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Tutorial Summary 8: Digital Service Adoption
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Summary of articles discussed during the tutorial. Emphasis on Study Design, Results and Implications
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SESSION 8: Digital Service AdoptionDigitalization gives rise to all kinds of new services that potentially transform the customers adopting and
using these new services. In this session we focus on two of these new services, namely smart home devices
(Adamopoulos et al. 2020) and digital music services such as Spotify (Datta et al. 2018). Customers using
such devices potentially change their purchase behavior by taking advantage of the possibilities offered by
such devices. First article discus the question if mobile home devices changes consumer shopping behavior. The
second one how availability of content like Music (Spotify), Movies (Netflix) impacts the users adopting such
devices.
Adamopoulos et al. (2020) Demand Effects of the Internet Of Things Sales Channel: Evidence from
Automating the Purchase Process
Internet of things (IoT): Uniquely identifiable physical objects embedded with electronics, sensors/actuators,
software, and wireless network connectivity that enable these objects to exchange data over the internet with
low energy consumption.
Method: Using empirical data form an online retailer who adopted IoT that largely automates the consumers’
purchases, minimizing human interaction (see p. 9 to see where data comes from).
- Quasi-experimental design
- DiD & DDD
- Additional analyses of the IoT effect and delve into the effect heterogeneity, examining various
important moderating effects on the impact of IoT, empirically validating the underlying mechanism of
the effect.
Main Findings:
- When a product becomes eligible for purchase also through the IoT channel, products sales
experience statistically and economically significant increase, highlighting the business value of IoT
technologies for retailers and brands.
- Less expensive, experience goods (vs. search goods), utilitarian (vs. hedonic), more differentiated
products benefit more of the introduction of the IoT channel.
- Increase in sales is mainly because of an increased demand from the existing customer base.
- Verify the underlying mechanisms drawing on mental accounting and automaticity theory; a
technological development affecting efficiency, ease, speed at which products can be purchased -
lowering intangible transaction costs – useful means of adjusting the mental accounting of customers
and changing their consumption patterns (depicted by product demand levels).
Implications:
- Marketers should invest in IT because of the significant positive impact of these technologies on
product sales.
- Understand which products would accrue higher benefits from leveraging more effectively the novel
IoT technologies and which would benefit less.
- More accurate product sales predictions for retailers, leading to more efficient supply chain
operations.
- Allows collection of valuable data at the time of usage of physical products (consumption analytics).
- Need to implement security and encryption protocols to effectively protect any sensitive consumer
information. And foster consumer trust by adapting the policies to the channel.
- sict is the sales rank of product i in market c in time period t within the corresponding product category
(sales rank for each product is a proxy for demand).
- Treatmentict is a binary variable indicating whether product i was treated in market c in time period t.
- βT captures the effect of IoT on product sales (coefficient of main interest).
- Xict control for time-varying covariates (e.g., product price, rating, reviews, …)
, - Zict additional control such as the seller or public holidays.
Primary Identification Strategy (DiD)
Using both treated products – before and after the treatment
– and nontreated products that are similar to treated products
in the same market.
- Model 1: Examines the impact of IoT introduction on
product sales while accounting for the product rating,
the number of user-generated review, product price,
product seller, as well as product-market fixed effects
and nonlinear time trends.
- Model 2: Controls for user-generated reviews solicited
by the seller.
- Model 3: In addition to the aforementioned variables,
controls for holidays in order to capture additional
seasonality effects.
The coefficient of the variable capturing the IoT channel
introduction is negative and statistically significant, suggesting
that when a product is becoming eligible for IoT purchase, the
product’s sales increase (i.e., lower sales rank). Economically
significant because the introduction of the IoT technology leads,
on average to an improvement of about 13.92% in sales ranking
- Price: Higher product prices increase the sales rank and therefore reduce product sales; if a product
price is increased by 1%, the sales rank increases by about 0.64%.
- If the average rating is increased by 1 unit (e.g., 1 star, an increase of +- 30% of the product
average rating), the sales rank decreases by 3.28%.
- Volume of reviews: If the number of reviews is increased by 1%, the sales rank improves by 0.43% (if
two similar review ratings, choosing the one with more ratings).
- Larger fraction of solicited reviews has a negative impact on sales. If the proportion of solicited
reviews is increased by 1%, the sales rank decreases by almost 0.75%.
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