11 research outputs found
Interview with Adner Batts
No full textMaster Sergeant Adner Batts, a native of Edgecombe in rural eastern North Carolina, made the Marine Corps a career. He entered the Corps in 1948 and served as a cook in the Korean War. After duty in the Mediterranean and at Montford Point, he served two tours in Vietnam, providing logistical support to a Marine engineering unit and saw action at Khe Sahn. Retired, he resides in Jacksonville, North Carolina
Old technology responses to new technology threats: demand heterogeneity and technology retreats
No full textWe explore the implications of a real and common alternative to attempting the transformation required to embrace a new, dominant, technology--the choice to maintain focus on the old technology. In considering this choice, we distinguish between "racing" strategies, which attempt to fight off the rise of the new technology by extending the performance of the old technology, and "retreat" strategies, which attempt to accommodate the rise of the new technology by repositioning the old technology in the demand environment. Underlying our arguments is the observation that the emergence of a new technology does more than just create a substitute threat--it can also reveal significant underlying heterogeneity in the old technology's broader demand environment. This heterogeneity is a source of opportunities that can support a new position for the old technology, in either the current market or a new one. Using this lens, we explore the decision to stay with the old technology as a rational, proactive choice rather than as a mark of managerial and organizational failure. We then consider the distinctive challenges and organizational dynamics that arise in technology retreats, and their implications for the ways in which managers and scholars should approach questions regarding the management of capabilities, lifecycles, and ecosystems. Copyright 2010 The Author 2010. Published by Oxford University Press on behalf of Associazione ICC. All rights reserved., Oxford University Press.
Competitive advantage as a legitimacy-creating process
No full textPurpose – The purpose of this paper is to explore how small firms in the tattooing industry actively shape institutional expectations of value for consumers in a changing industry. Design/methodology/approach – The paper draws upon interviews with key actors in the firms under study to explore their experiences with consumers and other constituents in determining how competitive advantage is constructed in this environment. These data are complemented data with interviews with governmental representatives and material from secondary sources.
Findings – The results reveal efforts of firms to construct and increase organizational legitimacy through the prominence of discourses of professionalism based on artistry and medicine/public health. These bases of competitive differentiation are not the clear result of exogenous pressure, rather they arise through the active efforts of the firm to construct value guidelines for consumers and other constituents. Practical implications – Strategic management in small firms is a complex and dynamic process that does not necessarily mirror that of large organizations. Constructing competitive advantage is an interacting process between key actors of small firms and various constituents.
Originality/value – The paper extends the application of institutional theory in strategic management by illuminating the active role that firms play in creating industry norms, especially in industries where norms are not well established or no longer entrenched. Moreover, exploring an alternative site of study offers a means through which to see well-studied issues in new ways
The Impact of Causal Ambiguity on Competitive Advantage and Rent Appropriation
No full textWe seek to develop the conceptual and practical understanding of causal ambiguity. Specifically we extend current thinking by setting out three types of causal ambiguity, based on whether firm resources are perceived to display linkage and/or characteristic ambiguity, and by examining for each type the impact of causal ambiguity on the sustainability of competitive advantage and on rent appropriation. We highlight the difficulties decision-makers face when they perceive ambiguity and finally we explore some implications of ambiguity with respect to resource-creation processes
The emergence of dynamic capabilities in SMEs: A critical realist study
No full textHow do dynamic capabilities develop in small firms? More specifically, what mechanisms account for the emergence of dynamic capabilities in SMEs? The dynamic capabilities perspective synthesises evolutionary theory, the resource based view of the firm and organisational learning to explain how firms sustain competitive advantage. However, the literature tends to focus on larger firms and assumes the existence of routines and processes, particularly those needed to assimilate new knowledge. The manner in which dynamic capabilities evolve may be different in the small firm context as routines and processes associated with seeking out and assimilating new knowledge will differ from those of large firms.
This research draws on a single case study using a critical realism perspective to study the emergence of dynamic capabilities. The case firm is a small Irish owned print firm that has evolved into an international brand and artwork management business. The study draws on interviews, company records and documents, and notes from 186 meetings between the author and the CEO over a seven year period. The case data is analysed using the framework of critical realism’s ontological strata of the Empirical, the Actual and the Real to interpret the data captured and to explore the underlying mechanisms present in order to provide defensible explanations for the phenomenon observed.
Analysis of the case data suggests that dynamic capabilities developed as a consequence of (i) the focus on firm performance, (ii) capacity building (people and technology), (iii) the evolution of higher order learning, (iv) managerial purposefulness, and (v) the use of third parties. Adopting a critical realist perspective suggests that dynamic capabilities emerged as a result of the interaction of the CEO’s capacity to engage in higher order learning and the CEO’s knowledge and networks as they relate to the business that allow the CEO identify, assimilate and exploit new knowledge. Contributions include a description and explanation of how dynamic capabilities emerged in an SME; the application of the dynamic capabilities perspective to the small firm context; and the use of the critical realist perspective to study dynamic capabilities in the context of small firms
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The process of developing innovative capabilities in biotechnology: the case of UK firms
No full textThe advent of born-global bio-tech firms signal the genesis of a new business model that is emerging in the biotechnology sector. Born globals are small firms whose knowledge supply-chain includes global resources from multiple countries. Their innovation ‘ecosystems’ consists of experienced scientists, science parks, academics, well-established bio-pharmaceutical firms and government institutions. The firms plan their business based on global strategic perspectives and this significantly increases their productivity and innovativeness. But surprisingly, little is known about their capability development processes in the specialised networks of the biotechnology sector. As a result, this study explores the connectivity of various elements, within their knowledge supply-chain, and how they influence their capacity to generate new scientific knowledge and technical know-how. The study employs a multi-case approach. It examines five cases of bio-tech firms from the East Midlands region of the United Kingdom which have an entrepreneurial flair synonymous with born-global firms. The findings from within and across cases, secondary data analysis and results from a ‘pilot study’ led to the construction of a new conceptual framework of knowledge and innovative capability development. The model is created from the ideas of Freeman and others and it contributes to an understanding of the concepts of dynamic capabilities and network theories
Value-Based Working Capital Management Determining Liquid Asset Levels in Entrepreneurial Environments
No full textCollected data and research material presented in the monograph are a result of financing of the Polish science budget in the years 2011−14; the research project was financed by the National Science Centre according to decision no. DEC-2011/01/B/HS4/04744.
