1. Enterprise Systems and Innovations Benjamin Engelstätter ZEW Mannheim CoInvest Lisbon, Portugal March, 18 - 19 2010

2. Brief Introduction Enterprise Systems (ES) Software to control, manage and support business processes Three Main Branches Customer Relationship Management (CRM): Front Office Enterprise Resource Management (ERP): Middle Office Supply Chain Management (SCM): Back Office Additional Types Technical Software (CAx) MES, PLM, … Market 39 billion USD for complex enterprise systems in 2008, 1.9 Bil. Euro in Germany Market for large firms is satisfied, SMEs are now focused especially ERP, SCM and CRM spread out worldwide

3. Enterprise Systems and Innovations Enterprise Resource Planning standardizes complex interfaces and automates financial transactions collects and updates firm intern data in real-time Supply Chain Management coordinates flow of information, materials and finances along the value chain improves operational and business planning with real-time planning capabilities Customer Relationship Management provides a firm-wide centralized database of customer information offers a complete view of customer needs and wants Possible Effects on Innovations SCM & ERP identify bottlenecks and shortages generated databases provide exact information facilitating process enhancements CRM database can be used as information source for product innovations

4. Contribution Effects on Innovation First empirical evidence of the impact of adopting any of the three main enterprise systems on firms’ innovational performance process as well as product innovations are concerned 1st Step: Revealing impacts of enterprise systems on probabilities to innovate 2nd Step: Revealing impacts of ES on number of realized innovations

5. Literature Direct Effects on Innovation ERP facilitate the building of business innovations (Shang & Seddon, 2000) customer preferences retrieved via CRM improve innovational success (Joshi & Sharma, 2004) ES allow people to be more innovative (Davenport, 1998) Indirect Effects on Innovation business units more innovative if in central network position (Tsai, 2001) more innovation through upstream /downstream contacts to suppliers and customers (Chriscuolo et al., 2004) organizational flexibility leads to increased innovative activity (Hempell & Zwick, 2005) → ES offer advantages in all categories and might foster innovational performance

6. Research Methodology Innovation/Knowledge Production Function output of innovation process represents result of several research linked inputs (1) z i * = X i ’β 1 + ID i ’β 2 + ES i ’β 3 +ε i z i = 1 if z i * ≥ 0; z i = 0 otherwise Number of innovations (2) y i * = Z i ’λ 1 + ID i ’λ 2 + ES i ’λ 3 +γ i y i = y i * if z i = 1; y i = 0 if z i = 0 Variables z i – Product/Process Innovation y i – Number of Product/Process Innovation Z i / X i – determinants affecting innovation ID i – control dummies for industry sector ES i – Enterprise Systems in use ε i / γ i – standard error term

7. Estimation Procedure Procedure Maximum Likelihood count data corner solution with 2-part model 2 alternatives: -Hurdle model -Zero-inflated model both allow for separate treatment of zeros and strictly positive outcomes 2 possible distributions: -Poisson -Negative-binomial

8. Possible Models Hurdle model reflecting 2 stage decision making process each part model of one decision f 1 (·) determining zeros, f 2 (·) determining positive counts both parts functionally independent 1st part uses full sample, 2nd only positive count observations Zero-inflated Model f 1 (·) determining zeros, f 2 (·) determining positive counts 2 types of zeros: - one type arising from binary process - other type is realization of count process (when binary process takes on 1) f (y) =

9. Database ZEW ICT Survey computer-aided telephone survey specific focus on diffusion and use of ICT in German companies one recent ICT topic specifically covered each wave each wave contains about 4000 firms with 5+ employees seven branches of manufacturing, seven selected service sectors five waves (2000, 2002, 2004, 2007, 2010) waves of 2004 and 2007 used in current analysis

10. Variables Table 1: Summary statistics VariableMeanStd. Dev.DV 2 Software Use 04MeanDV 2 process innovations 04-060.635yesno software0.231yes number of process innovations3.1034.057ERP0.635yes product innovations 04-060.600yesSCM0.434yes number of process innovations5.0059.639CRM0.524yes process innovations last period0.755yesall three0.275yes product innovations last period0.654yes labor 1 213.0636.4Additional control variables share of computer workers0.4690.329working hours 3 0.704yes share of highly skilled workers0.2260.259job rotation0.191yes share of medium skilled workers0.5570.262quality circles0.425yes ISO certificated0.444yesown cost units 4 0.386yes East Germany0.267yesworkgroups 5 0.623yes Number of Observations989 Notes: 1 Labor is measured in total number of employees. 2 Dummy variable. 3 Accounts for working hours. 4 Units with own cost and result responsibilty. 5 Self dependent workgroups; Source: ZEW ICT survey 2004, 2007. Own calculations.

