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Seller marketing capability, brand reputation, and consumer journeys on e-commerce platforms

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Abstract

Seller marketing capability and brand reputation are central to firm performance and customer behaviors. However, little is known about how these two dimensions matter in the increasingly important domain of e-commerce platforms, where sellers are diverse and brand reputations are challenged. This research examines the effects of marketing capability and brand reputation on key customer purchase journey outcomes on e-commerce platforms, from click to browsing time, purchase, and post-purchase frustration. Using smartphone category data from a leading e-commerce platform, the authors demonstrate the positive and increasing effect of marketing capability on consumer journey outcomes. This research also paints a more nuanced view of brand reputation in e-commerce platform environments and illustrates nuanced U-shaped effects of brand reputation on consumer journey outcomes. These findings provide implications for brands and sellers on e-commerce platforms.

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Notes

  1. Source: International Data Corporation (IDC) InfoBrief, SMB Success in the Multichannel Era, January 2020. Methodology: Survey of small and medium business owners, executives and managers across multiple industries, currently selling or planning to sell online, fielded August–September 2019.

  2. Small businesses alone make up 99.9% of U.S. businesses and employ almost 60 million people; see https://cdn.advocacy.sba.gov/wp-content/uploads/2019/04/23142719/2019-Small-Business-Profiles-US.pdf.

  3. https://www.statista.com/topics/2711/us-smartphone-market/

  4. https://www.mordorintelligence.com/industry-reports/smartphones-market

  5. https://arstechnica.com/gadgets/2019/12/fewer-than-10-of-americans-are-buying-1000-smartphones-report-says/

  6. https://www.wsj.com/articles/upgrade-no-thanks-americans-are-sticking-with-their-old-phones-1540818000?mod=rss_Technology

  7. https://www.emarketer.com/content/when-buying-expensive-items-consumers-turn-to-reviews

  8. https://www.marketingcharts.com/industries/retail-and-e-commerce-109539

  9. We count an umbrella brand only as one brand, and house of brands as different brands, e.g., Apple, Samsung, Google, Sony, Motorola, Xiaomi, LG, Nokia, OnePlus, Oppo, Asus. In our analysis, we use brand fixed effects to capture specific brand effects.

  10. Examples of customer frustration words include irritate, disappointment, angry, blow, discontent, upset, annoyed, discomfort, terrible, disappointed, extremely disappointing, worst ever, waste of time, waste your money, super annoying, horrible, chokes, troublesome, wearing on my patience, unbearable, nightmare, frustrating, unacceptable, suck, buyer beware, very disappointed, robbed, bricked, most regrettable purchase, problematic, hate, and major loss.

  11. The result using scale by Patrick and Hagtvedt (2011) has 0.86 correlation with our approach; the result from the Linguistic Inquiry and Word Count (LIWC) (Pennebaker et al., 2015; Tausczik & Pennebaker, 2010) has a correlation of .85 with our approach.

  12. Examples of marketing capability words include polite, responsive, resolved issues satisfactorily, fast delivery, good customer service, great transaction, excellent service, consistent with advertisement, satisfied customer, great price, smooth return, nice packaging, fast shipping, exceptional value, as promised, matching needs, easy check-out, top notch, easy to deal with, dependable, pleasure to deal with, great communication, good price, excellent value, everything is nice, delighted, on time delivery, everything as expected, earlier than the expected delivery, sealed as advertised.

  13. ROC curve provides a way to represent the trade-off between false positives and true positives for different values of the rejection threshold by showing the relation between the sensitivity and specificity of the forecast. This curve is obtained by plotting sensitivity (proportion of times the model predicts a positive when it is actually a positive) versus 1- specificity (proportion of times the model predicts a negative when it is actually a negative) for all possible values of cut-off points.

  14. The AUC summarizes the area under the ROC in the entire range [0, 1] false positive rate. The higher the AUC value, the lower the false positive rate for a given true positive rate (i.e., the model performs better because it identifies true positives more frequently with fewer false positives).

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Appendices

Appendix 1

Robustness tests for customer frustration

To test the robustness of our measure, we employed two approaches. First, we asked two research assistants to assess the degrees of customer frustration (“To what extent, do you think that the customer feels frustrated” on a scale of 1 to 7, 1 = “not at all,” and 7 = “extremely” based on Patrick & Hagtvedt, 2011) for each of the 300 randomly selected comments. The inter-coder agreement is 0.83, and the averaged assessment from the two assistants has 0.86 correlation with our measures, suggesting that our continuous measure can robustly capture the degree of customer frustration for large scale data. We did similar tests for post-purchase customer frustration. Second, Kübler et al. (2020) show that the results of sentiment analyses for marketing models are prone to category effects. Consistent with their recommendations, we use an automated text analysis tool to quantify consumers’ frustrated emotions (e.g., disappointed, frustrated). The Linguistic Inquiry and Word Count (LIWC) program provides the scale score of frustrated emotions using the LIWC2015 Dictionary, which contains a list of 6400 words, word stems, and selected emoticons (Pennebaker et al., 2015). The LIWC is an appropriate and robust tool for textual sentiment analysis, as it can accommodate numbers, punctuation, short phrases, and informal languages, and its internal reliability and external validity are well supported in the literature (Pennebaker et al., 2015; Tausczik & Pennebaker, 2010). The result from LIWC has a correlation of .85 with our approach.

Appendix 2

Robustness check for endogeneity

Although we use a large set of control variables and fixed effects to partial out alternative explanations, our analysis can be subject to endogeneity. For example, current marketing capability can be a function of sellers’ current responses to competitors. Likewise, current brand reputation can be a function of brands’ current marketing campaigns and spending. These unobserved factors potentially impact these independent variables as well as the current consumer journey. Therefore, omitting unobservable factors may result in endogeneity (e.g., Wooldridge, 2010). Accordingly, we use instrumental variables (IV) to account for the possibility that there may be unobserved factors affecting customer journey variables that may be correlated with marketing capability or brand reputation.

