The Adoption of Energy Efficient Light Bulbs and Information Diffusion of Multidimensional
Product Characteristics: A Text-Mining Analysis Using Amazon Product Webpages
Yu-li Ko1 & Kenneth L. Simons2
Date of This Version:
5/23/2016
WORKING PAPER: Please do not cite or circulate without permission.
* This work was supported in part by the Engineering Research Centers Program of the National Science
Foundation under NSF Cooperative Agreement No. EEC-0812056 and partially by New York State under NYSTAR
contract C090145. Any Opinions, findings and conclusions or recommendations expressed in this material are those
of the author(s) and do not necessarily reflect those of the National Science Foundation or New York State.
1
2
Rensselaer Polytechnic Institute, [email protected]
Rensselaer Polytechnic Institute, [email protected]
The Adoption of Energy Efficient Light Bulbs and Information Diffusion of
Multidimensional Product Characteristics: A Text-Mining Analysis Using Amazon Product
Webpages
1. Introduction
Currently, electric lighting consumes almost 20% of global electricity production (Waide
& Tanishima, 2011). Fast adoption of technologies and further innovations can effectively
reduce energy consumption in the lighting sector. In this context, policies have supported two
historically important changes in the electric lighting industry: phase-out of inefficient
incandescent lamps and the adoption of efficient light-emitting diode (LED) lamps. However,
energy efficient products tend to diffuse slowly despite their cost-saving benefits (Jaffe &
Stavins, 1994a; Jaffe & Stavins, 1994b; Sorrell, 2004), and the previous experience with
compact fluorescent lamps is a well-known example of slow adoption of energy efficient
products. Multiple explanations for the “energy paradox” have been suggested, such as risk,
hidden costs, difficulties in financing, split incentives between renters and landlords, bounded
rationality, and imperfect information (Sorrell, 2004). While all these reasons would play a role,
this study focuses on imperfect information, specifically, consumer communication regarding
unobservable product characteristics. If consumers communicate less about unobservable
product characteristics, information on improved efficiency may not diffuse as fast as observable
characteristics, resulting in a suboptimal adoption rate. To understand the problem, 103,003
product reviews web-scraped from Amazon.com were classified into product characteristic
categories, including efficiency, color, brightness and the price of the bulb.
This study is evolved from a Lancasterian perspective of multidimensional product
characteristics (Lancaster, 1966), combining insights from studies on search goods, experience
goods, and credence goods (Nelson, 1970, 1981; Darby & Karni, 1973). The classification of
search goods, experience goods, and credence goods depends on how easy it is to gather
information for consumers (Nelson, 1970; Darby & Karni, 1973). A search good is a product for
which consumers can assess its value before purchase. For an experience good, consumers can
assess its value after purchase. For a credence good, it is hard for consumers to assess its value
because assessment requires specialized knowledge and/or high cost. Later literature tends to
emphasize “search attributes”, “experience attributes,” and “credence attributes” (Nelson, 1981),
rather
than
product-level
classifications
embracing
a
Lancasterian
perspective
of
multidimensional product characteristics. This study applies such attribute categories to
understand technology adoption better, especially energy efficiency improvements. Unbalanced
information diffusion, originated from the cost of evaluation, may adversely impact the adoption
of technological change in credence-good attributes, like energy efficiency.
To empirically assess this claim, a text-mining method is applied to 103,003 consumer
reviews collected from Amazon product webpages in the light bulb category between September
2010 and June 2014. The comments were first parsed into words. The 2,000 most frequent words
are detected by text-mining, and manually classified into categories of most discussed product
characteristics including efficiency, longevity, price, brightness, color, and heat emission. By
using frequent words, the analysis focuses on major attributes. These product characteristics are
further classified into search attributes, experience attributes, and credence attributes. Efficiency
and longevity are classified as credence attributes, price is classified as a search attribute, and
brightness, color and heat emission are classified as experience attributes. Then, total counts of
comments for each characteristic/attribute are calculated by month. Using these data, consumers’
communication for each characteristic/attribute is quantified.
Prior studies analyzed product attribute categories using Amazon reviews. Mudambi and
Schuff (2010) studied whether product attribute categories affect the subjective helpfulness of
the review. They picked a music CD, an MP3 player, and a video game to represent experience
goods, and a digital camera, a cell phone, and a laser printer to represent search goods. This
classification fits the dominant attributes of products studied in prior literature. Their statistical
results suggest that extreme reviews are less helpful for experience goods compared to search
goods. Hong, Chen, and Witt (2012) developed theoretical models to classify products into
experience goods and search goods using consumer reviews. They applied the theory using
Amazon reviews based on the theoretical models. Siering and Muntermann (2013) analyzed
review contents to understand how reviews discussing credence goods differ from reviews
discussing search or experience goods, and found differences in product evaluations, quality
assessment, and expressed feelings of need. While the prior studies reveal how product attribute
categories affect consumer reviews, this study focuses on consumer communication about
product attributes within a product category.
