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
6 KASULAITIS ET AL.
4
5
6
7
8
9
0 1 2 3 4Generation
4
5
6
7
8
9
0 1 2 3 4 5Interest in Technology
(1 = none, 5 = extremely interested)
4
5
6
7
8
9
0 50,000 100,000 150,000Household Income
Num
ber o
f pro
duct
s
4
5
6
7
8
9
0 1 2 3 4 5 6Years of post secondary education
a a
bb
ede
cdbc
ab ab aba
d
cd
c
b
a
aa aabb
yxx
xxy
Millennial Gen X Baby Boomer Silent
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
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
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.
Achachlouei, M. A., & Moberg, Å. (2015). Life cycle assessment of a magazine, Part II: A comparison of print and tablet editions. Journal of Industrial Ecology,19(4), 590–606. https://doi.org/10.1111/jiec.12229
Ajzen, I. (2001). Nature and operation of attitudes.Annual Review of Psychology, 52(1), 27–58. https://doi.org/10.1146/annurev.psych.52.1.27Althaf, S., Babbitt, C. W., & Chen, R. (2019). Forecasting electronic waste flows for effective circular economy planning. Resources, Conservation and Recycling,
151, 104362.Atkyns, R., Blazek,M., & Roitz, J. (2002). Measurement of environmental impacts of telework adoption amidst change in complex organizations: AT&T survey
methodology and results. Resources, Conservation and Recycling, 36(3), 267–285. https://doi.org/10.1016/S0921-3449(02)00082-4Ayres, R. U., & Peiró, L. T. (2013). Material efficiency: Rare and critical metals. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engi-
Babbitt, C.W., Kahhat, R.,Williams, E., & Babbitt, G. A. (2009). Evolution of product lifespan and implications for environmental assessment andmanagement:
A case study of personal computers in higher education. Environmental Science & Technology, 43(13), 5106–5112.Babbitt, C.W.,Madaka, H., Althaf, S., Kasulaitis, B. V., &Ryen, E. G. (2020). Disassembly-based bill ofmaterials data for consumer electronic products. Scientific
Data. https://doi.org/10.1038/s41597-020-0573-9Bundgaard,A.M.,Mosgaard,M.A., &Remmen,A. (2017). Fromenergyefficiency towards resourceefficiencywithin theEcodesignDirective. Journal of Cleaner
Production, 144, 358–374. https://doi.org/10.1016/j.jclepro.2016.12.144Chancerel, P., & Rotter, S. (2009). Recycling-oriented characterization of small waste electrical and electronic equipment.Waste Management, 29(8), 2336–
Clemm, C., Nissen, N. F., Schischke, K., Dimitrova, G., Marwede, M., & Lang, K.-D. (2019). Implications of the circular economy for electronic products. In
A. Hu, M. Matsumoto, T. Kuo, & S. Smith (Eds.), Technologies and eco-innovation towards sustainability I (pp. 91–104). Singapore: Springer. Retrieved from
https://doi.org/10.1007/978-981-13-1181-9_8
Cleveland, C. J., & Ruth,M. (1998). Indicators of dematerialization and thematerials intensity of use. Journal of Industrial Ecology, 2(3), 15–50.Coroama, V. C., Hilty, L. M., & Birtel, M. (2012). Effects of Internet-based multiple-site conferences on greenhouse gas emissions. Telematics and Informatics,
29(4), 362–374. https://doi.org/10.1016/j.tele.2011.11.006Coroama, V. C., Moberg, Å., & Hilty, L. M. (2015). Dematerialization through electronic media? Advances in Intelligent Systems and Computing, 310, 405–421.
https://doi.org/10.1007/978-3-319-09228-7_24
Deng, L., Babbitt, C. W., &Williams, E. D. (2011). Economic-balance hybrid LCA extended with uncertainty analysis: Case study of a laptop computer. Journalof Cleaner Production, 19(11), 1198–1206.
Denning, P. J., & Lewis, T. G. (2016). Exponential laws of computing growth. Communications of the ACM, 60(1), 54–65. https://doi.org/10.1145/2976758Fu, X., Beatty, D. N., Gaustad, G. G., Ceder, G., Roth, R., Kirchain, R. E., . . . Olivetti, E. A. (2020). Perspectives on cobalt supply through 2030 in the face of
changing demand. Environmental Science & Technology, 54(5), 2985–2993. https://doi.org/10.1021/acs.est.9b04975Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance.MIS Quarterly: Management Information Systems, 19(2), 213–233.
https://doi.org/10.2307/249689
Hertwich, E.G., Ali, S., Ciacci, L., Fishman, T.,Heeren,N.,Masanet, E., . . . Wolfram,P. (2019).Material efficiency strategies to reducing greenhousegas emissions
associatedwith buildings, vehicles, and electronics—A review. Environmental Research Letters, 14(4), 043004. https://doi.org/10.1088/1748-9326/ab0fe3Hogg, N., & Jackson, T. (2009). Digital media and dematerialization. Journal of Industrial Ecology, 13(1), 127–146. https://doi.org/10.1111/j.1530-9290.2008.
