Performance management in collaborative networks: difficulties and barriers

[EN] Global competitiveness obliges to enterprises to collaborate in many processes such as new product and services development in order to shorten the lifecycle, development and commercialization. Therefore, the competence has drifted from an individual focus to a supply chain management one and, from some years, to a collaborative enterprises network approach. It is common to find frameworks for measuring/managing the performance within extended enterprises, supply chains, virtual enterprises, etc. However, few authors deal with a higher level: the collaborative networks one. This concept of enterprises management set up bigger difficulties regarding not only from a conceptual and structural point of view but also considering both the design and posterior development of systems capable of managing the performance achieved in this type of organizations. This work describes both the main difficulties and barriers when trying to apply performance management concepts to collaborative networks. In this sense, it is highlighted the weaknesses of the existing intra-organizational frameworks that cannot be projected, as they are conceived, to manage performance within collaborative networks.Alfaro Saiz, JJ.; Rodríguez Rodríguez, R.; Verdecho Sáez, MJ. (2011). Performance management in collaborative networks: difficulties and barriers. IFIP Advances in Information and Communication Technology. 362:133-139. doi:10.1007/978-3-642-23330-2_15S133139362Hausman, W.H.: Supply chain performance metrics. The practice of supply chain management: Where theory and application converge. Kluwer Academic Publishers, Dordrecht (2003)Coughlan, P., Coughlan, D.: Action research: action research for operations management. International Journal of Operation and Productions Management 22(2), 220–240 (2002)Kaplan, R.S., Norton, D.P.: The balanced scorecard. Measures that drive performance. Harvard Business Review, 71–79 (January/February 1992)Bourne, M.: Designing and implementing a balanced performance measurement system. Control - Official Journal of the Institute of Operations Management, 21–24 (July/August 1999)Neely, A., Adams, C.: Perspectives on Performance. The Performance Prism’ Web Site of Neely A (2001), www.som.cranfield.ac.uk/som/cbp/adn.htmHronec, S.M.: Vital Signs. Amacom, New York (1993)Bititci, U.S., Mendibil, K., Martinez, V., Albores, P.: Measuring and managing performance in extended enterprises. International Journal of Operations & Production Management 25(4), 333–353 (2005)Folan, P., Browne, J.: Development of an extended enterprise performance measurement system”. Production Planning & Control 16(6), 531–544 (2005)Gaiardelli, P., Saccani, N., Songini, L.: Performance measurement systems in the after-sales service: an integrated framework. International Journal of Business Performance Management 9(2), 145–171 (2007)Alfaro, J.J., Ortiz, A., Rodríguez, R.: Performance measurement system for Enterprise Networks. International Journal of Productivity and Performance Management 56(4), 305–334 (2007)Romero, D., Galeano, N., Molina, A.: A conceptual Model for Virtual Breeding Environments Value System. In: Camarinha-Matos, L., Afsarmanesh, H., Novais, P., Analide, C. (eds.) Establishing the Foundation of Collaborative Networks. Springer, Heidelberg (2007)Msanjila, S.S., Afsarmanesh, H.: Trust analysis and assessment in virtual organization breeding environments. International Journal of Production Research 46(5), 1253–1295 (2008)Bititci, U., Turner, T., Mackay, D., Kearney, D., Parung, J., Walters, D.: Managing synergy in collaborative enterprises. Production Planning & Control 18(6), 454–465 (2007)Chalmeta, R., Grangel, R.: Performance Measurement Systems for Virtual Enterprise Integration. International Journal of Computer Integrated Manufacturing 18(1), 73–84 (2005)Francisco, R.D., Azevedo, A.: Dynamic Performance Management In Business Networks Environment. In: Digital Enterprise Technology. Springer, US (2007)Busi, M., Bititci, U.S.: Collaborative performance management: Present gaps and future research. International Journal of Productivity and Performance Management 55(1), 7–25 (2006)Rodriguez, R., Ortiz, A., Alfaro, J.: Fostering collaborative meta-value chain practices. International Journal of Computer Integrated Manufacturing 22(5), 385–394 (2009)Rodriguez, R.R., Gomez, P., Franco, D., Ortiz, A.: Establishing and keeping inter-organisational collaboration: Some lessons learned. International Federation for Information Processing 1, 214–222 (2007)Leseure, M., Shaw, N., Chapman, G.: Performance measurement in organisational networks: an exploratory case study. International Journal of Business Performance Management 3(1), 30–46 (2001

Understanding user experience of mobile video: Framework, measurement, and optimization

Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the user’s interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining users’ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account user’s needs and desires when using the service, emphasizing the user’s overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study

Driving continuous improvement

The quality of improvement depends on the quality of leading and lagging performance indicators. For this reason, several tools, such as process mapping, cause and effect analysis and FMEA, need to be used in an integrated way with performance measurement models, such as balanced scorecard, integrated performance measurement system, performance prism and so on. However, in our experience, this alone is not quite enough due to the amount of effort required to monitor performance indicators at operational levels. The authors find that IT support is key to the successful implementation of performance measurement-driven continuous improvement schemes

Qualitative Case Studies in Operations Management: Trends, Research Outcomes, And Future Research Implications

Our study examines the state of qualitative case studies in operations management. Five main operations management journals are included for their impact on the field. They are in alphabetical order: Decision Sciences, International Journal of Operations and Production Management, Journal of Operations Management, Management Science, and Production and Operations Management. The qualitative case studies chosen were published between 1992 and 2007. With an increasing trend toward using more qualitative case studies, there have been meaningful and significant contributions to the field of operations management, especially in the area of theory building. However, in many of the qualitative case studies we reviewed, sufficient details in research design, data collection, and data analysis were missing. For instance, there are studies that do not offer sampling logic or a description of the analysis through which research out-comes are drawn. Further, research protocols for doing inductive case studies are much better developed compared to the research protocols for doing deductive case studies. Consequently, there is a lack of consistency in the way the case method has been applied. As qualitative researchers, we offer suggestions on how we can improve on what we have done and elevate the level of rigor and consistency

Developing a conceptual model for the internal data source to measure customer satisfaction

Traditional CSM approach is performed at certain frequencies. The gap between such events can be termed as a ‘blind period’, because customer satisfaction is left unobserved and unmanaged. The blind period may sometimes accelerate the growth of customer dissatisfaction. One way to eliminate the impact of the blind period is to reduce the gap between CSM events. The initial assessment indicates that conducting CSM more frequently, may weaken the accuracy of measurement, and increase the cost of the programme. The authors believe that the reason behind these limitations is the use of the external data source, collecting data directly from customer, therefore suggests using the internal data source, as an alternative to measure customer satisfaction. The purpose of this paper is to develop a conceptual model for the internal data source to measure customer satisfaction. To achieve this objective, a conceptual model need to be developed based on three determined steps: define the formation of customer satisfaction value, identify the CSM factors and dimensions, and mirror the CSM instruments to identify the internal performance values. The paper indicates that internal data source could provide researchers with an alternative data source to measure customer satisfaction with minimum limitations on frequency of implementation, accuracy and cost

The economics of accreditation

This paper is a report on a research project on the economics of accreditation in the UK. The main motivation and objective for the study is to have available a detailed analysis of how the accreditation system impinges on important aspects of economic life, such as innovation and business and economic performance. It aims to improve the general understanding of the benefits of using accredited conformity assessment and to help businesses make informed decisions when procuring conformity assessment and related services. It is also intended to be helpful to government by supporting evidence-based policy making in relation to accreditation and conformity assessmen