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Enterprise Systems and Innovations

Enterprise Systems and Innovations. Benjamin Engelstätter ZEW Mannheim CoInvest Lisbon, Portugal March, 18 - 19 2010. Brief Introduction. Enterprise Systems (ES) Software to control, manage and support business processes Three Main Branches

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Enterprise Systems and Innovations

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  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) zi* = Xi’β1 + IDi’β2 + ESi’β3 +εizi = 1 if zi* ≥ 0; zi = 0otherwise Number of innovations (2) yi* = Zi’λ1 + IDi’λ2 + ESi’λ3 +γiyi= yi* if zi = 1; yi= 0 ifzi = 0 Variables zi – Product/Process Innovation yi – Number of Product/Process Innovation Zi / Xi– determinants affecting innovation IDi – control dummies for industry sector ESi – Enterprise Systems in use εi / γi – 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. f (y) = f (y) = Possible Models • Hurdle model • reflecting 2 stage decision making process • each part model of one decision • f1(·) determining zeros, f2(·) determining positive counts • both parts functionally independent • 1st part uses full sample, 2nd only positive count observations • Zero-inflated Model • f1(·) determining zeros, f2(·) 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)

  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 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. Zero-inflated neg. bin. model selected in both cases Descriptive Evidence and Model Selection Table 2: Descriptive analysis Notes: Standard Errors in parentheses. Source: ZEW ICT survey 2004, 2007 and own calculations. Table 3: Model selection Notes: *** p<0.01, ** p<0.05, * p<0.1; Source: ZEW ICT survey 2004, 2007 and own calculations.

  12. Results – Process Innovations 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. Table 7: R&D spending and ES usage Dependent variable: R&D spending in share of total sales in 2006 (OLS) workforce characteristics, ISO, former process innovator n. s. Share of high skilled workers 0.120*** (0.040) Product innovations last period 0.030** (0.013) ERP 0.011 (0.014) SCM 0.031** (0.015) CRM 0.013 (0.015) Controls Industry, East, Size, Org Factors Marginal Effects and Robustness Checks - Process Innovations Table 5: Marginal Effects (short-term) 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.

  14. Results – Product Innovations 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 Table 9: Marginal Effects (short-term) 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

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