with the laptop. Further, more than half used a
flat screen television towatchTVormovies, despite theability to viewmediaonall threeotherdevices that theyowned.Additional leisure activities,
including social media, browsing the Internet, and playing games, were consistently themost frequently performed activity across all products.
These findings are consistent with past research showing that some products are, or are perceived to be, optimized for particular activities.
In some cases, multi-functional products that seem to be aligned with specific activities, as the tablet is for entertainment (Li, 2014), may not be an
acceptable substitute for existingproducts, and their redundant consumptionmay increase thenetmaterial footprintwithout providing a functional
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Product use maximization Functionally redundant users Survey sub-population
F IGURE 3 Use patterns for the smartphone, tablet, and laptop for the subpopulation of survey respondents who have all three products(n= 388; shown by dashed line at top of figure). Results show the number of respondents who used each product for each task. Product usemaximization (indicated by the dark gray horizontal marker) represents the total users who performed that activity, while the bars represent theusers who used each specific product for the activity. Diamonds indicate functionally redundant users who performed the function on all three ofthe devices. Data used to create this figure are available at Kasulaitis et al. (2020) and summarized in Tables S8–S10 in the Supporting Information
benefit. On the other hand, some multi-functional products almost completely replace single-function or specialized devices, as the smartphone
did for the point and shoot camera (Shu, 2016), automotive GPS navigation systems, and MP3 audio players (Althaf et al., 2019), creating a net
reduction of material intensity by owning one product instead of multiple devices. The heterogeneity in the degree to which multi-functionality is
maximized is likely due to consumer preferences for certain features when determining task–technology fit. For example, a smartphone may be a
partially acceptable option for typing an email or document when pressed for time, but users are likely to prefer the ergonomics of a full keyboard
and visual quality of a full screen for extended use. Thus, it is expected that multiple products will continue to be consumed according to specific
task–technology preferences, limiting the ability to dematerialize through device convergence, at least for the products analyzed here.
3.4 Perceived quality of multi-functional products
To explore consumer willingness to adopt fewer, convergent devices and identify leverage points for material efficiency, the utility of each product
for given tasks was also considered. Building on the results in the previous section, product use for three common tasks was analyzed based on
the respondent’s revealed product preference, defined as the single product they used most frequently to perform an activity in the last month, in
comparison to their perception of the quality of experience provided by the product for that activity. Note that respondents were allowed to make
their own determination of what constituted quality of experience for each product, so results likely reflect a mix of factors, including ease of use,
ergonomics, convenience, mobility, and sound or image clarity.
When asked which single product had been used most in the months preceding the survey, more than 60% used the laptop most frequently to
write or edit documents andmore than 40% indicated that they used a laptopmost frequently for surfing the Internet andwriting or sending emails.
Although over 80% of the subpopulation used a smartphone to browse the Internet or to write or send emails on occasion (Figure 3), only about
25%used the smartphonemost frequently for these tasks. Adding to the dematerialization conundrum,many respondents used a different product
altogether, increasing functional redundancy. For example, although all three multi-functional products in this comparison can be used to watch
television or movies, over half used a televisionmost frequently (Figure 4).