The project that resulted in this monograph was financed from public funds for education for 2011 − 2014, the National Science Center under Contract No. DEC-2011/01/B/HS4/04744.Value-Based Working Capital Management analyzes the causes and effects of improper cash flow management between entrepreneurial organizations with varying levels of risk. This work looks at the motives and criteria for decision-making by entrepreneurs in their efforts to protect the financial security of their businesses and manage financial liquidity. Michalski argues that businesses exposed to greater risk need a different approach to managing liquidity levels. The scientific aim of this monograph is to present the essence of financial liquidity management under specific conditions faced by enterprises with risk and uncertainty. Enterprises differ from one another in risk sensitivity. This difference affects the area of taking decisions by the managers of those enterprises. The result of interactions between levels of liquidity and sensitivity to risk affects the managers of such enterprises (Altman 1984; Tobin 1958; Back 2001; Tobin 1969). In this monograph the research hypothesis is the claim that enterprises with a higher sensitivity to risk are very different from enterprises with a lower sensitivity to risk, resulting in a different approach to managing their working capital. Enterprise managing teams react to risk, and this reaction is adjusted by an enterprise’s sensitivity to risk. Because of its subject area, the book will address the issues of corporate finance. The monograph discusses the behavior of enterprises and the relationships between them and other factors in the market occurring in the management process under the conditions of limited resources. As a result of these interactions with the market and the environment in which individuals who manage enterprises operate, there is an interaction between money and real processes that in the end are the cornerstone of wealth building. This chapter discusses the objectives and nature of enterprises in the context of their risk sensitivity, as well as the relationships between the objectives of enterprises and the characteristic features of their businesses. Enterprises operate in various business environments, but generally speaking, they all have one main aim: wealth creation for their owners. The realization of that aim depends on an idea of business in which the enterprise is an instrument to collect money from clients of the enterprise’s services and products. Business environment is crucial not only for future enterprise cash inflows from the market but also for risk and uncertainty (Asch, and Kaye 1997; Copeland, and Weston 1988; Fazzari, and Petersen 1993). According to the author, it is necessary to include an understanding of that risk and uncertainty of future in the rate that reduces the net size of free cash flows for the enterprise owners, beneficiaries, or more generally stakeholders. Enterprise value creation is the main financial aim of the firm in relation to working capital components (Graber 1948; Jensen, and Meckling 1976; Lazaridis, and Trifonidis 2006). Working capital management is a part of a general enterprise strategy to its value maximization (Laffer 1970; Kieschnick, Laplante, and Moussawi 2009; Lyland, and Pyle 1977). This chapter presents a definition of financial liquidity and liquidity-level measurements. This chapter contains four subchapters that address the specific role of short-term financial decisions, a classification of definitions of financial liquidity, sources of information about liquidity level, and liquidity-level measurements (Lazaridis and Tryfonidis 2006; Long, Malitz, and Ravid 1993; Kieschnick, Laplante, and Moussawi 2009). Financial liquidity definition and liquidity-level measurements Here we have an opportunity to present the author’s opinion on what assets should be financed with short-term funds and what the level of liquidity is in an enterprise (Michalski 2012a). The discussion also pertains to the issue of the dividing line between long-term and short-term decisions, with greater emphasis on the durability of their effects, rather than the decision-making speed. This section also attempts to answer the question: What are the short-term effects of operations under conditions of uncertainty and risk? The reason for the considerations in this section is the need to characterize the decisions that affect the level of enterprise liquidity. The research hypothesis of this monograph assumes that differences between more risk sensitive and less risk sensitive enterprises are seen in liquidity management. Simply because the enterprises, during financial liquidity management, take into account the differences in their risk sensitivity. This chapter discusses the relationship between firm value and business risk sensitivity. The chapter starts with a presentation of intrinsic liquidity value and firm reactions to market liquidity value. This is the basis for target liquidity level in the enterprise. Liquid assets are the main part of working capital assets, so the next part of the chapter focuses on working capital investment strategies and strategies of financing such investments in working capital in the context of firm value creation. The chapter concludes that, from a firm-value-creation point of view, more risk-sensitive entities should use flexible-conservative strategies, while less risk-sensitive entities have the freedom to use restrictive-aggressive strategies. In the context of a crisis, this is the clear answer and explanation for higher levels of working capital investments observed empirically during and after a crisis. The determinants of intrinsic value of liquidity are attributed to liquidity by enterprise management. Enterprises in which financial liquidity has a high internal value will have a tendency to maintain reasonable liquid resource assets at a higher level. The levels of stocks of funds maintained by enterprises are also the result of the relationship between the liquidity