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Research Policy 42 (2013) 1239-1250 Contents lists available at SciVerse ScienceDirect Research Policy journal homepage:elsevier.com/locate/respol...

In this assignment, assess the performance metrics that can be used to evaluate the success of innovation and marketing. The assessment must include:

  • Discuss the importance of metrics
  • Discuss/assess at least five metrics to measure innovation
  • Discuss/assess at least five metrics to measure marketing strategy
  • Recommend the appropriate metrics to use for your company


Support your paper with a minimum of three (3) peer reviewed articles published in the last five years. In addition to these specified resources, other appropriate scholarly resources, including older articles, may be included.

Length: 3-5 pages not including title page and references

Research Policy 42 (2013) 1239– 1250 Contents lists available at SciVerse ScienceDirect Research Policy jou rn al hom epage: www.elsevier.com/locate/respol You can’t manage right what you can’t measure well: Technological innovation efFciency q Claudio Cruz-Cázares a , , Cristina Bayona-Sáez b , Teresa García-Marco b a University of Barcelona, Diagonal Avenue 690, Tower 2, 3rd Floor, 08034 Barcelona, Spain b Public University of Navarra, Edi±cio Departamental de los Madro˜nos, 31006 Pamplona, Spain a r t i c l e i n f o Article history: Received 11 December 2011 Received in revised form 7 March 2013 Accepted 28 March 2013 Available online 22 April 2013 Keywords: Technological innovation efFciency DEA bootstrap Global Malmquist index ±irm performance a b s t r a c t This paper proposes a new approach to tackle the innovation–performance relationship. It addresses the, so far, mixed and inconclusive results of studies analyzing this relationship. We argue that the undiffer- entiated use of innovation inputs and outputs to measure Frm innovativeness is not without problems, and that, from a productive perspective, they should be simultaneously analyzed. This study follows a two-stage empirical analysis using a sample of Spanish manufacturing Frms for the period 1992–2005. By examining two inputs and two outputs of the innovation process in the Frst stage, we estimate tech- nological innovation efFciency by means of an intertemporal data envelopment analysis (DEA) bootstrap and also observe the yearly efFciency changes based on a global Malmquist index. In the second stage we analyze the effect of technological innovation efFciency on Frm performance through a generalized method of moments (GMM) system. The results support our arguments that the best measurement of out- comes of technological innovations is through the efFciency with which they are developed. In addition, we test the moderating effect of technological intensity level and Frm size on the efFciency–performance relationship. © 2013 Elsevier B.V. All rights reserved. 1. Introduction While most of the literature in the innovation Feld argues that technological innovations are central to business success, empirical results are inconclusive as they have reported positive, negative or no effects of innovations on Frm performance. We believe that this controversy might have its origins in the measurement of innova- tion. Thus far, it has variously been measured as innovation inputs ( O’Regan et al., 2006 ) or as innovation outputs ( Akgün et al., 2009 ). Additionally, there is a lack of agreement among authors about how to measure the effect of innovation on Frm performance. This paper differs from previous studies and proposes a new approach to measuring the effects of technological innovation q Claudio Cruz-Cázares and Teresa García-Marco thank the Spanish Ministry of Science and Technology for its Fnancial support granted through the project ECO2010-21393-C04-03. Claudio Cruz-Cázares thanks the University of Barcelona for the Fnancial support granted through the Research Project in Social Science, modality A (ref. 200033266) and to CONACyT (ref. 187799). Cristina Bayona-Sáez also thanks the Spanish Ministry of Science and Technology for its Fnancial sup- port obtained through the project ECO2010-21242-C03-03. Authors also thank the editor, Prof. Keun Lee, and the reviewers for their valuable comments and sug- gestions that have signiFcantly improved the quality of the paper. ±inally, authors acknowledge the guidance provided by Victor Gimenez, Pablo Arocena and Silverio Alarcón. Corresponding author. Tel.: +34 934 02 90 41; fax: +34 934 02 45 80. E-mail addresses: [email protected] (C. Cruz-Cázares), [email protected] (C. Bayona-Sáez), [email protected] (T. García-Marco). activities on Frm performance. Tidd and Bessant (2009) stress that innovation is a complex process and