User perceptions of the quality of experience provided by a product for a given task appear to be a driving factor for redundant consumption
(Figure 4). For over 65%of respondents, themost frequently used productwas perceived as delivering the best possible experience for a given task,
such as watching television or movies, browsing the Internet, or sending emails. The vast majority of respondents (86%) who used the television
most frequently for watching TV or movies rated it as offering the best possible experience, and no respondents rated it as providing minimally
KASULAITIS ET AL. 9
F IGURE 4 User perceptions of quality provided by the product usedmost frequently to watch television andmovies (subpopulation ofrespondents who have three functionally redundant products: smartphone, tablet, and laptop). Inner segments correspond to the percent ofrespondents who used the specified product most frequently; outer segments correspond to how those respondents rated the quality of thatproduct for watching TV andmovies, in order of relative number of responses for “best,” “satisfactory,” and “minimum” quality (minimum does notappear for all products due to space limitations, but is reflected by the lightest color segment). For all products except the tablet, users primarilyevaluated themost used product as providing the “best” quality experience. For the tablet, users most often regarded it as providing “satisfactory”quality. Data used to create this figure are available at Kasulaitis et al. (2020) and summarized in Tables S11 and S13 in the Supporting Information
acceptable or unacceptable quality. Conversely, almost half of respondents who indicated that they used one of the three functionally redundant
products—smartphone, tablet, laptop—most frequently towatch television andmovies perceive that product as offering a satisfactory orminimally
acceptable experience.While the specific decision-making process was not probed, it may be attributed to increased convenience and potential for
mobile viewing, which is likely to be further expanded in the future given the surge in streaming media content options entering the market since
this survey was administered. This case also highlights a compounding factor working against dematerialization through device convergence: user
preference is not only driven by task or activity, but also by setting. For example, when watching movies or TV at home, consumers preferred a
television, but theymight watchmovies or television in a different settingmore often, necessitating the purchase of an additional product.
Finally, the case of the tablet versus the laptop computer highlights the risk of relying on new, multi-functional products to replace legacy
products. Despite the early expectation that the tablet would outcompete and drive the laptop to extinction (O’Connell, 2013), it was rarely the
most frequently used product for any activity, and actually far less frequently used than the laptop. The tablet was judged by respondents to have
comparable or even lower quality than the smartphone. Results underscore the challenges faced by multi-functional products as part of the mate-
rial efficiency solution. Unless perception of their quality is improved, through design, material selection, and functionality, they are unlikely to be
accepted by consumers as full substitutes for technologies optimized for given tasks. However, this finding also suggests that a better alternative
may be to improve multi-functionality of those products deemed best for essential functions. For example, the laptop was the product considered
most desirable for e-commerce andwriting documents, and the smartphone for navigation and communication. Thus, adding convenience functions
to laptops and phonesmay ultimately reduce the need for tablets altogether. New options have entered themarket since this survey was deployed,
10 KASULAITIS ET AL.
Replace with same make/ model
Replace with different make/ model of the
same type of product
Replace with different product
Use available product
Do not replace
Tabletonly
Laptoponly
Neither
Replace both
Replace with a different product
F IGURE 5 Assessment of product substitutability according to likely replacement decisions by respondents if their laptop, tablet, or bothproducts broke and needed to be replaced. Data used to create this figure are available at Kasulaitis et al. (2020) and summarized in Tables S23 andS25 in the Supporting Information
such as laptops with touch screens, removable keyboards, and foldable configurations, and smartphones with larger screens and more processing
power, whichmay provide themechanismwhereby redundant product ownership becomes less common.
3.5 Direct product substitutability
While the above sections assess consumer acceptance of multi-functional products indirectly, this section directly analyzes substitutability by way
of product replacement. Respondents who reported owning both a tablet and laptop were presented with scenarios in which one or both of these
products broke and asked whether and how they would make their replacement decisions. Among this subpopulation, nearly 80% indicated that
they would replace the broken product with either the same make and model, or with a different make and model of the same type of product
(Figure 5). Of the 10%who indicated they would replace it with a different product, only 30% chose the tablet as a replacement for a broken laptop
while 40% chose the laptop to replace a broken tablet. However, when asked about replacing both products at the same time, less than 30% indi-
cated they would choose this option; approximately 25% said they would replace only the tablet and over 40% indicated they would replace only
the laptop.When asked a similar question about replacing a broken tablet and television or a broken tablet and smartphone at the same time, 12%
and 8%, respectively, would replace only the tablet, and 36% and 38%, respectively, would replace both, while 47% and 50% would replace only
the television or smartphone, respectively (see Tables S23 and S25–S27 in the Supporting Information). These results confirm that the tablet is not
perceived as a realistic substitute for the laptop, television, or smartphone.
Despite the tablet’s design andmarketing as a multi-functional generalist, it was seen to interact with other products rather than displace them.