market value and the intrinsic value of liquidity. It demonstrates how to approach the estimation of liquidity and presents the market value of liquidity. Having connected this information with the knowledge of manifestations of the internal liquidity, we can offer an explanation as to why the target (and also probably the optimal) level of liquidity for enterprises with higher-than-average risk sensitivity is at a higher level than the corresponding target (optimal) level for enterprises with a lower level of risk sensitivity. Working capital value-based management models In this part of the monograph we discuss the items contained within the cost of maintaining inventory. Using this approach, a model of managing inventories is presented. Theoretically, the value-maximizing optimal level of inventory is determined to be the modified EOQ model, presented as VBEOQ model. We also present an outline of issues associated with the risk of inventory management and its impact on the value of the enterprise for its owner. We also discuss the principle of the optimal batch production model and how the size of the production batch affects the value of the enterprise for its owner. Here also is demonstrated a modification of the POQ model: VBPOQ. The proposed modification takes into account the rate of the cost of capital financing and the measures involved in inventory when determining the optimal batch production. When managing the commitment of the inventory, it is crucial to take into account the impact of such decisions on the long-term effectiveness of the enterprise. This chapter also discusses the relationships between the management of accounts receivables and the value of a business. A modified (considering the value of a business) model of incremental analysis of receivables is presented, as is a discussion of the importance of capacity utilization by an enterprise for making management decisions pertaining to accounts receivables. Issues related to the management of working capital and enterprise liquidity are and will be an area of research. The analysis in this study focused primarily on working capital and liquidity management; understanding its specifics will facilitate the management of liquidity in any type of organization. Working capital as a specific buffer against risk has its special role during a crisis and can serve as a good forecasting indicator about future economic problems in the economy if a whole business environment notices higher levels of working capital and its components, like cash, inventories, and accounts receivables. The scientific value of the issues discussed in the book is associated with the issue of working capital and liquidity management in enterprises. It is also a result of the exploration and definition of the main financial objective of businesses and the relationship between the objective and the management of working capital and enterprise liquidity. The choice of topic and the contents of research resulted also from empirical observation. Empirical data on enterprises that operate in countries touched by the last crisis document higher-than-average levels of working capital before, during, and after the crisis in these enterprises. These conditions provided the means for a “natural experiment” of sorts. From that point, working capital management theory faced a necessity of even wider development.Collected data and research material presented in the monograph are a result of financing of the Polish science budget in the years 2011−14; the research project was financed by the National Science Centre according to decision no. DEC-2011/01/B/HS4/04744. The project that resulted in this monograph was financed from public funds for education for 2011 − 2014, the National Science Center under Contract No. DEC-2011/01/B/HS4/04744.How to Cite this Book Harvard Grzegorz Michalski . (April 2014). Value-Based Working Capital Management . [Online] Available at: http://www.palgraveconnect.com/pc/doifinder/10.1057/9781137391834. (Accessed: 28 May 2014). APA Grzegorz Michalski . (April 2014). Value-Based Working Capital Management . Retrieved from http://www.palgraveconnect.com/pc/doifinder/10.1057/9781137391834 MLA Grzegorz Michalski . Value-Based Working Capital Management . (April 2014) Palgrave Macmillan. 28 May 2014. Vancouver Grzegorz Michalski . Value-Based Working Capital Management [internet]. New York: Palgrave Macmillan; April 2014. [cited 2014 May 28]. Available from: http://www.palgraveconnect.com/pc/doifinder/10.1057/9781137391834 OSCOLA Grzegorz Michalski , Value-Based Working Capital Management , Palgrave Macmillan April 2014Author Biography Grzegorz Michalski is Assistant Professor of Corporate Finance at the Wroclaw University of Economics, Poland. His main areas of research are Business Finance and Financial Liquidity Management. He is currently studying the liquidity decisions made by organizations. He is the author or co-author of over 80 papers and 10 books, and sits on the editorial board of international conferences and journals. Reviews 'Due to the recent financial crisis, interest in the topic of working capital has grown significantly to both theory and practice. The research results presented by Grzegorz Michalski contribute to the development of a comprehensive theory of liquidity management and the creation of an integrated working capital and liquidity for different types of business model. The job is processed on a high quality level." -Marek Panfil, Ph.D, Director of Business Valuation Department Warsaw School of Economics 'The book of Grzegorz Michalski is a very good publication that has found the right balance between theory and practical aspects of financial liquidity management. It is extremely timely and valuable, and should be required reading for all corporate finance practitioners, academicians, and students of finance. Value-Based Working Capital Management is comprehensive, highly readable publication, and replete with useful practical examples. It has also enabled corporate leaders to make better-informed