that it should be evaluated as such, not as a single input or output activity. Therefore, we defend the idea that innovation inputs produce innovation out- puts and the key to increasing Frm performance is the efFciency with which the technological innovation process is undertaken. Moreover, we argue that directly linking innovation inputs to Frm performance would generate misleading results since innovation inputs (e.g. R&D expenditure) could not improve Frm performance by themselves because they involve short-term costs and those investments that do not result in innovations are sunk costs that will not improve Frm performance ( Koellinger, 2008 ). ±inally, link- ing innovation outputs to Frm performance without considering the effort innovation inputs needed to achieve those innovation outputs leads to a skewed perspective. Based on the previous discussion, this paper aims to contribute to the innovation–performance literature by proposing a new approach to measuring the effect of the technological innovation process on Frm performance. Moreover, we assess the moder- ating effect of technological intensity level and Frm size on the relationship between technological innovation efFciency and Frm performance. The methodological strategy is executed in two stages. In the Frst stage, taking into account the causal and lagged effect of inno- vation inputs upon innovation outputs, we estimate technological innovation efFciency for each Frm based on an intertempo- ral output-oriented data envelopment analysis (DEA) bootstrap. 0048-7333/$ see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.respol.2013.03.012
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1240 C. Cruz-Cázares et al. / Research Policy 42 (2013) 1239– 1250 Looking for more robust results we estimate the global Malmquist index in order to observe the dynamics of the technological inno- vation efFciency. In the second stage, we take the estimated technological innovation efFciency as the explanatory variable of Frm performance through the estimation of a dynamic panel data model. To verify the consistency of our arguments, we also esti- mate two models that include innovation inputs and innovation outputs instead of technological innovation efFciency as explana- tory variables of Frm performance. In order to achieve the second objective, we also test for the moderating effect of technological intensity level and Frm size in these models. ±ew studies have endeavored to measure technological innova- tion efFciency and most of them have mixed innovation inputs or outputs beyond the innovation process ( Zhong et al., 2011 ) while others have disregarded the lag effect of R&D on innovation outputs ( Guan et al., 2006 ) or have used macro-level data ( Lee et al., 2010 ). Moreover, the linkage between technological innovation efFciency and Frm performance is almost non-existent in the literature. In this context, this paper contributes to the literature by estimating a technological innovation efFciency measure using only innova- tion inputs and outputs in the analysis, which allows an objective evaluation of the technological innovation process and by linking the estimated efFciency with Frm performance. In addition, the nature of our sample allows us to obtain more robust results since we are able to correct for endogeneity and autocorrelation at the second stage of the analysis. This paper proceeds as follows. Section two presents the the- oretical framework the hypotheses. Data and methods used are described in the third section. Results from the Frst- and second- stage estimations are shown in the fourth section, while the Ffth is reserved for discussion and conclusions. 2. Theoretical framework 2.1. Technological innovation efFciency concept When evaluating the performance implications of innovation activities, some studies have focused on the short-term direct effect of innovation inputs on Frm performance ( George et al., 2002 ), while others seek the long-term indirect effect through the inno- vations achieved ( Balkin et al., 2000 ) and a third strand disregards innovation inputs and links innovation outputs directly to Frm performance ( Weerawardena et al., 2006 ). ±urthermore, different types of innovation inputs have been used, such as R&D expendi- tures ( O’Regan et al., 2006 ), R&D intensity ( Hitt et al., 1997 ) and R&D manpower ( Wang and Huang, 2007 ), and a variety of innovation outputs like product innovations ( Li, 2000 ), process innovations ( Akgün et al., 2009 ) and patents ( Zahra and Nielsen, 2002 ). This use of a wide range of measurements and effects has led to results that are often inconclusive and ambiguous, highlighting the need for further examination of the innovation–performance relationship. In this study we propose a new approach to measuring the effects of technological innovation activities on Frm performance considering both innovation inputs and outputs. We argue that the new approach presented here overcomes some limitation of previous studies. 