This redundant consumption ultimately works againstmaterial efficiency goals of device convergence. A small set of respondents (n= 26) reported
purchasing their currently owned smartphone and tablet at the same time, and 31% of those respondents indicated that the ability to use the two
products together was the driving factor behind the decision. Almost one third of respondents who owned a tablet indicated that the availability
KASULAITIS ET AL. 11
of a smartphone in their suite of products increased their decision to purchase the tablet, and almost one quarter of respondents who owned a
tablet indicated that the presence of a laptop or television in their product suite increased their purchase decision. Similarly, of respondents who
owned a smartphone, about one third reported their decision to purchase it was increased by already owning a tablet (see also Tables S28–S31 in
the Supporting Information). These results suggests that products interact in such a way that increases consumption of both. Only 14% of those
who have a smartphone but not a tablet (n= 248) and 10% of those who have a tablet but not a smartphone (n= 96) indicate that the presence of
the other product in their community would prevent them from accepting a free tablet or smartphone, respectively, while 57% and 24% indicate
that nothing would prevent them from accepting a free tablet or smartphone, respectively (Tables S32 and S33 in the Supporting Information).
4 CONCLUSIONS
Results from this survey suggest that consumers are unlikely to adopt a minimally redundant portfolio of multi-functional products as substitutes
for functionally specialized alternatives, particularly if the quality and task fit are not perceived as equivalent. While not directly quantified here,
consumer responses suggest that other factors, such as convenience and mobility, likely influence consumption and use decisions. These findings
suggest a limit to the potential for material efficiency gains via device convergence alone. The greater opportunity for dematerialization may be
through technological progress that tends toward lightweighting over time, as seen in the transition from largeCRT televisions to lighter flat screen
alternatives that has been ongoing since this survey was deployed (Althaf et al., 2019).
On the other hand, current consumption trends suggest that products may reach the limit of natural dematerialization and that continued tech-
nological innovation, such as the device proliferation expected of the Internet of Things (Xia, Yang, Wang, & Vinel, 2012), may ultimately lead to
greater electronic productmaterial intensity, energy use, and climate impacts. If thematerial efficiency promise of device convergence is to be real-
ized, itwill require a significant increase in consumer’s interest in technologyproducts andknowledgeof product capabilities.Dematerializationwill
also require a parallel shift in industry’s approach to product design to enhance both the performance andmulti-functional capabilities for existing
electronic products and emerging smart technologies. These types of system interventions are often limited by user-induced effects, such as form
factor lock-in and behavioral rebound, whichmayminimize improvements in material consumption.
Material efficiency within the electronics sector will likely require a broader suite of strategies, including disruptive innovation to replace larger
devices with less material intense, service-oriented alternatives, and continued efforts to expand recycling infrastructures and secondary mar-
kets to displace primary material demand and reduce associated climate impacts. Further, consumers play a key role in these strategies, and more
research is required to understand sustainable behavior drivers. Efforts toward encouraging sustainable behavior should investigate further the
factors that may lead consumers to purchase a minimally redundant product ecosystem and create responsive solutions, including design, manu-
facturing, business models, policies, and educational initiatives to inform and encourage this type of behavior.
ACKNOWLEDGMENTS
The authors gratefully acknowledge and thank Dr. Erinn Ryen, Mona Komeijani, and Gabrielle Thurston at RIT for their assistance in developing
and pre-testing the survey instrument, Jon Siegel atMeliora Research for facilitating the survey development and instrumentation, andDr. Gregory
Babbitt and Dr. EricWilliams for their feedback on research interpretation.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ORCID
CallieW.Babbitt https://orcid.org/0000-0001-5093-2314
AnnaChristina Tyler https://orcid.org/0000-0002-7845-995X
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SUPPORTING INFORMATION
Additional supporting informationmay be found online in the Supporting Information section at the end of the article.
How to cite this article: Kasulaitis B, Babbitt CW, Tyler AC. The role of consumer preferences in reducingmaterial intensity of electronic
products. J Ind Ecol. 2020;1–13. https://doi.org/10.1111/jiec.13052