Among 103,003 comments, 55% are about LED bulbs, 20% are about CFL bulbs, 12%
are about halogen bulbs, and 13% are about incandescent bulbs. According to the U.S. Energy
Information Administration (2014), the market share of LED bulbs in 2013 was 2.3%, and that of
CFL bulbs was 15%-20%. A clear pattern is that consumers’ tendency to talk was greater for
LED light bulbs, which are new products. In all, 26.6% of comments discussed price. Since price
is shared information across all consumers, the marginal utility of this information to other
consumers is nearly zero, and the marginal cost of observation is also nearly zero. No risk of
fraud or ambiguity is associated with this information. Therefore, this can be a benchmark value
to understand the fraction of other product characteristics. For the three experience attributes,
32.4% of comments discussed brightness, 26.8% discussed color, and 6.0% discussed heat. For
the two credence attributes, 9.8% discussed efficiency and 2.6% discussed longevity. The 9.8%
frequency of efficiency comments is not much higher than the 6.0% frequency of heat-related
comments. Considering that heat is not a main characteristic of a light bulb, 9.8% is surprisingly
low. In sum, 26.6% of comments were classified as search-attribute related, 47.3% of comments
were classified as experience-attribute related, and only 12.0% of comments were classified as
credence-attribute related. The results suggest that consumer communication is biased towards
experience attributes such as brightness and color, rather than credence attributes such as
efficiency. Results are mostly similar across technologies and over time.
The results support the need for command-and-control policies like phase-out of
incandescent bulbs, active public advertisement, and education. Policies must aid the diffusion of
information on credence attributes such as energy efficiency. Information on technological
change in efficiency may not diffuse as fast as observable characteristics, and this can create two
disadvantages for incentivizing further improvement in energy efficiency. First, products with
superior efficiency may experience a smaller market than is socially desirable, disincentivizing
technological change in efficiency improvement. Second, social reputations for technological
change in efficiency improvement may be insufficient, psychologically discouraging innovators.
This bias found in consumer communication on light bulbs has broader and general implications
on technology policies, suggesting the need to promote technological change in unobservable
characteristics more intensively.
In section 2, light bulb technologies and some history are briefly introduced. In section 3,
research topics are explained. In section 4, the data and method are explained. In section 5,
results are summarized. The last section provides a summary and discussion.
2. Light Bulb Technologies
Currently, the global light bulb market is served mainly by four technologies:
incandescent lamps, halogen lamps, compact fluorescent lamps (CFLs), and LED lamps. These
lamp types differ on a key dimension of technological change, energy efficiency. For electric
lighting, energy efficiency means the degree to which electrical energy input is converted to light
output. Light output per unit of electrical energy is commonly measured in units of lumens per
watt. Energy efficiency of the four major lighting technologies is reported in Table 1. For climate
change abatement, improvement in energy efficiency has become important.
Most incandescent bulbs convert less than 5% of electrical energy to light, and the other
95% is mostly wasted as heat. As seen in Table 1, energy efficiency of 60-watt incandescent
light bulbs is only 16 lumens per watt. In addition, bulb life is very short. Because of their low
efficiency, governments have been mandating a phase-out of incandescent lamps. Table 2
summarizes phase-out policies of major industrialized regions.
The efficiency of halogen bulbs is not much better than that of incandescent bulbs, but
life is significantly longer. Halogen lamps are incandescent lamps with small amounts of halogen
elements, such as iodine or bromine, added. The halogen elements help the incandescent filament
last longer.
CFLs were introduced in the 1970s. The efficiency of 60-watt CFLs is 67 lumens per
watt, which is significantly better than for incandescent or halogen lamps. Bulb life is also longer.
The adoption rate of CFLs has varied greatly by country. In the U.S., adoption has been
especially low despite supportive government policy.
LED lamp technology is the most recent technology. The efficiency and longevity of
LED lamps exceeds previous technologies such as incandescent lighting, CFLs, and halogen
lamps, and there is still good potential for efficiency improvement. However, up-front cost has
been relatively high. It is expected that technological change can be fast, further reducing
production cost and improving efficiency. Despite the policy objective, adoption has been slow.
As of 2013, the market share of LED bulbs in 2013 was only 2.3%.
3. Research Topics
The cost of electric lighting using existing fixtures consists of two components. The first
component is the price of the light bulb distributed and discounted over the life of a light bulb.
The second component is electricity cost, which is reduced by improvements in energy
efficiency. With improvements in efficiency and longevity, energy efficient light bulbs reduced
the cost of electric lighting, even taking account of the price of the light bulb (Sanderson &
Simons, 2014). However, energy efficient light bulbs have experienced slow diffusion. In
general, energy efficient products are known to diffuse slowly despite their cost saving features.
An old debate in energy economics has addressed this “energy efficiency gap” or “energy
paradox.” It has been argued that consumers are unlikely to adopt energy efficient technology
quickly even if it is beneficial to them. For example, an empirical study concludes that
consumers appear to be “indifferent between one dollar in discounted future gas costs and only
76 cents in vehicle purchase price” (Allcott & Wozny, 2012, p. 31). Multiple explanations have
been suggested for the energy paradox, such as risk, hidden costs, difficulties in financing, split
incentives between renters and landlords, bounded rationality, and imperfect information (Sorrell,
2004).
This paper researches biased diffusion of information on product characteristics, which
can be unfavorable to efficiency. When a product has multidimensional characteristics, and cost
of evaluation differs by each attribute, information diffusion may not be balanced across all
product characteristics. Such unbalanced information diffusion can adversely impact adoption of
technological changes in credence-good attributes, in this case energy efficiency. This implies
that information on technological change in efficiency may not diffuse as fast as observable
characteristics, resulting in a suboptimal adoption rate of energy-efficient products. This can
hinder a critical climate change abatement method.