00079.x
Hsiao, K. L. (2017). What drives smartwatch adoption intention? Comparing Apple and non-Apple watches. Library Hi Tech, 35(1), 186–206. https://doi.org/10.1108/LHT-09-2016-0105
Im, S., Bayus, B. L., &Mason,C.H. (2003). An empirical studyof innate consumer innovativeness, personal characteristics, andnew-product adoptionbehavior.
Journal of the Academy of Marketing Science, 31(1), 61–73. https://doi.org/10.1177/0092070302238602Intlekofer, K., Bras, B., & Ferguson,M. (2010). Energy implications of product leasing. Environmental Science & Technology, 44(12), 4409–4415. https://doi.org/
10.1021/es9036836
Kasulaitis, B. V., Babbitt, C. W., Kahhat, R., Williams, E., & Ryen, E. G. (2015). Evolving materials, attributes, and functionality in consumer electronics: Case
study of laptop computers. Resources, Conservation and Recycling, 100, 1–10.Kasulaitis, B. V., Babbitt, C. W., & Krock, A. K. (2019). Dematerialization and the circular economy: Comparing strategies to reduce material impacts of the
consumer electronic product ecosystem. Journal of Industrial Ecology, 23(1), 119–132.Kasulaitis, B., Babbitt, C. W., & Tyler, C. (2020). Electronic product adoption: Consumer survey questionnaire, data, and interpreted results. Retrieved from https:
//doi.org/10.6084/m9.figshare.12444044
Kim, J., & Rohmer, S. (2012). Electronic billing vs. paper billing: Dematerialization, energy consumption and environmental impacts. Electronics Goes Green2012+, 1–4. https://ieeexplore.ieee.org/abstract/document/6360455
Kitou, E., & Horvath, A. (2003). Energy-related emissions from telework. Environmental Science & Technology, 37(16), 3467–3475. https://doi.org/10.1021/es025849p
Koomey, J. G., Matthews, H. S., & Williams, E. (2013). Smart everything: Will intelligent systems reduce resource use? Annual Review of Environment andResources, 38(1), 311–343. https://doi.org/10.1146/annurev-environ-021512-110549
Leung, L., &Wei, R. (1999).Who are themobile phone have-nots?NewMedia & Society, 1(2), 209–226. https://doi.org/10.1177/1461444899001002003Li, S.-C. S. (2014). Adoption of three new types of computers in Taiwan: Tablet PCs, netbooks, and smart phones. Computers in Human Behavior, 35, 243–251.Makov, T., & Vivanco, D. F. (2018). Does the circular economy grow the pie? The case of rebound effects from smartphone reuse. Frontiers in Energy Research,
6(39), 1–11. https://doi.org/10.3389/fenrg.2018.00039Malmodin, J., & Lundén, D. (2018). The energy and carbon footprint of the global ICT and E&Msectors 2010–2015. Sustainability, 10(9), 3027. https://doi.org/
10.3390/su10093027
Mancini, L., Sala, S., Recchioni, M., Benini, L., Goralczyk, M., & Pennington, D. (2015). Potential of life cycle assessment for supporting the management of
critical rawmaterials. International Journal of Life Cycle Assessment, 20(1), 100–116. https://doi.org/10.1007/s11367-014-0808-0Mashhadi, A. R, Vedantam, A., & Behdad, S. (2019). Investigation of consumer’s acceptance of product-service-systems: A case study of cell phone leasing.
Resources, Conservation and Recycling, 143, 36–44. https://doi.org/10.1016/j.resconrec.2018.12.006Masnick, G. (2017).Defining the generations redux. Retrieved from https://www.jchs.harvard.edu/blog/defining-the-generations-redux/
Moberg, Å., Borggren, C., & Finnveden, G. (2011). Books from an environmental perspective—Part 2: E-books as an alternative to paper books. InternationalJournal of Life Cycle Assessment, 16(3), 238–246. https://doi.org/10.1007/s11367-011-0255-0
Morrell, R. W., Mayhorn, C. B., & Bennett, J. (2000). A survey of world wide web use in middle-aged and older adults. Human Factors, 42(2), 175–182. https://doi.org/10.1518/001872000779656444
O’Connell, M. (2013, April). Tablet sales skyrocketing as the PC approaches extinction. PC World, https://www.pcworld.com/article/2033167/tablet-sales-
skyrocketing-as-the-pcapproaches-extinction.html.