1 1 ±or example, linking innovation inputs to Frm performance could lead to mis- leading results, for various reasons: (a) R&D expenditure is a measure disconnected from the requirements of competitive advantages since it makes no reference to potential customer demand ( Liao and Rice, 2010 ) and (b) R&D activities cannot improve Frm performance by themselves, since they are simply an input that involves short-term costs and those investments that do not result in innovations are sunk costs that will not improve Frm performance ( Koellinger, 2008 ). Technological innovations are achieved through a long and complex process, involving the phases of searching for, selecting, implementing and capturing value ( Tidd and Bessant, 2009 ) and a realistic evaluation of the effects of technological innovation activi- ties on Frm performance should encompass the innovation process as a whole. We defend the idea that the key to increasing such performance is through the efFciency with which the innovation process is undertaken. The resource-based view (RBV) gives us sup- port for considering innovation as a process and for evaluating it from an efFciency perspective; RBV supports the concept of the transformation of Frm resources R&D into desirable outputs innovations through the use of the internal capabilities efF- ciency. ±urthermore, without these capabilities efFciency the mere possession of a large quantity of resources R&D does not guarantee the creation of a competitive advantage innovations or superior performance ( Song et al., 2007 ). Chiesa and ±rattini (2009 : 2) took the view that . . . a larger availability of higher level resources does not necessarily lead to superior performance in R&D”. As previously mentioned, we deFne technological inno- vation efFciency as the relative capability of a Frm to maximize innovation outputs given a certain quantity of innovation inputs. Measuring efFciency of innovation activities from the technical efFciency perspective ( ±arrell, 1957 ) is not new in the literature but the relevant empirical evidence is limited. In Table 1 we list some studies applying this efFciency measurement at a micro- and macro-level. China, Japan and Spain are the countries in which the micro-level analyses have been performed. Divergences can be observed in these studies as some included inputs and outputs beyond the technological innovation process (e.g. Guan et al., 2006; Hashimoto and Haneda, 2008 ) and some did not take into consid- eration the time lag required before R&D projects are completed and innovation outputs are achieved (e.g. Revilla et al., 2003; Díaz- Blateiro et al., 2006; Guan et al., 2006 ). ±inally, those papers which at a micro-level only considered inputs and outputs of the tech- nological innovation process and controlled for the lagged effects (e.g. Wang and Huang, 2007; Guan and Chen, 2010 ) did not link efFciency to Frm performance. As the discussion above indicates, this study makes three impor- tant contributions. ±irst, it estimates a technological innovation efFciency measure using only innovation inputs and outputs in the analysis and compares efFciency scores across industries. Second, it takes into consideration the lagged effects of innovation inputs in producing the desired outputs while estimating efFciency. ±inally, it links the efFciency of the technological innovation process to Frm performance. We expect that efFciency with which the technological process is achieved will produce a positive and signiFcant effect on Frm performance. In other words, Frms able to transform their limited innovation resources efFciently through the use of their internal capabilities into the desired innovation outputs will perform better. The conceptual framework for the research model is presented in ±ig. 1 . As mentioned earlier, in the Frst stage we estimate tech- nological innovation efFciency by means of an intertemporal DEA bootstrap and in the second stage we link the efFciency score obtained to Frm performance. In order to corroborate our hypothe- ses, we additionally link innovation inputs and outputs to Frm performance and expect a negative effect for the former and a positive effect for the latter. Based on the RBV, which argues that Frms might transform their resources in an efFcient way in order to achieve the needed out- puts to obtain a competitive advantage, we propose the following hypothesis. Hypothesis 1. Technological innovation efFciency will have a pos- itive effect on Frm performance.