A product could have multiple characteristics (Lancaster, 1966). For example, in the case
of electric lighting, brightness and color are dominant product characteristics. If consumers talk
less about some product characteristics, two main reasons may exist. On one hand, each
characteristic’s contribution to utility can differ across products, and the difference may provoke
consumer communication. A 60-watt-equivalent replacement LED light bulb may be brighter or
more bluish compared to a particular previously-experienced 60-watt incandescent light bulb,
and a consumer may or may not like the brightness or color difference. Actual variance in
characteristics and consumers’ utility from each characteristic can affect what characteristics
consumers talk about.
On the other hand, consumers may talk less about some characteristics due to the cost of
observation, and this part is the main research topic of this paper. Not all product characteristics
are equally easy to evaluate. Products can be classified as search goods, experience goods, and
credence goods depending on how easy it is to gather information about them for consumers
(Nelson, 1970; Darby & Karni, 1973). A search good is a product for which consumers can
figure out its value before purchase. An experience good is a product for which consumers can
figure out its value after purchase. A credence good is a product for which it is hard for
consumers to figure out its value, because evaluation requires specialized knowledge and/or high
cost. A later literature tends to emphasize “search attributes,” “experience attributes,” and
“credence attributes” (Nelson, 1981). Brightness and color are experience attributes while
efficiency is a credence attribute. The study investigates whether consumers communicate less
about credence good characteristics like efficiency.
4. Data and Method
4.1 Data Collection
To understand biases in consumer communication, online consumer reviews can be a
good source of information. Studies using online reviews have become popular in academia. For
e-commerce, consumer comments are an essential part of consumer communication.
Product webpages were collected from the U.S. Amazon internet retailer, well known by
its internet address of amazon.com. Amazon consumer reviews are representative and relatively
reliable. Amazon is the world’s largest online retailer, with net sales of $61.09 billion in 2012.
Alexa Internet, Inc. ranks Amazon as having the world’s sixth-highest internet traffic rank as of
April 3, 2016. Amazon consumer ratings show good correlation with expert ratings (Mudambi &
Schuff, 2010).
The collected products belong to the light bulb category. For this study, subcategories are
restricted to incandescent bulbs, halogen bulbs, CFL bulbs (excluding fluorescent tubes), and
LED bulbs, because these are the most common residential-use bulbs. The text was web-scraped
during July 8, 2013 to early July 2014, and is an almost complete collection of consumer reviews.
This dataset has many strong points. It is an almost complete collection of reviews in the
period, avoiding sample selection issues. Use of textual instead of spoken words facilitates
processing by computer. With the growth of e-commerce, understanding online communication
is important by itself. One advantage over interviews is that there is no personal interaction
between interviewees and interviewers.
Use of Amazon reviews may yield differences compared to the general US or world
populations. Review writers are all web users, and mainly US consumers. The writers’ age
distribution may be biased somewhat toward a younger generation. In the U.S., CFL penetration
was largely unsuccessful, signaling that U.S. consumers may be less sensitive to energy
efficiency than consumers worldwide. Consumers who experience a large gap between
expectation and reality may be disproportionately likely to leave comments, and ratings are
likely to follow a J-curve distribution (Hu, Pavlou, & Zhang, 2009). One- or two-star customer
ratings would generally signal a low level of consumer surplus while four- or five-star ratings
would signal a high level of consumer surplus. Consumers are likely to say nothing about
product characteristics if they experience moderate satisfaction. In this period, incandescent
lightbulbs were phased-out in the U.S., with production of 100-watt bulbs ceasing in 2012, then
75-watt bulbs ceasing production, then 60- and 40-watt bulbs ceasing production in 2014.
Producers could still sell incandescent bulbs. While the phase-out was anticipated by producers
and many consumers, the majority of incandescent light bulb consumers may not have known its
details. This event, and other U.S. government policies, may have provoked awareness of
efficiency, so it is possible that this increased comments using efficiency-related words. Since
Amazon’s page views greatly increased in this period, newer time periods and products are also
represented more than older periods and products.
4.2 Data Preparation Method
4.2.1 Data Clean-Up
Originally 269,462 comments in the light bulb category were collected, including revised
versions of the same review on the same product by the same reviewer. The original dataset went
through clean-up processes. As a result of clean-up, 103,003 comments were left.
First, unusual lighting products were excluded from the dataset, to focus on normal white
light bulbs. Color-changing bulbs, LED strip lights, and lanterns were excluded. Amazon’s
categorization of products is not perfect, so exclusions to focus on normal white light bulbs may
be imperfect.
Second, only comments written between September 2010 and June 2014 are used. In the
early part of the original dataset, few reviews were available. Amazon’s growth is one reason for
an increased number of reviews over time, as is accumulated consumer experience writing
reviews so that consumers begin to leave reviews with increased frequency. If an Amazon seller
inactivates a product, the associated product web page is no longer searchable. Without
truncation, old reviews would over-represent products that survived. Amazon employees were
consulted regarding this issue, but no information was available from them. From September
2010, the number of comments per month for each product category exceeded 30, which was
chosen as a cutoff for sample inclusion. Data for early July 2014, at the end of the sample, were
excluded so that when data are reported on a monthly basis, analyses include (nearly) all reviews
during each month.