11. Descriptive Evidence and Model Selection Table 2: Descriptive analysis No systemAll systemsERPSCMCRM recent process innovator0.4780.7900.7260.7670.726 number of process innovations mean 1.783 (2.774) 4.039 (4.252) 3.764 (4.402) 3.935 (4.424) 3.590 (4.274) recent product innovator0.4080.7280.6670.7000.681 number of product innovations mean 2.142 (5.045) 7.167 (10.82) 6.002 (10.15) 6.859 (10.87) 6.197 (10.45) Notes: Standard Errors in parentheses. Source: ZEW ICT survey 2004, 2007 and own calculations. Process innovationsProduct innovations Vuong-Test6.050***7.830*** Llhd.-ratio Test449.220***2864.020*** Notes: *** p<0.01, ** p<0.05, * p<0.1; Source: ZEW ICT survey 2004, 2007 and own calculations. Table 3: Model selection Zero-inflated neg. bin. model selected in both cases

12. Results – Process Innovations Specification (1)Specification (2) Probit Model Neg. Bin. Model Probit Model Neg. Bin. Model ISO certification -0.270** (0.140) 0.230** (0.092) -0.137 (0.149) 0.196** (0.095) Process innovations last period -0.377** (0.130) 0.262*** (0.098) -0.310** (0.139) 0.242** (0.105) Enterprise Resource Planning -0.141 (0.136) 0.282*** (0.105) -0.110 (0.138) 0.279*** (0.105) Supply Change Management -0.318** (0.137) 0.064 (0.089) -0.284* (0.153) 0.059 (0.096) Customer Relationship Management -0.181 (0.133) -0.068 (0.094) -0.198 (0.141) -0.103 (0.095) Controls Industry, East, Size, Workforce Char Industry, East, Size, Workforce Char Industry, East, Size, Workforce Char, Org Factors Industry, East, Size, Workforce Char, Org Factors Number of Observations890 (547 non-zero, 343 zero) Table 4: Determinants of the number of process innovations, zero-inflated neg. bin. estimates Notes: *** p<0.01, ** p<0.05, * p<0.1; robust standard errors in parentheses. Overdispersion coefficient alpha highly significant (not reported). Source: ZEW ICT survey 2004, 2007 and own calculations.

13. Marginal Effects and Robustness Checks - Process Innovations Spec. 1Spec. 2 overall marg. effect ERP0.972*** (0.291) 0.923*** (0.294) overall marg. effect SCM0.624** (0.292) 0.556* (0.300) overall marg. effect CRM0.057 (0.288) -0.024 (0.288) Table 5: Marginal Effects (short-term) Spec. 1Spec. 2 overall marg. effect ERP0.856** (0.360) 0.913** (0.376) overall marg. effect SCM0.415 (0.457) 0.271 (0.447) overall marg. effect CRM0.122 (0.391) 0.019 (0.383) Table 6: Marginal Effects (medium-term, ES use 02) Notes: *** p<0.01, ** p<0.05, * p<0.1; robust standard errors in parentheses. Source: ZEW ICT survey 2004, 2007 and own calculations. Controls 0.013 (0.015) CRM 0.031** (0.015) SCM 0.011 (0.014) ERP 0.030** (0.013) Product innovations last period 0.120*** (0.040) Share of high skilled workers workforce characteristics, ISO, former process innovator n. s. Dependent variable: R&D spending in share of total sales in 2006 (OLS) Table 7: R&D spending and ES usage Industry, East, Size, Org Factors

14. Results – Product Innovations Specification (1)Specification (2) Probit Model Neg. Bin. Model Probit Model Neg. Bin. Model ISO certification -0.364** (0.172) -0.135 (0.142) -0.345** (0.174) -0.109 (0.138) Product innovations last period -1.067*** (0.152) -0.010 (0.182) -1.051*** (0.151) -0.031 (0.177) Enterprise Resource Planning -0.085 (0.170) 0.027 (0.150) -0.144 (0.173) -0.091 (0.155) Supply Change Management 0.135 (0.182) 0.116 (0.140) 0.153 (0.180) 0.070 (0.134) Customer Relationship Management -0.296* (0.161) 0.084 (0.129) -0.326** (0.163) 0.012 (0.126) Controls Industry, East, Size, Workforce Char Industry, East, Size, Workforce Char Industry, East, Size, Workforce Char, Org Factors Industry, East, Size, Workforce Char, Org Factors Number of Observations886 (490 non-zero, 396 zero) Table 8: Determinants of the number of product innovations, zero-inflated neg. bin. estimates Notes: *** p<0.01, ** p<0.05, * p<0.1; robust standard errors in parentheses. Overdispersion coefficient alpha highly significant (not reported). Source: ZEW ICT survey 2004, 2007 and own calculations.

15. Marginal Effects - Product Innovations Specification 1Specification 2 overall marg. effect ERP0.345 (0.728) -0.084 (0.757) overall marg. effect SCM0.238 (0.730) -0.035 (0.704) overall marg. effect CRM1.156* (0.675) 0.866 (0.654) Table 9: Marginal Effects (short-term) Specification 1Specification 2 overall marg. effect ERP-0.385 (1.089) -0.611 (1.139) overall marg. effect SCM-0.390 (0.984) -0.344 (0.973) overall marg. effect CRM1.544 (1.130) 1.522 (1.137) Table 10: Marginal Effects (medium-term, software use 02) Notes: *** p<0.01, ** p<0.05, * p<0.1; robust standard errors in parentheses. Source: ZEW ICT survey 2004, 2007 and own calculations.

16. Conclusion Main Results ERP+SCM positively impacts number of process innovations SCM usage lowers probability of being a non-innovator in case of process innovations both results stable for short and medium-run CRM users face a higher probability to product innovate (only short-term based) Implications manager should not only focus on possibly huge costs and expected fast evolving performance benefits when purchasing or upgrading ES increased process innovational performance via SCM and ERP might even reduce costs product innovations realized based on CRM data might increase financial performance via opening up new markets