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ISSN 1822-6515 ISSN 1822-6515 EKONOMIKA IR VADYBA: 2011. 16 ECONOMICS AND MANAGEMENT: 2011. 16 1323 MARKETING EFFECTIVENESS BY WAY OF METRICS Marek Solcansky 1 , Lucie Sychrova 2 , Frantisek Milichovsky 3 1 Brno University of Technology, Czech Republic, [email protected] 2 Brno University of Technology, Czech Republic, [email protected] 2 Brno University of Technology, Czech Republic, [email protected] Abstract Every company should be able to demonstrate own efficiency and effectiveness by used metrics or other processes and standards. Businesses may be missing a direct comparison with competitors in the industry, which is only possible using appropriately chosen instruments, whether financial or non-financial. The main purpose of this study is to describe and compare the approaches of the individual authors. to find metric from reviewed studies which organization use to measuring own marketing activities with following separating into financial metrics and non-financial metrics. The paper presents advance in useable metrics, especially financial and non-financial metrics. Selected studies, focusing on different branches and different metrics, were analyzed by the authors. The results of the study is describing relevant metrics to prove efficiency in varied types of organizations in connection with marketing effectiveness. The studies also outline the potential methods for further research focusing on the application of metrics in a diverse environment. The study contributes to a clearer idea of how to measure performance and effectiveness. Keywords: effectiveness, performance, financial metrics, non-financial metrics, benchmarking. JEL Classification: M39. Introduction To be an effective in all actions, also in marketing activities, it is one of fundamental interests of each company. For business competitiveness and sustainability of its successful functioning of the market, it is important to have appropriate metrics for measuring effectiveness. The issue of measuring the effectiveness of marketing activities between the professional and scientific community devoted considerable interest. Every company should be able to demonstrate own efficiency and effectiveness by used metrics or other processes and standards. Businesses may be missing a direct comparison with competitors in the industry, which is only possible using appropriately chosen instruments, whether financial or non-financial. In conclusion, this paper has focused on review of chosen articles which deal with kind of metrics, especially marketing metrics. It is remarkable that some authors aren’t considering the use of some metrics without possibility of their application in other areas of production. Therefore, it is important to do thorough research, in which there would be a definition of the metrics for measuring effectiveness of the company. The aim of this study is to describe and compare the approaches of the individual authors. The study contributes to a clearer idea of how to measure performance and effectiveness. The paper deals with marketing effectiveness, points to its importance for company activities and shows the most necessary methods for measurement of marketing effectiveness and suggests benefits. In general metrics are set of disposals that help companies quantify, compare and at last interpret own performance. In view on marketing field we can speak about marketing metrics. The metrics could be divided into two groups – financial metrics and non-financial metrics. Some companies use Marketing dashboard as the comprehensive set of important tools for internal and external synthesis and interpretation itself. For business competitiveness and sustainability of its successful functioning of the market, it is important to have appropriate metrics for measuring effectiveness. The issue of measuring the effectiveness of marketing activities between the professional and scientific community devoted considerable interest. Therefore it is necessary to determine the effectiveness of own marketing activities, it is appropriate to examine how the level of efficiency can be obtained. It was necessary to choose an approach to achieve the following objectives: 1. Review of relevant literature with a focus on metrics. 2. Distribution of the metrics used in the individual groups. 3. Conclusions based on our survey.
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ISSN 1822-6515 ISSN 1822-6515 EKONOMIKA IR VADYBA: 2011. 16 ECONOMICS AND MANAGEMENT: 2011. 16 1324 Introduction to marketing metrics In general metrics are set of disposals that help companies quantify, compare and at last interpret own performance. In view on marketing field we can speak about marketing metrics. The metrics could be devided into two groups – financial metrics and non-financial metrics (Kotler, Keller, 2007). Some companies use Marketing dashboard as the comprehensive set of important tools for internal and external synthesis and interpretation itself (Kotler, Keller, 2007). Financial metrics Financial metrics should be defined as kind of metrics where is possible to formulate exact amount of money. Authors Gaiardelli, Saccani and Songini (2007) used process-oriented metrics with the ability to distinguish features of the supply chain performance measurement – the SCOR model (supply chain operations reference), profitability ratios (ROE, ROI, ROS), MSI index (measures proportion between count of customers and totally number of potential customers), index BM (reports on cost, revenues and margins). Table 1. Financial metrics Source: own work Also Greenyer (2006) used ROI in his study. Furthermore, the independent variables are used (constructed 12 points, each representing a different marketing activities), the dependent variable (use of primary and secondary data – sales, profit, ROA). (O’Sullivan, Abela a Hutchinson, 2009). Authors Vardnyan and Tremblay (2006) used as one of metrics DEA (data envelopment analysis) and of course standard deviations of variables. Non-financial metrics Non-financial metrics can’t be defined in amount of money. They illustrate a comprehensive view of business. Authors Greiling (2006) and Town (2000) used compare studies in own articles. According to Zahay and Griffin (2010) the customer scale are not too strict like financial metrics. This customer scales are e.g. customer lifetime value, share of wallet, customer retention. Washburn and Plank (2002) used in their study testing of model Yoo and Donth in six instances, factor analysis with oblique rotation and the CFA model.
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Running head: METRICS 1 Metrics
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Metrics Metrics are significant limits that are used to measure quantifiable evaluation. They are
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