4.2.2 Keyword Classification
Text mining is a computerized method that processes natural language text to find
patterns, and recent research in economics makes use of the technique. In this paper, a comment
is classified into a subject category if the comment contains keywords associated with the
category. To classify comments, a list of frequent words was constructed by text-mining, and the
2000 most frequent words were manually classified, where relevant, into product characteristics
categories. Words not associated with a product characteristic were ignored. The classification is
not mutually exclusive. For example, if a comment contains “white” and “efficiency,” the
comment is classified into the category of color and also into the category of efficiency. Since
the classification depends on the pool of keywords, selecting keywords is a central problem of
data preparation for this study.
The critical point of this classification is to minimize the possibility of incorrect
classification of a comment. A sufficient number of keywords should be used, and ambiguity
should be investigated and reduced.
To avoid biases in selecting keywords, a list of most frequent words in the reviews is
generated first. Among 42,215 words, the most frequent 2,000 words were examined manually.
Keywords from the list were identified that consumers use to describe product characteristics.
These keywords were categorized into product characteristics including color, brightness, heat,
efficiency, longevity, and price. The classification did not start with preset categories to avoid
bias. Since the classification is based on the most frequent keywords, these are product
characteristics that consumers talk about often. It would be impractical to go through all 42,215
words, and largely meaningless to do so. Examination halted at the 2,000th most frequent word,
because meaningful keywords were rarely found near the 2,000th word.
Note that the
distribution of words usually follows a power-law distribution, and the frequency of the most
frequent words is overwhelmingly high compared with the frequency of less frequent words. For
example, the 2,000 most frequent words are 4.7% of the single words that ever appear, but
represent 87.4% of the words in the text. Therefore, neglecting low frequency words is less
problematic.
Naturally, rare terms are not included, and thus truncation of the words used may differ
across product characteristics. To illustrate the problem, consider a total set of words that a
person can mention to talk about a certain topic. For example, for color, a total set of color-
related terms can cover the spectrum of visible light, and these terms can capture comments that
talk about color very well, as the set of words is complete and dense. At the same time, some
color-related terms are rarer than other terms. Violet would not be as frequent as red. Since 2,000
frequent words are used, rarer color-related terms are not captured. If few words are associated
with a product characteristic, the associated words may be more likely to appear within the list of
2,000 most frequent words. This would be a problem if the words that people use to describe a
product characteristic are overall very rare and differ across people, causing the words not to
make it to the top 2,000 list. However, since the 2,000 most frequent words cover 87.4% of the
words in the text, the problem is likely to be negligible.
In case there is no ambiguity, a word is easy to classify. For example, “white” can be
classified in the category of color without ambiguity. Table 3 is the list of non-ambiguous words
and their classifications. This non-ambiguity takes account of the context of light bulb reviews.
When “blue” is detected, it will be interpreted as blue color. It is possible that it means blue
emotion, but within the context of light bulb reviews, it is judged that ambiguity is insignificant.
Ambiguity of a word can be a great source of biases, so is treated carefully. An
assumption in the frequency analysis is that the distribution of signs, namely words and
collocations, reflects the distribution of meanings. Text analysis favors rare and technical terms
that assign one sign to one meaning. In case of frequent and non-technical terms that assign one
sign to multiple meanings, text analysis may be biased. Since this analysis is about product
characteristics, it is likely that a term is assigned to one meaning. However, there are cases that
require caution. For example, when “cool” is detected, it will be interpreted as cool color, but it
is possible that it means cool temperature or something nice.
If at least one keyword appears in the text, the text is classified under the associated
characteristic. Therefore, if a consumer uses multiple keywords in her review, classification is
likely to become more accurate. For example, if a consumer writes that “the bulb’s color is
white,” the review is categorized as related to color for two reasons, both “color” and “white.”
The availability of multiple keywords for a characteristic thus reduces ambiguity and chance
fluctuations in classification.
There are some resolutions for the remaining ambiguity, and each has its own problems.
First, ambiguous terms can be neglected. Second, one ambiguous term can be assigned to one
characteristic category according to a manually analyzed distribution. Third, ambiguous terms
can be assigned to multiple characteristics categories according to a manually analyzed
distribution. For example, suppose that 4 out of 10 appearances of ‘cool’ are temperature cool,
and 6 out of 10 appearances of ‘cool’ are color cool (meaning a bluish white light). Then, the
first way neglects cool due to ambiguity, the second way assigns all the comments that include
‘cool’ to color cool, and the third way assigns 60% of all the comments that include cool to color
cool and 40% to temperature cool. In this paper, the second way is used, and to complement the
method, a word strongly associated with the word is used if possible. For each ambiguous word,
twenty comments that include the word were randomly retrieved and examined to determine
classification rules. A list of ambiguous words and their treatment methods is found in Table 4.
The first way is sometimes used when meanings did not generally pertain to any of the product
characteristics analyzed. Two-word phrases that more specifically capture a meaning were also
sometimes used; for example, when ‘long’ is neglected for ambiguity, “last long” is included to
capture longevity-related comments.
Technical words that describe product characteristics are included in the list of keywords,
because it appears that customers do use technical words to talk about product characteristics.
For example, lumen and output are used to describe brightness. The words 2700k, 3000k, 6000k,
and kelvin are used to describe color temperature, so these are classified as color-related
keywords.
As seen in Table 5, it may look like the number of keywords for each category unfairly
differs, but this is not a problem. For example, there are 17 color-related keywords while there
are only 5 brightness-related keywords. This reflects two underlying patterns in the dataset.