The current IV approach for two-stage least squares hinges on the system being linear in the parameters and variables. When they can be applied, IV methods can mitigate omitted variable bias, reverse causality, selection bias, and errors-in-variables in our efforts to estimate casual relationships using observational data. The question of identification in nonlinear models is complex and little can be said about global identification, although conditions for local identification sometimes yield useful insights. While numerous empirical and econometric studies explore the implications of parameter heterogeneity for IV estimation, very few studies focus on the implications of nonlinearity when the estimated model is assumed to be linear. However, in many applications in marketing, there is no particular reason to expect the true relationship to be linear. In our case, theory suggests models that are nonlinear rather than linear. Standard instrumental variable approaches for linear models, such as two-stage least squares, would provide inconsistent results for our nonlinear models (Abrevaya et al., 2010). Therefore, we employ a control function variable approach, which has been used in extant nonlinear marketing models (Jindal, 2020; Srinivasan et al., 2018).

The first step is to find valid instruments. For the instruments to be valid, they must meet the requirements of the relevance and exclusive restrictions. We use the percentage of focal seller’s net marketing capability words (positive marketing capability words minus negative marketing capability words) scaled by the total number of customer review for focal seller six months prior to the actual observation period, and six-months brand social media reach prior to the actual observation on Facebook to instrument current brand reputation.

Our selection of instruments takes advantage of the sequential nature of the key variables (Wooldridge, 2010, 2015). This approach could apply to very old lags which would have no direct effects on current customer behaviors. Logically, using lagged marketing capability words alleviates the concern for sellers’ current competitive response and strategic intent. Likewise, lagged social media reach alleviates the concerns for brands’ current marketing campaigns and spending. These lagged variables are thus not prone to the current observed temporal shocks. However, they reflect the sellers’ and brands abilities to address the marketing and competitive environment of the time, and thus are correlated with current marketing capability and brand reputations. Thus, our instruments satisfy the relevance criteria for instrument variables. As reported in Appendix Table 7, the coefficient estimates for the associated instruments in each of the first-stage regressions are significant (p < .001), indicating that the instruments are relevant.

In practice, older UGC is not salient on e-commerce platforms as customers seldom go through many pages of UGC to get to the old UGC. Given the fast-moving nature of e-commerce platforms (in social media time), sellers also are less likely to go through UGC of six months old to extract insights to improve their current marketing capability. Net marketing capability words derived from older UGC comments therefore have less influence on current customer behavior, and any possible influence of marketing capability derived from UGC will thus be reflected in the latest UGC. The same logic applies to the lagged brand social media reach. These practical considerations thus make our instruments ecologically valid. Thus, our instruments satisfy the exclusion restriction for instrument variables.

We estimate marketing capability and brand reputation as follows:

$$ Marketing\ {capability}_{kt}={a}_{0,}+{a}_1\ast \frac{\Delta Marketing\ capability\ words\ \left( six\ mont hs\ prior\ to\ the\ observation\ period\right)}{Total\ number\ of\ customer\ review\ words\ \left( six\ mont\ priosr\ to\ the\ observation\ peroid\right)}+\kern7em {a}_2\ast social\ media\ reach\ \left( six\ mont hs\ prior\ to\ the\ observation\ peirod\right)+\varphi \ast \mathrm{Exogenous}+\upvartheta . $$
(1)
$$ Brand\ {reputation}_{jt}={b}_{0,}+{b}_1\ast social\ media\ reach\ \left( six\ months\ prior\ to\ the\ observation\ period\right)+\kern6.75em {b}_2\frac{\Delta Marketing\ capability\ words\ \left( six\ months\ prior\ to\ the\ observation\ peroid\right)}{Total\ number\ of\ customer\ review\ words\ \left( six\ months\ prior\ to\ the\ observation\ period\right)}\kern0.75em +\varphi \ast \mathrm{Exogenous}+\upvartheta $$
(2)

where φ is a vector of coefficients that captures the impact of the set of exogenous variables, Exogenous is the vector of exogenous variables including the control variables, and ϑ is the residue.

We tested whether the instrumental variables met the requirements of the relevance and exclusive restrictions. First, Cragg–Donald Wald F-statistics on our instrumental variables for each of the first stage equations demonstrates are all above the rule-of-thumb threshold of 10 (Staiger & Stock, 1997), with the lowest being 375.68 as indicated by Appendix Table 7. Therefore, the null hypothesis of weak instrument can be rejected, and our instruments satisfy the requirement for relevance (i.e., strongly correlated with the endogenous variables). Second, Sargan test (Sargan, 1958) for overidentification could not reject the null hypothesis that the focal instrument was uncorrelated with the error term in the second-stage equation as suggested by Appendix Table 7. These tests suggest the validity of the instrumental variables.

After estimating Eqs. (1) and (2), we corrected for endogeneity bias by entering the residual values into the model specified in Eqs. (3) and (4) described in our model specification section as additional covariates to test for the presence of endogeneity using the standard z-test, after bootstrapping the standard errors (Papies et al., 2017). Appendix 2 Table 6 presents the second stage estimation results and Table 7 presents the first stage results. The second stage regression results are consistent with our main analysis.

Table 6 Control function estimation results (Second stage results)
Table 7 Control function estimation results (First stage results)

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Mu, J., Zhang, J.Z. Seller marketing capability, brand reputation, and consumer journeys on e-commerce platforms. J. of the Acad. Mark. Sci. 49, 994–1020 (2021). https://doi.org/10.1007/s11747-021-00773-3

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