How legacy firms can embrace the digital ecosystem via digital customer orientation

Abstract

Because of modern digital technologies, business environments are turning into digital ecosystems, wherein a firm’s traditional interdependencies are increasingly influenced by digital connectivity. For legacy firms, or firms yet to incorporate these technologies into their business models, this shift ushers in new opportunities for value creation, albeit through new capabilities. In this article, we focus on how legacy firms can embrace digital ecosystems to create value through a new capability: digital customer orientation. We define digital customer orientation as offering customized and enriched customer experiences made possible by embracing digital ecosystems. We develop a framework for legacy firms to develop capabilities for digital customer orientation in three steps: (1) by distilling key insights on how digital natives such as Amazon, Google and Facebook leverage their digital ecosystems for digital customer orientation; (2) by showing how legacy firms can apply those insights to harness their digital ecosystems and develop their own approaches for digital customer orientation; and (3) by offering a road map and a research agenda for legacy firms to engage in digital customer orientation—both by highlighting the organizational attributes needed, and by framing those attributes within the principles of transformative marketing.

Appendix: Pre-test

Here we provide a pre-test of the impact of the three basic organizational factors on a firm’s emerging customer orientation. We mailed surveys to 252 senior executives whose names were obtained from 19 large Fortune 500 corporations that were part of a corporate sponsorship and recruiting program at a leading U.S. business school. The need to obtain access, and the constraints on time and funding, prevented the use of a random sample of firms from the entire Fortune 500 list. However, as described below, given the size and range of the companies studied, and the diversity of industries they represent, there was no prima facie reason to expect any systematic bias in our results.

We followed Huber and Power’s (1985) guidelines for single informant data collection. Since all our respondents were at the vice president or general manager level, they were knowledgeable about their respective firm’s customer orientation, culture, etc. Personalized cover letters were sent explaining the purpose of the study and assuring confidentiality. In order to minimize bias, respondents received self-addressed envelopes to return the completed questionnaires directly to the authors. Thirteen executives indicated they would not be able to complete the survey as they were not qualified to respond. Of the remaining 239 potential respondents, we received 128 surveys, yielding a response rate of about 57.7%, a relatively high rate considering our respondents were senior-level executives. Of the 138 surveys, 10 had many missing values, thus reducing the effective sample size to 128 and the response rate to 53.6%. Following Srinivasan et al. (2002), we assessed the non-response bias by conducting a t-test between responses from early and late respondents on key variables; we found no significant differences. Further, there was no significant difference between the respondents and the non-respondents with respect to such corporate characteristics as sales and employees.

Our sample covered six industry sectors: consumer non-durables (14.7% of sample), light manufacturing (18.4%), heavy manufacturing (17.7%), pharmaceuticals (19.8%), technology (19.1%), and telecommunications (11.1%). The average annual corporate sales ranged from $9.4 billion to $39.5 billion; and the average number of corporate employees ranged from 34,844 to 102,400, thus yielding a heterogeneous sample of firms.

Measures

We reviewed previous research to locate, where possible, measures that would appropriately capture the constructs under study. As a pre-test, we note that the emerging customer orientation discussed in Govindarajan et al. (2011) is similar in spirit to digital customer orientation and hence we used the emerging customer orientation in our pre-test. The various measures, the respective sources, and the coefficient alphas (or the correlations) are as follows. (All Likert-type items use 7-point, strongly disagree/strongly agree scales, unless otherwise specified. Items with an asterisk are reverse scaled).

• I. Emerging Customer Orientation (based on Govindarajan et al. 2011) (Coefficient Alpha = .91, GFI = .95)

(1) This firm pursues ideas that emerging customer segments value. (2) This firm sufficiently allocates the critical financial and human resources to carve out a strong position in emerging customer segments. (3) This firm focuses adequate energy and talent on emerging customer segments. (4) This firm places a lot of emphasis on customers of the future.

• II. Culture (based on Deshpandé et al. 1993) (Coefficient Alpha = .87): (1) This firm is a very dynamic and entrepreneurial place. People are willing to stick out their necks and take risks. (2) The head of this firm is generally considered to be an entrepreneur, an innovator, or a risk taker. (3) The glue that holds this firm together is a commitment to innovation and development. There is an emphasis on being first.

• III. Incentive (based on Govindarajan 1988) (r = .28, p < .01): (1) Your annual incentive bonus is based on a formula tied to actual performance on quantifiable criteria rather than based on a subjective judgment.* (2) In your periodic evaluation, your superior places a lot of emphasis on short-term performance versus long-term performance.*

• IV. Structure (based on Benner and Tushman 2003): During the past 5 years, this firm has created separate organizational units to develop innovations.

Control variables

Based on prior research (Ahuja & Lampert 2001; Capon et al. 1992; Chandy and Tellis 1998; Katila 2002), we used six firm-level objective variables as controls. They are (i) size, operationalized as the natural log of number of employees at the firm level; (ii) corporate performance, operationalized as corporate net profit margin and (iii) corporate return on investment; (iv) organizational slack, operationalized as corporate debt-to-equity ratio; (v) fixed-asset intensity, operationalized as corporate net fixed assets divided by corporate sales; and (vi) research and development intensity, operationalized as corporate R&D expenditure divided by corporate sales. Also, since there are likely to be industry-level differences in the innovation types, we controlled for industry-specific effects via five industry dummies representing the six industry classes in our data (Srinivasan et al. 2002). See Table 1 for variable list, descriptive statistics, and correlations.

Results

We conducted multiple regression analysis and included firm-level control variables and five industry dummies. The results are presented in Table 2. With respect to incentives for key executives, subjective incentives show a positive and significant (p < .1) effect on a legacy firm’s orientation toward emerging customer orientation, adhocracy culture shows a significant (p < .01) positive effect on the orientation toward emerging customer orientation at a legacy firm. Creation of separate organizational units for digital innovation has the hypothesized significant (p < .05) positive impact of fostering a legacy firm’s emerging customer orientation. See Table 1 for variable list, descriptive statistics, and correlations.

Table 1 Descriptives and correlations (n = 128)

Table 2 Regression results (standard errors in parentheses, n = 128) dependent variable: emerging customer orientation