Color-related keywords are indeed frequent, and color related terms have a finer spectrum. In
comparison, brightness-related terms are simpler using only variants of bright and dark. Thus,
there is an inherent difference in the number of terms in English language. However, the
numbers of comments classified with these keywords are aggregated by product characteristics,
so the number of keywords for each category is unlikely to cause any bias. In fact, the analysis
results indicate that the number of comments for brightness is generally higher than the number
of comments for color despite the smaller number of color-related keywords.
The product characteristics are further classified according to whether they are search,
experience, or credence characteristics. Color and brightness are experience attributes, because
they are observed after purchase. Heat is also an experience attribute. The price of the bulb is a
search attribute. Efficiency and longevity are credence attributes. The borderlines between these
categories are not necessarily sharp. Longevity is technically observable. However, measurement
of longevity is costly, in terms of time and record-keeping, and thus it is classified as a credence
attribute.
4.3 Method
Simple statistical methods are used. The fraction of reviews discussing each product
characteristic and each attribute (search/experience/credence) is calculated and compared. From
the dataset, the frequency with which reviews mention each characteristic is calculated, as the
number of reviews that match a certain product characteristic category, divided by the total
number of reviews. If these rates are overall higher for experience attributes, this would support
the claim that information on experience attributes is easier to diffuse. Since the dataset is an
almost complete collection of the consumer reviews in the light bulb category on Amazon,
simple statistics directly represent the population. Additionally, to check the robustness of the
results, comments on ‘Energy Star’ products, differences across technologies, and temporal
trends are studied.
First, differences across product characteristic and search/experience/credence attributes
by bulb technologies are examined. LED bulb reviewers may be more sensitive to credenceattributes such as efficiency and longevity.
Second, the monthly dynamics of the fraction of reviews discussing product
characteristics are studied to see if there are temporal trends. From the dataset, the frequency
with which reviews mention each characteristic is calculated on a monthly basis, as the number
of reviews that match a certain product characteristic category, divided by the total number of
reviews that month. Since this period went through technological change, including phase-out of
incandescent bulbs and commercial development of LED bulbs, an examination of temporal
trends allows consideration of potential impacts of these shifts. New products, especially LED
products, were introduced over time, and might have more comments. Over the time period
analyzed, LED bulbs’ market share increased. The industry experienced severe price reductions,
and LED bulbs benefited from technological change, yielding cheaper, better looking, brighter,
more-reliable bulbs with better dimming and other features.
Third, the fraction of reviews discussing each product characteristic and each attribute
(search/experience/credence) is calculated and compared with the dataset restricted to products
that explicitly include ‘Energy Star’ in the product description. Energy Star is a U.S.
Environmental Protection Agency program that certifies energy efficient products. For the
Energy Star analyses, 31,958 comments (31% of the total comments) are analyzed. This analysis
is done to check whether consumers’ tendency to talk about search or experience attributes is
affected by information on efficiency from the product descriptions.
5. Results
First note that the majority of comments was about LED bulbs even though the market
share of LED bulbs was low, as shown in Figure 1. Note that 55% of the total comments are
about LED bulbs, 20% are about CFL bulbs, 12% are about halogen bulbs, and 13% are about
incandescent bulbs. It appears that consumers talked more about the new technology than the
incumbent technology. The number of LED bulb products for sale became substantially higher
than for the incumbent bulb types, so the mean number of comments per product was not
necessarily higher.
Table 5 Column (1) summarizes the aggregated results. Among 103,003 comments, 26.6%
of comments discussed price, which is a search-attribute. Since price is shared information
across all consumers, the marginal utility of this information to other consumers is nearly zero,
and the marginal cost of observation is also nearly zero. No risk of fraud or ambiguity is
associated with this information. Therefore, this can be a benchmark value to understand the
normalized frequency of other attributes. For the three experience attributes, 32.4% of comments
discussed brightness, 26.8% discussed color, and 6.0% discussed heat. For the two credence
attributes, 9.8% discussed efficiency, and 2.6% discussed longevity. In sum, 26.6% of comments
were classified as search-attribute related, 47.3% of comments were classified as experienceattribute related, and only 12.0% of comments were classified as credence-attribute related. For
bulbs, consumers were highly likely to talk about experience attributes, and fairly likely to talk
about search attributes. They were least likely to talk about credence attributes.
Table 5 Columns (2)-(5) summarize the results of the analysis by bulb technology. While
differences are observed, the fraction of experience attributes is clearly higher than the fraction
of credence attributes across all bulb technologies.
Figure 2 depicts the monthly trend of reviews that are related to bulb price, which is a
search-attribute. The fraction for price mostly stays between 25% to 35%, and a falling trend is
observed. Thus, 25-35% of customer reviews discuss price. Figure 3 gives the same information
for different bulb types. The decrease in discussion of price occurred across all bulb types, most
notably for incandescent and LED bulbs. Discussion of price was greatest for halogen bulbs,
which are relatively high-priced among incumbent bulb types, and for LED bulbs, whose price
started out high but has declined rapidly.
Figure 4 summarizes monthly trends of experience attributes, including color, brightness,
and heat. The trend shows little variance over time. Among the customer reviews, 25-35%
discuss color, 30-40% discuss brightness, and 5-10% discuss heat. The fraction for brightnessrelated keywords is similar. Keywords for heat, which is not a dominant attribute of a light bulb,
appear in around 5-10% of comments. It appears that the marginal utility change from heat is
relatively minor to ordinary consumers, since heat is not the main function of a bulb and does not
ordinarily pose problems. Color and brightness, however, are central aspects of consumer utility
from light bulbs, and the importance is likely to be reflected in the fractions along with the low
cost of observation.