decisions in their efforts to protect the financial security of their businesses and manage financial liquidity.' -Petr Polak, Author of Centralization of Treasury Management, and Associate Professor of Finance, University of Brunei DarussalamREFERENCES Introduction Adner, R., and D. A. Levinthal (2004). “What Is Not a Real Option: Considering Boundaries for the Application of Real Options to Business Strategy.” Academy of Management Review 29(1). Altman, E. (1984). “A Further Empirical Investigation of the Bankruptcy Cost Question.” Journal of Finance 39. Back, P. (2001). “Testing Liquidity Measures as Bankruptcy Prediction Variables.” Liiketaloudellinen Aikakauskirja—The Finnish Journal of Business Economics 2001(3). Baker, M., and J. Wurgler (2002). “Market Timing and Capital Structure.” Journal of Finance 57. Ben-Horim, M., and H. Levy (1982). “Inflation and the Trade Credit Theory Period.” Management Science 28(6), pp. 646–51. Blaug, M. (1985). Economic Theory in Retrospect. Cambridge University Press: Cambridge. Charnes, A., W. Cooper, and E. Rhodes (1978). “Measuring the Efficiency of Decision Making Units.” European Journal of Operational Research 2. Cokins, G. (2004). Performance Management: Finding the Missing Pieces to Close the Intelligence Gap. John Wiley & Sons: Hoboken, NJ. Dluhosova, D. (2006). Financial management of firms (in Czech: Financni rizeni a rozhodovani podniku). Ekopress: Prague. Emery, G. W. (1987). “An Optimal Financial Approach to Variable Demand.” Journal of Financial and Quantitative Analysis 22(2), pp. 209–25. Falope, O. I., and O. T. Ajilore (2009). “Working Capital Management and Corporate Profitability: Evidence from Panel Data Analysis of Selected Quoted Companies in Nigeria.” Research Journal of Business Management 3, pp. 73–84. Hill, M. D., G. W. Kelly, and M. J. Highfield (2010). “Net Operating Working Capital Behavior: A First Look.” Financial Management 39(2), pp. 783–805. Hirshleifer, J. (1958). “On the Theory of the Optimal Investment Decision.” Journal of Political Economy 66. Huyghebaert, N. (2006). “On the Determinants and Dynamics of Trade Credit Use: Empirical Evidence from Business Start-Ups.” Journal of Business Finance & Accounting 33(1). Ijiri, Y. (1978). “Cash-Flow Accounting and Its Structure.” Journal of Accounting, Auditing and Finance 1(4). Kaura, M. (2002). Management Control and Reporting Systems: Harmonising Design and Implementation. SAGE Publications, Response Books: New Delhi. Khoury, N., K. Smith, and P. MacKay (1999). “Comparing Current Assets Practices in Canada, the United States and Australia.” Revue Canadienne des Sciences de l’Administration 16(1), pp. 53–57. Lumby, S. (1993). Investment Appraisal and Financing Decisions. The Chapman & Hall Series in Accounting and Finance. Routledge Chapman & Hall: London. Mallik, A.K., and D. Sur (1998). “Working Capital and Profitability: A Case Study in Interrelation.” The Management Accountant 33(11), pp. 805–9. Mallik, A.K., D. Sur, and D. Rakshit (2005). “Working Capital and Profitability: A Study on Their Relationship with Reference to Selected Companies in Indian Pharmaceutical Industry.” GITAM Journal of Management 3, pp. 51–62. Markides, C. (1997). “Strategic Innovation.” MIT Sloan Management Review 39(3). Masulis, R. (1980). “The Impact of Capital Structure Change on Firm Value: Some Estimates.” Journal of Finance 38(1). Myers, S. (1984). “The Capital Structure Puzzle.” Journal of Finance 39(3), Available at MIT: http://dspace.mit.edu/bitstream/handle/1721.1/2078/SWP-1548-15376697.pdf (date of access: January 10, 2014). Myers, S., and N. Majluf (1984). “Corporate Financing and Investment Decisions When Firms Have Information Investors Do Not Have.” Journal of Financial Economics 13(2) Available at MIT: http://dspace.mit.edu/bitstream/handle/1721.1/2068/SWP-1523-15376412.pdf (date of access: January 10, 2014). Myers, S. C. (1984). “The Capital Structure Puzzle.” Journal of Finance 3. Peel, M. J., N. Wilson, and C. Howorth (2000). “Late Payment and Credit Management in the Small Firm Sector: Some Empirical Evidence.” International Journal of Small Business 18(2). Petersen, M. A., and R. G. Rajan (1997). “Trade Credit: Theories and Evidence.” The Review of Financial Studies 10(3), pp. 661–91. Pike, R., and B. Neale (1999). Corporate Finance and Investment: Decisions and Strategies. Prentice Hall: London. Pratt, S., and A. Niculita (2008). Valuing a Business: The Analysis and Appraisal of Closely Held Companies. McGraw-Hill: New York. Preve, L. A., and V. Sarria-Allende (2010). Working Capital Management, Financial Management Association Survey and Synthesis Series. Oxford University Press: New York. Robichek, A. A. (1975). “Interpreting the Results of Risk Analysis.” Journal of Finance 30(5), pp. 1384–86. Ruback, S. C. (2002). “Capital Cash Flows: A Simple Approach to Valuing Risky Cash Flows.” Financial Management 31(2). Scherr, F. C. (1989). Modern Working Capital Management. Text and Cases. Prentice Hall: Englewood Cliffs, NJ. Smith, J. E., and K. F. McCardle (1998). “Valuing Oil Properties: Integrating Option Pricing and Decision Analysis Approaches.” Operations Research 46(2). Soltes, V. (2012). “Paradigms of Changes in the 21st Century: Quest for Configurations in Mosaic.” Ekonomicky Casopis 60(4). Soufani, K. (2002). “On the Determinants of Factoring as a Financing Choice: Evidence from the UK.” Journal of Economics and Business 54. Stewart, G. (1991). The Quest for Value. HarperCollins: New York. Summers, B., and N. Wilson (2000). “Trade Credit Management and the Decision to Use Factoring: An Empirical Study.” Journal of Business Finance & Accounting 27(1). Tobin, J. (1958). “Liquidity Preference as Behavior Towards Risk.” Review of Economic Studies 67, pp. 65–86. Tobin, J. (1969). “A General Equilibrium Approach to Monetary Theory.” Journal of Money Credit and Banking 1(1). Washam, J., and D. Davis (1998). “Evaluating Corporate Liquidity.” TMA Journal 18(2). Williamson, O. (1988). “Corporate Finance and Corporate Governance.” Journal of Finance 43(3). Wilner, B. (2000). “The