To survive in the competitive retail landscape, retailers launch service innovations designed to grant additional value to consumers. This study investigates whether and in which circumstances retail service innovations create shareholder value, using stock returns to capture investors’ point of view. An event study is used to analyze a broad, varied set of 350 service innovation announcements by publicly listed retailers. The study shows that the customer value benefit(s) aimed for by the retail service innovation (i.e., its level of convenience and engagement) has an impact on shareholder value. Moreover, this impact is contingent upon the stage of the consumer purchase process that the innovation targets, and upon the hedonic or utilitarian nature of the products offered by the retailer that initiates the innovation. The impact on retailer shareholder value is more positive for service innovations high on convenience that speed up and simplify the shopping process, when implemented at the purchase stage or by retailers offering utilitarian products. Service innovations high on engagement that focus more on non-transactional initiatives instead fare well in the post-purchase stage.
Some service innovations, like personalized advice, can improve products too, through the added service they provide. While this could be the outcome of the service innovations we consider, in this research, we do not focus on product innovations that directly alter the core benefits that the retailer delivers.
As we explain in the Data section, each service innovation is assigned to one purchase process stage only (cf. Lemon and Verhoef 2016) and retailers can offer primarily utilitarian, primarily hedonic or both utilitarian and hedonic products (Li et al. 2020).
The examples we list here (and later) are the service innovations in our sample that were assigned to the pre-purchase stage and had the highest scores on convenience or engagement. See also Appendix and Figure 2.
As some retailers offer both utilitarian and hedonic products, in our results section, we will also compare service innovations that aim at improving convenience and engagement for these type of retailers.
Innovations are often introduced at the banner level (e.g., Walmart introduces separate service innovations for Asda and for Sam’s Club), but we estimate our model at the retailer level, because stock returns are not available at the individual banner level. We do control for the share of the banner.
In contrast to Woodroof et al. (2019), we do not include retailer size (i.e., dollar sales) as a control variable, due to its high correlation with retailer share (both variables log-transformed produce a correlation > .80). But, in a robustness check where we replace retailer share with retailer size, the same substantive results were obtained.
The leverage effect refers to how a 1% change in operating business translates into a percentage change in shareholder value, taking into account the retailer’s debt and non-operating assets (e.g., Schulze et al. 2012).
We took a random selection (10%) of the identified events and verified the announcement data in the Lexis-Nexis database, which also includes a broad selection of business and news publications (Beckers et al. 2018; Raassens et al. 2012). These results point out that Edge Retail Insights includes a larger scope of service innovation announcements and is much quicker in picking up news (for 31.43% there was a delay of on average 28 days). When the announcement was earlier in Lexis-Nexis (for 42.86%), the delay at Edge Retail Insights is limited to 3 days on average (median: 1 day). This falls within the event window that we retain (see below).
A retailer can be active in more than one industry; the primary SIC code indicates its core market. We exclude non-traditional retailers in the building, materials, hardware, garden supply, and mobile home dealer (SIC 52), automotive dealers and gasoline service stations (SIC 55), and restaurant (SIC 58) sectors.
We also identified events in the period January 2010–December 2010 (83 events in total). We use this information to operationalize the control variables order of entry (to assess whether the events in the estimation period are new announcements of service innovations not previously introduced) and the relative retailer innovativeness (to assess how innovative the retailer is compared to the market, for events that happen in 2011).
In line with Beckers et al. (2018) and Raassens et al. (2012), we use the choice of a specific type of service innovation (high/low convenience; high/low engagement; pre-purchase, purchase, or post-purchase stage) instead of the mere announcement of a service innovation. Identifying a sample of non-occurring service innovation announcements would be arbitrary.
There is also a rather high correlation between purchase and pre-purchase (see Table 3), but this is driven by the fact that we only observe a limited set of post-purchase service innovations (53 out of 350).