Figure 5 summarizes monthly trends of credence attributes, including efficiency and
longevity. The percentages are fairly stable over time. Roughly 10-15% of reviews discussed
efficiency, and about 3% discussed longevity. Thus, it appears that consumers are less likely to
write about efficiency or longevity. This confirms the hypothesis that credence attributes are less
like to be discussed or communicated among consumers.
Figure 6 shows the monthly trend for efficiency, by bulb technology. LED and CFL
reviews were most likely to include efficiency-related keywords, with about 7-15% of CFL
reviews discussing efficiency, and roughly 7-17% of LED reviews discussing efficiency. The
discussion of efficiency for CFL bulbs trended downward over time. For incandescent and
halogen bulbs, efficiency was discussed in about 7-10% of reviews, albeit with considerable
fluctuation over time partly due to relatively small sample sizes. It appears that reviewers of LED
and CFL products, which might be chosen for efficiency reasons, were most likely to consider
efficiency in assessments of light bulb products. Nonetheless, discussion of this key attribute of
light bulbs is very infrequent.
LED light bulbs experienced substantial efficiency gains over this period, whereas
incandescent, halogen and CFL bulbs were mature technologies with little if any efficiency
improvement over the sample period. One might expect that the changes in efficiency over time
would stimulate more discussion, but any such effect regarding efficiency apparently was very
modest. Improvements in other aspects of LED bulbs were also important, including for price,
color, brightness, heat, and longevity.
6. Discussion
Only 9.8% of comments discussed efficiency. For comparison, 26.6% of comments
discussed price, and 32.4% of comments discussed brightness. In general, while 47.3% of
costumer reviews consider experience attributes, only 12.0% consider credence attributes. The
9.8% considering efficiency is only slightly higher than the fraction of heat-related comments,
which is 6.0%. Since heat is not a main characteristic of a bulb, 9.8% is surprisingly low. This
substantial difference in frequency is consistent with information on efficiency being less likely
to diffuse relative to information on color or brightness. It appears that experience attributes are
much more likely to be mentioned in review comments than credence attributes.
As efficiency-related terms are less often communicated compared to experience-related
terms, the rate of information diffusion is affected. This implication for the rate of diffusion has
consequences for adoption and innovation. If efficiency improvement is not associated with
improvements in experience attributes – and this is generally true for LED lighting – then the
products that embed efficiency innovations would not be adopted rapidly. This would disincentivize innovations in efficiency. This also suggests an incentive for biased technological
change, favoring improvements in experience attributes. Technological change in credence good
qualities may be slower.
Facing this, on the firm’s side, first, it is competitively essential to improve experience
and search good qualities along with any improvements that might be made in credence good
qualities. In the case of LEDs, the changing technology is changing in multiple characteristics,
and improvement in energy efficiency alone is not sufficient for rapid diffusion of the technology
when consumers weigh other characteristics more than unobservable energy efficiency. Superior
performance in light quality should be coupled with better efficiency. Improvement in efficiency
alone is not sufficient for fast diffusion when a product has multiple characteristics. This would
be one reason for the energy efficiency gap. When a technology is bundled, it is desirable to
invest more in improving other characteristics to some extent. For new technologies, the
potential to improve other characteristics, that are more salient to consumers, should be assessed.
Cost is not the only concern of consumers, and new technologies with improvement in one
characteristic may not satisfy the needs of consumers for other characteristics. Second, this
credence good quality can be diffused by expert consumers. Expert consumers might be trusted,
if their expertise becomes apparent in reviews, and their ratings and uses may affect future
consumers. Targeting this group may help. Consumers with technical knowledge often
voluntarily provide information including information about credence good quality. Technical
comments often are written in the early stage of product introduction. This would imply a role of
consumer experts in diffusion, especially for products with important credence good qualities.
The results support two government policies. First, to spur adoption, it may be helpful to
support technological changes in other characteristics along with efficiency, even though
efficiency improvement is the main policy target. This implies that technology policy that only
promotes energy efficiency may be likely to fail. If government subsidy is given only to improve
energy efficiency, the resulting products may be of ordinary, or even low, quality in terms of
other characteristics that are salient to consumers, causing low adoption. Thus government
agencies supporting efficiency improvement should also support improvement of other
characteristics. A gap in perceived product quality may explain why some consumers still use
incandescent lamps, in preference to CFLs and to early generations of LED bulbs. LED products
with better color, brightness, and other observable features can penetrate the market faster, by
building on improvements in experience attributes. Utility gains from characteristics other than
energy efficiency are high, and energy-efficient products do not yield enough utility to overcome
uncertainties or a lack of information about efficiency. This yields complex implications for the
phase-out of incandescent bulbs. Efforts to improve light quality may be reduced without
competitive pressure from incandescent bulbs, although competition among CFL and LED bulbs
still provide competitive pressure to improve these features. On the other hand, if consumers are
not aware of some information, phase-out may be justified.
Second, the lack of consumer attention to important non-salient features justifies the need
for command-and-control policies like phase-out of incandescent bulbs, active public
advertisement, and education. Globally, governments have pursued the policies, but policies can
be more intensive. More active top-down information diffusion can be effective. It would also be
helpful to make the credence good quality visible. Technological change to improve visibility of
credence good qualities, such as a more visible electricity meter, or internet-of-things data
collection leading to display of the electricity cost of specific devices, may be helpful. In this
way, credence good qualities like efficiency might be converted into experience or search
qualities. Energy auditing also may help to make credence information visible. Education or
advertisements may help. Incentivizing expert citizens may supply more information.