Exploitation of Relationships in Financial Distress: The Case of Trade Credit.” Journal of Finance 55, pp. 153–78. Wojciechowska, U. (2001). Liquidity of Polish companies in transition economy: Microeconomic and macroeconomic aspects (in Polish: Plynnosc finansowa polskich przedsiebiorstw w okresie transformacji gospodarki). SGH: Warsaw. Zardkoohi, A. (2004). “Do Real Options Lead to Escalation of Commitment?” Academy of Management Science 29(1). Zmeskal, Z., and D. Dluhosova (2009). “Company Financial Performance Prediction on Economic Value Added Measure by Simulation Methodology.” Proceedings of the 27th International Conference on Mathematical Methods in Economics, Mathematical Methods in Economics; H. Brožová, R. Kvasnička eds., Czech University of Life Sciences, Chapter 1. Working Capital Management in the Business Context Adner, R., and D. A. Levinthal (2004). “What Is Not a Real Option: Considering Boundaries for the Application of Real Options to Business Strategy.” Academy of Management Review 29(1). Amram, M., and N. Kulatilaka (1999). Real Options: Managing Strategic Investment in an Uncertain World. Harvard Business School Press: Boston. Asch, D., and G. R. Kaye (1997). Financial Planning: Profit Improvement through Modelling. Kogan Page: London. Bagchi, B. and B. Khamrui (2012). “Relationship between Working Capital Management and Profitability: A Study of Selected FMCG Companies in India.” Business and Economics Journal 60. Banos-Caballero, S., P. J. García-Teruel, and P. Martinez-Solano (2010). “Working Capital Management in Enterprises.” Accounting & Finance 50(3), pp. 511–27. Baxter, M., and A. Rennie (1996). Financial Calculus: An Introduction to Derivative Pricing. Cambridge University Press: Cambridge. Baz, J., and G. Chacko (2004). Financial Derivatives: Pricing, Applications and Mathematics. Cambridge University Press: Cambridge. Beck, P. E., and D. R. Stockman (2005). “Money as Real Options in a Cash-in-Advance Economy.” Economics Letters 87. Ben-Horim, M., and H. Levy (1982). “Inflation and the Trade Credit Theory Period.” Management Science 28(6), pp. 646–51. Berger, P. G., E. Ofek, and I. Swary (1996). “Investor Valuation of the Abandonment Option.” Journal of Financial Economics 42(2). Black, F., and M. Scholes (1973). “The Pricing of Options and Corporate Liabilities.” Journal of Political Economy 81. Brasch, J. J. (1972). “The Role of Trade Credit in Economic Development.” Nebraska Journal of Economics and Business 11(1), pp. 63–67. Brealey, R. A., and S. C. Myers (1999). Basics of enterprises finance (Polish edition: Podstawy finansow przedsiebiorstw). WN PWN: Warszawa. Brennan, M. J., and E. S. Schwartz (1985). “Evaluating Natural Resource Investments.” Journal of Business 58(2). Cassimon, D., and P. J. Engelen (2003). “The New Frontiers of Corporate Finance.” Global Business Review 5(1). Cokins, G. (2004). Performance Management: Finding the Missing Pieces to Close the Intelligence Gap. John Wiley & Sons: Hoboken, NJ. Copeland, T. E., and V. Antikarov (2001). Real Options: A Practitioner’s Guide. B
Dynamic utilisation of knowledge in decision making
No full textThe contribution of this research is a set of novel insights on the interplay of knowledge assets during decision making. Knowledge is conceptualised as a dynamic resource. Its value is a function of the contribution it makes relative to other knowledge stock at a point of application. The information flows can renew the resource, and the influence of power makes the knowledge development process subject to reconciliation of local interests. The focus on dynamics of knowledge development through a set of value-adding
processes also moves the analysis away from the rational and political perspectives of knowledge in decision making. This offers an alternative view on how the value of knowledge can be assessed and understood. The research proposes how the decision making process could be a useful mechanism for the development of dynamic capabilities. The findings stem from the view of knowledge developed in this research as a knowledge
capsule comprised of two or three knowledge assets which can draw upon two other types of knowledge from outside the decision process. The analysis relies on two in-depth strategic decision case studies and suggests that the value of knowledge can be identified from the point at which the central decision is generated. The value of each interplay can be assessed in a 'transactional' space where three types of knowledge 'meet'. Decision making is a knowledge-creating activity. The interplay of knowledge assets is a source of value in decision making and, this thesis argues, the basis of heterogeneity of this strategic asset between organisations. Power
impacts the contribution of knowledge assets and through application is redistributed during the decision making process. As knowledge stocks interplay, some knowledge is attuning and some advancing the work in decision episodes. The value can be identified by assessing the outcomes of interplay such as insights and decisions. The managerial implications focus on the challenges for developing knowledge assets and the extraction of value from existing knowledge assets during strategic decision making
Inovação colaborativa: uma perspectiva tecnológica
