Abstract
There is a rapid increase in the use of enterprise technologies, such as enterprise portals, CRM, RFIDs, and partner interface systems. Often defined as Enterprise 2.0 technologies, social communication technologies, such as blogs and wikis are further increasing the range of ITs used by firms. Greater use of these Enterprise 2.0 technologies is changing the ways organizations transact with partners, as IT systems become predominant means to communicate, coordinate, analyse, conceptualize, and respond. As these technologies increase the information content of work, IT intensity of firms is becoming an increasingly important asset for doing business. To leverage enterprise technologies, firms are increasing IT intensity by making greater investments in more modular, user-friendly, integrated, and customized ITs. Greater IT intensity changes how a firm transacts business and inter-organizational relationships. However, the effects of increased IT intensity on inter-organizational relationships are not clear. In this study, we address this gap and assess how a firm’s IT intensity influence inter-organizational value. Using the data from customer-supplier dyads, we examine partner-related value creation, co-creation, and appropriation of value between firms within customer-supplier relationships. Using the Compustat database as our source of financial information from publicly-traded firms, we identify 5868 unique dyadic pairs of customer-supplier over the period 1991 to 2005 and hypotheses are tested using a sample of 15,028 customer-supplier dyad-years. IT spending for each firm is based on data available from InformationWeek magazine. Our results indicate that a partner’s IT usage co-creates value for both partners. We show a strong positive relation between customer and supplier IT spending intensity and corresponding profitability— measured as the firm’s excess gross profit relative to industry levels — for both customer and supplier firms as well as the combined dyad. Also, contrary to views that larger customers exploit smaller suppliers, our study finds that an increased IT intensity enhances the probability of value being generated for both the partners in a dyad.
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Notes
Compustat is a database that holds financial and other information related to global securities dating back to 1950.
For the first column, the dependent variable is suppliers’ excess gross profit. For the second column, the dependent variable is customers’ excess gross profit, and for the third column, the dependent variable is combined excess gross profit, i.e., co-created value. Column 4 presents results where actual IT/Sales information is available for customer firms.
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Appendix
Appendix
Note1: Financial Accounting Standards
FAS 14 and 131 require firms to disclose a significant customer relationship if “a single external customer amount to 10 % or more of an enterprise’s revenues.” Although there is no requirement to disclose the name of the customer and the actual level of sales to that customer, firms often report that information. To the extent that it is reported, Compustat lists customer names and sales volumes.
Note 2: Information Week
InformationWeek gathers IT spending data for 500 larger companies each year, and since the customer firms in the customer-supplier dyads are generally larger, there is substantially more IT spending information available for them than for the smaller supplier firms. Thus, IT spending data from InformationWeek is available for approximately 8000 customer years but less than 800 supplier years. For the remainder of the firm years, we impute IT spending data based on the available InformationWeek data using two different approaches.
Note 3: Imputation Procedure
In our first approach, we use a model developed by Kobelsky et al. (2008), which describes the determinants of IT budgets in terms of firms’ organizational, environmental, and technological factors. They present the following model of the determinants of IT budgets:
where IT/Sales represents firm-year IT budget scaled by sales revenue, ind_conc_ratio measures four firm industry concentration ratio for the four-digit SIC, related_diversity measures the proportion of the firm’s sales in related industries, and unrelated_diversity measures the same proportion in unrelated industries, op_ros is operating income before depreciation scaled by sales, debt_ratio represents the ratio of long-term debt to total assets, sales_growth is average sales growth over the previous two years, automate, transform, high_tech, and low_tech are dummy variables indicating if the firm is in automate, transform, high-tech and low-tech industries respectively, and e is the firm-year residual. Using Eq. (5), we impute missing values for firm IT spending, and we call this variable IT_sls. This approach assumes that firms’ IT spending does not vary substantially with firm size, but Kobelsky et al. (2008) note that scaling IT spending by sales effectively controls for size and further controls for size were unnecessary.
Note 4: customer and supplier observations by Fama and French industry categories
Suppliers come from almost every industry, but the customers tend to be more concentrated; there are relatively more customers in the Machinery and Business Equipment, Retail Store, Automobiles, and Other industry categories. We recognize that inventory management is not generally an important performance measure for financial firms, but these firms did report inventories, there are relatively few firms in that industry, and there was very little change in results when we omitted that industry.
Note 5: Inventory performance measures
“Days’ sales in inventory” is a measure of inventory management effectiveness, and lower values generally indicate better performance although a firm’s performance should be compared to performance in the industry (Fabozzi et al. 2007).
Note 6: Descriptive statistics for customers and suppliers
On average, customer firms are much larger than supplier firms. The median customer has over $24 billion annual revenue, while the median supplier firm has about $150 million annual revenue. On a combined basis, the average dyad has about 4.5 to 5.5 months of inventory on hand. For both customers and suppliers, inventory represents a substantial investment. Although not tabulated, the investment in inventory for both represents about 16 % of total assets and exceeds the average net investment in plant, property, and equipment.
Note 7: Performance based on supplier size
For this panel, we divided the suppliers into three portfolios based on annual revenue. Clearly, larger suppliers are more likely to be linked with larger customers on average. The average small supplier is linked to customers with $33 billion annual revenue, but the average large supplier is linked to customers with $64 billion annual revenue. Large suppliers and their customers have significantly fewer days’ sales in inventory than small suppliers. Large suppliers and their customers are also significantly more likely to earn excess gross profits relative to their industry. Thus, supplier market power matters; the size of the supplier affects the profitability of the supplier, customer, and the dyad.
Note 8: Results of hypotheses testing
Hypotheses | Supported? |
H1a: Greater IT intensity at the customer firm will lead to greater value for their suppliers. | Supported |
H1b: Greater IT intensity at the supplier firm will lead to greater value for their customers. | Supported |
H2a: Greater IT intensity at supplier firm will lead to greater value co-creation for the relationship. | Supported |
H2b: Greater IT intensity at customer firm will lead to greater value co-creation for the relationship. | Supported |
H3a: The likelihood that both partners appropriate co-created value increases as supplier IT intensity increases. | Supported |
H3b: The likelihood that both partners appropriate co-created value increases as customer IT intensity increases. | Supported |
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Setia, P., Richardson, V. & Smith, R.J. Business value of partner’s IT intensity: value co-creation and appropriation between customers and suppliers. Electron Markets 25, 283–298 (2015). https://doi.org/10.1007/s12525-015-0189-7
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DOI: https://doi.org/10.1007/s12525-015-0189-7