Technological literacy differs across people, and information cost differs, so methods to simplify
the information and its flow may help; for example if the term “efficiency” is too technical and
ambiguous, “dollar savings in your area” may better communicate information.
The U.S. government and other governments have actively tried to address some of these
problems of consumer information diffusion for energy-saving products including light bulbs.
The U.S. Energy Star program is a government labeling campaign that has provided trustworthy
information, for products including light bulbs, about efficiency. In addition, the U.S.
Department of Energy’s Lighting Facts program requires LED light bulb manufacturers to
provide detailed information on product characteristics including brightness, color, and
efficiency on their product labels, based on test results that follow industry standard procedures
(U.S. Department of Energy, 2012).
The program provides credence to consumers and
incentivizes firms to improve multiple characteristics. The U.S. Department of Energy has
published reports on product characteristics of energy efficient light bulbs. However, this may
not sufficiently help fast diffusion of information on credence attributes. Many academic and
government reports already suggested that more active information diffusion policy may be
necessary such as consumer education and public advertisement, and the results of this paper
supports the direction.
7. Conclusion
To understand the role of imperfect information in the energy paradox, 103,003 Amazon
review comments on light bulbs were analyzed with text-mining methods. A new method is
suggested based on search attributes, experience attributes, and credence attributes classification.
While 30% of comments include color-related keywords and a similar fraction of comments
include brightness-related keywords, only 9.8% of comments include efficiency-related
keywords. This fraction is only slightly higher than the fraction of heat-related comments, which
is 6.0%. The fraction of comments including longevity is much lower. It appears that the pattern
is consistent across different bulb types.
The evidence confirms that efficiency is not a characteristic for which information is easy
to diffuse relative to observable characteristics, especially color and brightness. If information on
efficiency is not easy to diffuse despite cost savings, the rate of adoption for energy efficient
products will be slow.
The study has general implications for technological change. It is likely that
technological change that cannot be directly observed by consumers would be slow due to slow
diffusion. This type of technological change is less incentivized. Public communication of
characteristics including credence characteristics can be important, as is further confirmed by
U.S. government efforts to diffuse this sort of information. Especially when unobservable
characteristics involve an externality, this is potentially problematic.
There are some weaknesses associated with text-mining. Text-mining can yield noisy
results despite efforts to minimize noise. In future studies, to check sensitivity, two methods can
be used. First, only helpful reviews might be used to see if there is difference for good quality
reviews. Reviewers who write good quality reviews may write more about credence-good
attributes. Second, only highly rated products might be used to see if there is a difference for
good quality products. Reviews who buy good quality products may write more about credencegood attributes. Noise may be further reduced. Analysis might be restricted to certain bulb types
such as 60W and 40W equivalents. Since the impact of phase-out differs by wattage of bulbs,
which decides variable cost per bulb, the difference in comments can be analyzed in future
studies. This might provide unique evidence on the diffusion side of the energy paradox. Cleanup might be done better to address collocated words, such as the word pair “warm up.”
More extended research could be done. Similar analysis might be done with other
products, including other energy-efficient products. Information on credence good qualities may
be more likely to be spread by expert consumers, who have better access to and knowledge about
credence information, and this issue might be studied. Combining the work with sentiment
analysis, it can be studied whether incandescent light bulb consumers leave less technical and
more emotional reviews. Cross validation might be made using social networking services like
Twitter and Facebook. The same framework might be applied to other products, including other
energy-efficient products. Firm dummies might be included to see how company dominance is
established in the new LED sector. Further studies are needed to reveal more details.
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Tables and Figures
Table 1. Efficiencies of 60-Watt Replacement Lamps by Technology
Incandescent
Halogen
CFL
LED
16
20
67
83
Efficiency
(lumens/watt)
Table 2. Phase-Out Schedule of Incandescent Bulbs by Country (In U.S. and Other Countries)
Country
Date
U.S.
Jan/1/2012
Ban on 100-watt bulbs
Jan/1/2013
Ban on 75-watt bulbs
Jan/1/2014
Ban on 40-watt and 60-watt bulbs
Europe
Contents
Sept/1/2011 Ban on inefficient 60-watt light bulbs
Sept/1/2012 Ban on manufacturing and importing incandescent bulbs of any
type
Japan
Terminating production and sales of incandescent bulbs was
recommended by the government.
China
Oct/1/2012
Ban on imports and sales of 100-watt and higher incandescent
bulbs
Oct/1/2014
Ban on 60-watt and higher incandescent bulbs
Oct/1/2016
Ban on 15-watt and higher incandescent bulbs
Table 3. Non-Ambiguous Keywords Assigned to Product Characteristics
Number
Product
Attribute
Keywords
of
Characteristics
Keywords
Search
Bulb price
discount, overpriced, price, pricey
4
Experience
Brightness
brightly, brightness, brighten, bright, output,
5
Color
amber, black, blue, blueish, bluish, color,
22
colored, colors, green, greenish, hue, kelvin,
orange, pink, red, RGB, white, yellow,
yellowish, 2700k, 3000k, 6000k
Credence
Heat emission
heat, hot
2
Efficiency
bill, efficiency, efficient, electricity, energy
5
Longevity
longevity
1
Keyword
warm,
Table 4. Ambiguous Keywords and Applied Rules
Classification
Usage in Sampled Comments and Applied Rules
color
13 comments used warm as color-related, 2 comments
used “warm” as heat-related, 6 comments used “warm”
warmth
as in “warming up”. Thus, the expression “warming up”
is cleaned from the text and “warm” was used as a
color-related keyword. Warmth shows a similar pattern,
so is classified as color-related.
cheap,
price
19 comments used cheap as price-related, 1 comment
used “cheap” to describe production method, 0
cheaply
comments used “cheap” as efficiency-related. Thus,
“cheap” is classified as a price-related keyword.