No full textCollaborative innovation become one of the most strategy decision across firms and a well-defined phenomenon that became popular among practitioners and researchers (A. S. Cui & O’Connor, 2012; Liu et al., 2017). Many theories were considered to explain collaboration phenomena such as resources-based view, organization theory, strategy, information processing theory, the economic theory of complementarities among others (Barney, 1991; Cassiman & Veugelers, 2006; Daft & Lengel, 1986; Milgrom & Roberts, 1995; Tushman & Nadler, 1978). However, technology advances provide new variations in collaboration to innovativeness. For example, collaborative activities with suppliers and customers (Karhade & Dong, 2021), community source projects (Liu et al., 2017) or collaboration with distant partners (T. Cui et al., 2020), corporate engagement with startups (Shankar & Shepherd, 2019), innovation networks (Aarikka-Stenroos et al., 2017), and innovation ecosystems (Granstrand & Holgersson, 2020).Collaborative innovation takes over the existence of an inter-organizational activities executed by people that together perform with high level of interdependence something innovative (T. Cui et al., 2020; Davis & Eisenhardt, 2011). Some authors (Adner & Kapoor, 2010; T. Cui et al., 2020; Rico et al., 2008) highlight that this interdependence is characterized along two dimensions: technological and behavioral. Technological interdependence is linked to knowledge and the exchange of resources for research and development, and behavioral interdependence is associated with the field of communication, social interaction between collaborative actors and the coordination of these relationships to innovate.Other perspectives in the literature explain and theorize about collaborative innovation as knowledge-sharing trajectories (Majchrzak & Malhotra, 2016; Trkman & Desouza, 2012), or multi-actor collaboration (Torfing, 2019), or building collaborative capabilities (Swink, 2006) among other approaches. In this editorial, we bring some thoughts and idea about collaborative innovation under a technological perspective to incentive researchers to go beyond in innovative technologies research embedded in collaboration.Collaboration efforts also became a common way of firms to enhance innovations and its technological development with clear determinants about their beneficial effects, and therefore, the literature is well stablished in this subject (Pereira et al., 2018). However, collaboration only succeeds when technological resources and capabilities are combined, and parties define jointly how to enhance and use them accordingly (Snow, 2015).Collaborative innovation as a new technological paradigm refers to a network innovation model supported by interactions of multiple parties such as enterprises, universities and research institutions as core elements and government, financial institutions, nonprofit organizations, intermediaries as auxiliary elements (W. Zhang et al., 2021). Notwithstanding, collaboration networks operating in different organizational levels are present in various patterns and characteristics of evolution, they require different actors and capabilities in the network composition to become a remarkable asset in developing technologies to be patented afterwards in some cases (Gomes et al., 2017).In facing of risks of failures during innovative trajectories, firms invest in collaborative initiatives as an attempt to mitigate cost impacts, share responsibilities and greater technical performance in the process of technology lifecycle development. Thus, technological alliances are useful means to attend these goals (Kim & Song, 2007). Technological alliances are critical to enable digital transformation and innovation. Briefly, Zhang et al. (2021) highlight technological alliance as a voluntary interfirm cooperation involving codeveloping technologies through sharing and exchanging of these technologies to meet business needs (W. Zhang et al., 2021).The collaborations in various technological domains help to bring heterogenous knowledge, complementary resources, and capabilities for a better innovation performance (Swink, 2006; W. Zhang et al., 2021). Under the perspective that innovation is essentially knowledge creation (Nonaka, 1994), collaborative innovation through a technological perspective may be configured by different activities, processes, or routines of generation, sharing, integration, and utilization of knowledge produced during the innovation process lifecycle (Nonaka, 1994; W. Zhang et al., 2021). Further, this configuration of activities, processes, or routines support the development of evolutionary technological capabilities (Sampson, 2007).In the field of technological innovations, the evolution now is more collaborative in nature (J. Zhang et al., 2019). Collaboration is a trend for technological prosperity. Analyzing collaborative innovation in the literature is a great challenge even if the focus on technologies is defined because various aspects and applications of collaboration to innovate invade the academic literature in many forms. For instance, Zhou and Ren (2021) analyzed low-carbon technology collaborative innovation in industrial cluster; Shen et al. (2021) studied collaborative innovation in supply chain systems; Wan et al. (2022) highlight that blockchain application intensify collaborative innovation through distributed computing, cryptography and game theory; Li and Zhou (2022) researched on the mechanism of Government–Industry–University–Institute collaborative innovation in green technology; and Fan et al. (2022) pointed out that collaborative innovation also may act as a driver to mobilize and coordinate scientific and technological resources within a city, further promoting innovative development among cities.On the other hand, technological collaborative innovations has its own dark side for firms: it has been costly, it demands money, efforts, and time (Torugsa & Arundel, 2016; Wegrich, 2019), and, further, it provokes operational adjustments, technological reconfiguration, and legal barriers to overcome to be effective for innovation (McGuire & Agranoff, 2011; Vivona et al., 2022). To address this side of collaborative innovation, Vivona et al. (2022) developed the cost theory to systematize all insights from the literature in four main factors: governance, compactness, reliability, and institutionalization to shed light on a broader range of costs for innovation incurred by collaborative arrangements. Governance refers to relationships in hierarchical level and the number of collaborators involved, reliability refers to relationships’ quality; compactness is about the degree of formality in relationships that connect collaborators; and institutionalization that measure what the extent the