Pérez-Belis, V., Bakker, C., Juan, P., & Bovea, M. D. (2017). Environmental performance of alternative end-of-life scenarios for electrical and electronic equip-
ment: A case study for vacuum cleaners. Journal of Cleaner Production, 159, 158–170. https://doi.org/10.1016/j.jclepro.2017.05.032
Quariguasi-Frota-Neto, J., & Bloemhof, J. (2012). An analysis of the eco-efficiency of remanufactured personal computers andmobile phones. Production andOperations Management, 21(1), 101–114. https://doi.org/10.1111/j.1937-5956.2011.01234.x
Richa, K., Babbitt, C.W., Gaustad, G., &Wang, X. (2014). A future perspective on lithium-ion batterywaste flows fromelectric vehicles.Resources, Conservationand Recycling, 83, 63–76.
Ryen, E. G., Babbitt, C. W., Tyler, A. C., & Babbitt, G. A. (2014). Community ecology perspectives on the structural and functional evolution of consumer elec-
tronics. Journal of Industrial Ecology, 18(5), 708–721.Ryen, E. G., Babbitt, C. W., & Williams, E. (2015). Consumption-weighted life cycle assessment of a consumer electronic product community. Environmental
Science & Technology, 49(4), 2549–2559.Ryen, E. G., Gaustad, G., Babbitt, C.W., & Babbitt, G. (2018). Ecological foragingmodels as inspiration for optimized recycling systems in the circular economy.
Resources, Conservation and Recycling, 135, 48–57.Sabbaghi, M., & Behdad, S. (2018). Consumer decisions to repair mobile phones and manufacturer pricing policies: The concept of value leakage. Resources,
Conservation and Recycling, 133, 101–111. https://doi.org/10.1016/j.resconrec.2018.01.015Saphores, J. D.M., Nixon, H., Ogunseitan, O. A., & Shapiro, A. A. (2009). Howmuch e-waste is there inUS basements and attics? Results from a national survey.
Journal of Environmental Management, 90(11), 3322–3331. https://doi.org/10.1016/j.jenvman.2009.05.008
Scott, K., Roelich, K., Owen, A., & Barrett, J. (2018). Extending European energy efficiency standards to includematerial use: An analysis. Climate Policy, 18(5),627–641. https://doi.org/10.1080/14693062.2017.1333949
Shu, L. (2016). How digital photography reinvented itself to become better. Retrieved from https://www.digitaltrends.com/features/dt10-how-digital-
photography-reinvented-itself/
Solon, G., Haider, S. J., &Wooldridge, J. M. (2015). What are we weighting for? Journal of Human Resources, 50(2), 301–316. https://doi.org/10.3368/jhr.50.2.301
Tecchio, P., Ardente, F., Marwede, M., Clemm, C., Dimitrova, G., & Mathieux, F. (2018). Ecodesign of personal computers: An analysis of the potentials of
material efficiency options. Procedia CIRP, 69, 716–721. https://doi.org/10.1016/j.procir.2017.11.051van der Voet, E., Oers, L., & Nikolic, I. (2004). Dematerialization: Not just a matter of weight. Journal of Industrial Ecology, 8(4), 121–137.Vietri, J. T., Chapman, G. B., & Schwartz, J. (2009). Actor-observer differences in frequency-of-use estimates: Sometimes strangers know us better than our-
selves. Social Influence, 4(4), 298–311. https://doi.org/10.1080/15534510902857862VonWeizsäcker, E. U. (1998). Dematerialisation. In P. Vellinga, F. Berkhout, & J. Gupta (Eds.),Managing a Material World. Environment & Policy, 13, Dordrecht:
Springer.
Wagner, F., Peeters, J. R., De Keyzer, J., Janssens, K., Duflou, J. R., & Dewulf,W. (2019). Towards amore circular economy forWEEE plastics—Part A: Develop-
ment of innovative recycling strategies.WasteManagement, 100, 269–277. https://doi.org/10.1016/j.wasman.2019.09.026
Wang, Y.-X., Kuo, C.-H., Song, R., Hu, A., & Zhang, S.-S. (2017). Potentials for improvement of resource efficiency in printed circuit boardmanufacturing: A case
study based onmaterial flow cost accounting. Sustainability, 9(6), 907. https://doi.org/10.3390/su9060907Weber, C. L., Koomey, J. G., & Matthews, H. S. (2010). The energy and climate change implications of different music delivery methods. Journal of Industrial
Ecology, 14(5), 754–769.Wernick, I., Herman, R., Govind, S., & Ausubel, J. (1996). Materialization and dematerialization:Measures and trends.Daedalus, 125(3), 171–198.Xia, F., Yang, L. T., Wang, L., & Vinel, A. (2012). Internet of things. International Journal of Communication Systems, 25(9), 1101–1102. https://doi.org/10.1002/
dac.2417
Zhou, B., Li, W., Chan, K. W., Cao, Y., Kuang, Y., Liu, X., &Wang, X. (2016). Smart home energy management systems: Concept, configurations, and scheduling
strategies. Renewable and Sustainable Energy Reviews, 61, 30–40. https://doi.org/10.1016/j.rser.2016.03.047
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