“Cheaply” is also classified as a price-related keyword
accordingly.
expensive,
Price
17 comments used “expensive” as price-related, 1
comment used “expensive” ambiguously, 2 comments
inexpensive
used “expensive” for other products (not bulbs). Thus,
“expensive” is classified as a price-related keyword.
“Inexpensive” is classified as a price-related keyword
accordingly.
long,
longevity
6 comments used “longer” as physical-length-related, 1
longer
(used as “last
comment used “longer” to describe warming up time, 8
long”)
used “longer” as longevity-related, 5 used “longer” as in
Keyword
Classification
Usage in Sampled Comments and Applied Rules
no longer/any longer. When longer is used to describe
longevity, it is usually combined with last as “last
longer”. Thus, “last long” and variants are used to
capture the usage.
cost, costs
price
13 comments used “cost” as price-related, 3 comments
used “cost” as efficiency-related, 1 comment used
“cost” as longevity-related, 2 comments are ambiguous,
and 1 comment is about return-to-sender cost. When
cost is used to describe efficiency, it is “cost of lighting”
“energy cost” and “cost of operation”. For “energy
cost”, the keyword energy will capture the comment
without using “cost” as an efficiency-related keyword.
“Costs” is classified as a price-related keyword
accordingly.
cool, cooler
not used
7 comments used “cool” as color-related, 3 comments
used “cool” as heat-related, 2 comments used “cool” as
in “cool down”, 9 comments used “cool” as nice. To
avoid noise, “cool” is not used. In case cool is used to
describe color, the sentence usually contains other color
related terms such as “cool white”, so the majority of
such comments would be captured by other colorrelated keywords. Reviews that include cooler show a
Keyword
Classification
Usage in Sampled Comments and Applied Rules
similar pattern, and cooler is not used.
save,
efficiency
14 comments used “save” as efficiency-related, 2
saving,
comments used “save” as price-related, 3 comments
saver,
used “save” for other costs such as travel cost, 1
saved
comment used “save” ambiguously.
temperature
not used
17 comments used “temperature” as color-related, 2
comments used “temperature” as heat-related, 1
comment used “temperature” ambiguously. Thus,
“temperature” can be used to classify color, but in most
cases “temperature” is used as “color temperature”, so it
is unnecessary to use temperature.
forever
reduce
longevity
14 comments used “forever” as longevity-related, 5
(used as “last
comments used warming up time related, and 1 is other.
forever” and
The usage is usually “last forever”. “Last forever” and
variants)
its variants are used.
not used
16 comments used “reduce” for irrelevant other
meanings, and 4 comments used “reduce” as efficiencyrelated. When it is efficiency-related, other terms such
energy, electricity, save are used as well, so other
keywords can capture efficiency-related contents.
durable,
durability
not used
8 comments used “durable” for sturdiness, 12 comments
are ambiguous, and 1 comment used “durable” for
Keyword
Classification
Usage in Sampled Comments and Applied Rules
longevity. When “durable” was used ambiguously, it
was likely to be about sturdiness, but this was unclear.
cold
not used
9 comments used “cold” for color, and 12 comments
used “cold” for other meanings. Since other colorrelated keywords can capture comments, “cold” is not
used.
dollar, buck
price
19 comments used “dollar” for price, and 1 comment
used “dollar” for another meaning. “Buck” is similar.
economical
not used
16 comments used “economical” ambiguously, 2
comments used “economical” for efficiency, and 2
comments used “economical”’ for price. It seems that
customers use economical to mean everything related to
cost in general including longevity, bulk purchase, price,
and efficiency. Due to the ambiguity, “economical” is
not used.
Table 5. Frequency of Comments about Each Product Characteristic, by Bulb Type
Total
LED
CFL
Halogen Incandescent
Price
26.6%
26.2%
24.6%
34.9%
24.0%
Color
26.8%
33.4 %
25.5%
13.0%
19.2%
Brightness
32.4%
39.8 %
30.9%
19.2%
19.3%
Heat emission
6.0%
7.8%
4.3%
4.3%
4.4%
Efficiency
9.8%
11.9%
10.2%
5.4%
6.7%
Longevity
2.6%
2.4 %
3.0 %
3.2%
2.5%
Search Total
26.6%
26.2%
24.6%
34.9%
24.0%
Experience Total
47.3%
57.1%
44.5%
28.7%
33.4%
Credence Total
12.0%
13.7%
12.7%
8.5%
8.8%
N
103,003
58,815
23,551
13,633
15,976
Figure 1. Number of Reviews by Month and by Technology
Figure 2. Fraction of Reviews Discussing Bulb Price
Figure 3. Fraction of Reviews Discussing Bulb Price by Technology
Figure 4. Fraction of Reviews Discussing Experience Attributes
Figure 5. Fraction of Reviews Discussing Credence Attributes
Figure 6. Fraction of Reviews Discussing Efficiency by Technology