relationships in practice have been pre-established. This cost perspective may be explored empirically.The decentralization of technological collaborative innovation, its nonlinear, globalized, and networked form transformed its process to more collaborative approaches among entities (Fan et al., 2020). Lopes and Farias (2022) showed that technology tools support the establishment of relationships of trust promoted by leaders committed to well-established goals, being a characteristic of governance that has a positive influence on collaborative innovation processes. Hwang (2020) mentioned that several countries have implemented policies to facilitate technological convergence by supporting collaborative innovations. The author also mentions that collaborative innovation is a crucial strategy to facilitate technological convergence. In sum, firms have been increased collaboration in technological activities and collaboration works as an enabling to learn about turbulent technological change and uncertainties to enhance the ability to deal with innovations (Dodgson, 1993).Technological collaborative innovation is considered essential to promote the flow of resources, knowledge, and technology among entities, considering that innovation is no longer a closed and isolated system. The main premise is technologies do not exist in isolation. Only by exchanging materials, energy, and information with the environment the innovation system be renewed and developed. Therefore, the integrator condition of technological collaborative innovation is also conducive to a more comprehensive disclosure of the collaborative mode and overall performance of technological innovation activities (Fan et al., 2020).Technological collaborative innovation is not a merely coordination of an orderly arrangements of efforts to pursue a common technological purpose (Mooney, 1953), or a merely cooperation to join agreed-on goals into a share comprehension about design systems or reconfigure technological resources (Gulati et al., 2012). It merges cooperation (commitment towards same end) with coordination (complexity to work together effectively) (Vivona et al., 2022). This view may be much more explored by the researchers to enhance the practical aspects of this perspective.In general, collaboration itself does not survive in the face of inevitable behavioral problems which requires an establishment of trust characterized by receptive organizational cultures, community of interest, and continually supplement knowledge for the purpose of collaboration in highly successful technological innovations (Dodgson, 1993). Thus, this can be a new chapter for technological collaborative innovations.A inovação colaborativa tornou-se uma das decisões mais estratégicas entre as empresas e um fenômeno bem definido que se tornou popular entre profissionais e pesquisadores (A. S. Cui & O’Connor, 2012; Liu et al., 2017). Muitas teorias foram consideradas para explicar fenômenos de colaboração como visão baseada em recursos, teoria da organização, estratégia, teoria do processamento da informação, teoria econômica das complementaridades entre outras (Barney, 1991; Cassiman & Veugelers, 2006; Daft & Lengel, 1986; Milgrom & Roberts, 1995; Tushman & Nadler, 1978). No entanto, os avanços tecnológicos proporcionam novas variações na colaboração para a inovação. Por exemplo, atividades colaborativas com fornecedores e clientes (Karhade & Dong, 2021), projetos comunitários (Liu et al., 2017), colaboração com parceiros distantes (T. Cui et al., 2020), engajamento corporativo com startups (Shankar & Shepherd, 2019), redes de inovação (Aarikka-Stenroos et al., 2017) e ecossistemas de inovação (Granstrand & Holgersson, 2020).A inovação colaborativa assume a existência de atividades entre organizações executadas por pessoas que, juntas, realizam algo inovador com alto nível de interdependência (T. Cui et al., 2020; Davis & Eisenhardt, 2011). Alguns autores (Adner & Kapoor, 2010; T. Cui et al., 2020; Rico et al., 2008) destacam que essa interdependência se caracteriza em duas dimensões: tecnológica e comportamental. A interdependência tecnológica está atrelada ao conhecimento e a troca de recursos para pesquisa e desenvolvimento e a comportamental está associada ao campo da comunicação, da interação social entre os atores colaborativos e a coordenação dessas relações para inovar.Outras perspectivas na literatura explicam e teorizam sobre a inovação colaborativa como trajetórias de compartilhamento de conhecimento (Majchrzak & Malhotra, 2016; Trkman & Desouza, 2012), ou colaboração entre vários atores (Torfing, 2019), ou construção de capacidades colaborativas (Swink, 2006), entre outras abordagens. Neste editorial, trazemos alguns pensamentos e ideias sobre inovação colaborativa sob uma perspectiva tecnológica para incentivar pesquisadores a irem além na pesquisa sobre tecnologias inovadoras incorporadas em colaboração. Os esforços de colaboração também se tornaram uma forma comum das empresas potencializarem as inovações e seu desenvolvimento tecnológico com claros determinantes sobre seus efeitos benéficos e, portanto, a literatura está bem estabelecida neste assunto (Pereira et al., 2018). No entanto, a colaboração só é bem-sucedida quando recursos e capacidades tecnológicas são combinados e as partes definem conjuntamente como melhorá-los e usá-los de acordo (Snow, 2015).A inovação colaborativa como um novo paradigma tecnológico refere-se a um modelo de inovação em rede apoiado por interações de múltiplas partes, como empresas, universidades e instituições de pesquisa como elementos centrais e governo, instituições financeiras, organizações sem fins lucrativos, intermediários como elementos auxiliares (W. Zhang et al., 2021). Não obstante, as redes de colaboração que operam em diferentes níveis organizacionais estão presentes em vários padrões e características de evolução, elas requerem diferentes atores e capacidades na composição da rede para se tornarem um ativo notável no desenvolvimento de tecnologias a serem posteriormente patenteadas em