ICT and e-Business Impact in the Furniture Industry - empirica

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ICT and e-business impact in the furniture industrynumber and size of supplying firms as well as the number and preferences of customersand their size in case of businesses. An important aspect of market structure dynamics isthe level of ease of market entry. Further industry structure characteristics are related toproducts, production and production factors: the degree of product differentiation, thedegree of vertical integration of production, i.e. value chain characteristic, thetechnologies available to the firms, the firms’ cost structure (i.e. the relative importance ofcosts for items such as production facilities, energy, personnel), and finally the workforcecomposition and the demand for labour, most importantly with regard to knowledge andskills. All these characteristics determine the level of competition in the industry.These industry structure components influence a firm’s conduct. The conduct aspectsmost important here are production strategies, particularly with regard to inter-firmcollaboration, as well as investments in ICT and in ICT-enabled innovation.Finally, a firm’s performance is assumed to be the outcome of its conduct. Successfulinnovations improve firm performance by, for example, reducing production cost,increasing productivity, improving product quality or enabling it to enter new markets. Thismay eventually lead to increased sales, turnover and market shares.Extending the SCM paradigm: feedback effectsIn contrast to the standard SCP paradigm, the flow of causality is in fact not onedirectional(Fauchart and Keilbach, 2002 and Nepelski, 2003). As an example offeedback between performance and industry structure, successful and innovativecompanies are more likely to grow and increase their market share at the expense of lessprogressive firms, which transforms the market structure. There may also be feedbacksbetween conduct and industry structure: for example, depending on the innovation type –i.e. product or process innovation, ICT-enabled or not –, innovations influence the choiceof products manufactured and a firm’s cost structure. Innovations may also change theincentives to perform activities in-house versus outsourcing them and, consequently, mayinfluence the demand for labour and its composition. It may also further shape therelationships with suppliers and customers, for example with regard to collaborationintensity. Thus, in the following discussion it is assumed that firm performance may havea feedback effect on both firm conduct and industry structure, and conduct may have afeedback on structure. This conceptualisation allows for an enhanced economic approachthat studies the drivers and impacts of ICT and ICT-enabled innovations at the firm andsector level. Exhibit 4.1-1 illustrates the SCP paradigm together with the causalityrelationships of the elements studied in this sector report.Exhibit 4.1-1: Conceptual framework for the analysis of e-business impactsStructureStructureConductConductPerformanceMarket Market / / firmfirmcharacteristics:characteristics:- - MarketMarketstructurestructure- - TechnologyTechnology- - Value Value chainchainICTICTICT ICT enabledenabledadoptionadoptioninnovationinnovationFeed-back loopsPerformance:Performance:- - ProductivityProductivity- - TurnoverTurnover- - Market Market shareshareSource: DIW/empirica79
ICT and e-business impact in the furniture industryThe extended SCP paradigm defines the two dimensions of the forthcoming analysis.First, the extended SCP paradigm identifies market structure and firm characteristics thatdrive the diffusion of ICT and the process of turning ICT use into marketable products andproduction processes, i.e. ICT-enabled innovations. Second, the paradigm seeks toidentify the feedback effects of firms’ innovative activity on these characteristics and firmperformance.Applying the SCM paradigm to an analysis of ICT drivers and impactsThe SCM paradigm allows identifying firm and industry dimensions that can beconsidered as relevant for the diffusion of ICT and its impact on these dimensions.Consequently, the following elements of market and firm structure were identified as ICTdrivers: market rivalry, supplier-buyer relations and workforce composition. The impact ofICT adoption and ICT enabled innovation is studied through productivity and employmentas proxies for firm performance. This construct enables the understanding of not only unidirectionalcausal relationships but recognises the presence of firm performanceimpacting upon the drivers of ICT adoption.The following analysis is based on a set of hypotheses which focus on the critical role ofICT for innovation. The hypotheses allow for an economic approach to study the impactsof ICT at the firm level for the following business dimensions: innovation dynamics(Section 4.2), market structure (Section 4.3), and value chain characteristics (Section4.4). The hypotheses are tested on the basis of data from the e-Business ManufacturingSurvey 2007.In contrast to the other sectors studied in the Sectoral e-Business Watch 2007/2008,appropriate secondary sectoral data from the EU KLEMS database are not available forthe furniture sector. These data are essential for an econometric analysis of impactsrelated to productivity and employment. Consequently, an analysis of the dimensions ofproductivity and employment that would complete the conceptual framework cannot beprovided in the furniture industry report.In sections 4.2 - 4.4, regression analysis is used. For methodological reasons one shouldbe cautious with drawing conclusions about “drivers” and “impacts” of ICT; it ismore reasonable to refer to “links” between ICT adoption and other variables. Firstly,regression analysis is a technique used for modelling and analysing data, assuming thatone variable is dependent upon another single independent variable (simple regression)or several independent variables (multiple regression). Although regression can be usedto analyse causal relationships, one must be cautious in drawing conclusions regardingcausality, because there is typically a broad range of potential non-causal explanations oflinks between variables. In statistics, this is referred to as "confounding", i.e. aconfounding variable is associated with both the assumed cause (independent variable)and the assumed outcome (dependent variable). Secondly, the estimation results do notallow for conclusions about the direction of causality, mainly because the dependent andthe independent variables are reported for the same time period.80
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ICT and e-Business Impact in the Furniture Industry - empirica

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