alguns casos (Gomes et al., 2017).Diante dos riscos de falhas durante as trajetórias inovadoras, as empresas investem em iniciativas colaborativas na tentativa de mitigar os impactos de custos, compartilhar responsabilidades e maior desempenho técnico no processo de desenvolvimento do ciclo de vida da tecnologia. Assim, as alianças tecnológicas são meios úteis para atender a esses objetivos (Kim & Song, 2007). As alianças tecnológicas são fundamentais para permitir a transformação digital e a inovação. W. Zhang et al. (2021) destacam a aliança tecnológica como uma cooperação voluntária entre empresas envolvendo o codesenvolvimento de tecnologias por meio do compartilhamento e troca dessas tecnologias para atender às necessidades de negócios.As colaborações em vários domínios tecnológicos ajudam a trazer conhecimento heterogêneo, recursos complementares e capacidades para um melhor desempenho de inovação (Swink, 2006; W. Zhang et al., 2021). Sob a perspectiva de que inovação é essencialmente criação de conhecimento (Nonaka, 1994), a inovação colaborativa por meio de uma perspectiva tecnológica pode ser configurada por diferentes atividades, processos ou rotinas de geração, compartilhamento, integração e utilização do conhecimento produzido durante o ciclo de vida do processo de inovação (Nonaka, 1994; W. Zhang et al., 2021). Além disso, essa configuração de atividades, processos ou rotinas suporta o desenvolvimento de capacidades tecnológicas evolutivas (Sampson, 2007).No campo das inovações tecnológicas, a evolução agora é muito mais colaborativa por natureza (J. Zhang et al., 2019). A colaboração é uma tendência para a prosperidade tecnológica. Analisar a inovação colaborativa na literatura é um grande desafio mesmo que o foco em tecnologias seja definido, pois vários aspectos e aplicações da colaboração para inovar invadem a literatura acadêmica de várias formas. Por exemplo, Zhou e Ren (2021) analisaram a inovação colaborativa de tecnologia de baixo carbono em um cluster industrial; Shen et al., (2021) estudaram a inovação colaborativa em sistemas de cadeia de suprimentos; Wan et al., (2022) destacam que a aplicação de “blockchain” intensifica a inovação colaborativa por meio de computação distribuída, criptografia e teoria dos jogos; Li e Zhou (2022) pesquisou sobre o mecanismo de inovação colaborativa Governo–Indústria–Universidade–Instituto em tecnologia verde; e Fan et al., (2022) apontaram que a inovação colaborativa também pode atuar como um motor para mobilizar e coordenar recursos científicos e tecnológicos dentro de uma cidade, promovendo ainda mais o desenvolvimento inovador com outras cidades.Por outro lado, as inovações tecnológicas colaborativas têm seu próprio lado obscuro para as empresas: custa caro, demanda dinheiro, esforços e tempo (Torugsa & Arundel, 2016; Wegrich, 2019) e, além disso, provoca ajustes operacionais, reconfiguração tecnológica e barreiras legais a serem superadas para que a inovação seja efetiva (McGuire & Agranoff, 2011; Vivona et al., 2022). Para abordar esse lado da inovação colaborativa, Vivona et al. (2022) desenvolveu a teoria dos custos para sistematizar todos os insights da literatura em quatro fatores principais: governança, compactação, confiabilidade e institucionalização para esclarecer uma gama mais ampla de custos para inovação incorrida por acordos colaborativos. A governança refere-se às relações em nível hierárquico e ao número de colaboradores envolvidos. A confiabilidade refere-se à qualidade das relações. A compactação diz respeito ao grau de formalidade nas relações que conectam os colaboradores. E, a institucionalização mede até que ponto as relações na prática foram pré-estabelecidas. Esta perspectiva de custos pode ser explorada empiricamente.A descentralização da inovação tecnológica colaborativa, sua forma não linear, globalizada e em rede, transformou seu processo em abordagens mais colaborativas entre entidades (Fan et al., 2020). Lopes e Farias (2022) mostraram que as ferramentas tecnológicas auxiliam no estabelecimento de relações de confiança promovidas por líderes comprometidos com metas bem estabelecidas, sendo uma característica da governança que influencia positivamente nos processos de inovação colaborativa. Hwang (2020) mencionou que vários países implementaram políticas para facilitar a convergência tecnológica apoiando inovações colaborativas. O autor também menciona que a inovação colaborativa é uma estratégia crucial para facilitar a convergência tecnológica. Em resumo, as empresas têm aumentado a colaboração em atividades tecnológicas e a colaboração funciona como uma forma de aprender sobre mudanças tecnológicas turbulentas e incertezas para aumentar a capacidade de lidar com as inovações (Dodgson, 1993).A inovação tecnológica colaborativa é considerada essencial para promover o fluxo de recursos, conhecimento e tecnologia entre as entidades, considerando que a inovação não é mais um sistema fechado e isolado. A premissa principal é que as tecnologias não existem isoladamente. Somente trocando insumos e informações com o ambiente o sistema de inovação pode ser renovado e desenvolvido. Portanto, a condição integradora da inovação tecnológica colaborativa também é propícia à uma divulgação mais abrangente do modo colaborativo e do desempenho geral das atividades de inovação tecnológica (Fan et al., 2020).A inovação tecnológica colaborativa não é uma mera coordenação de arranjos ordenados de esforços para buscar um propósito tecnológico comum (Mooney, 1953), ou uma mera cooperação para unir objetivos acordados em uma compreensão compartilhada sobre sistemas de design ou reconfigurar recursos tecnológicos (Gulati et. al., 2012). Mescla cooperação (compromisso com o mesmo fim) com coordenação (complexidade para trabalhar em conjunto efetivamente) (Vivona et al., 2022). Essa visão pode ser muito mais explorada pelos pesquisadores para aprimorar os aspectos práticos dessa perspectiva.Em geral, a colaboração em si não sobrevive diante de problemas comportamentais inevitáveis que exigem um estabelecimento de confiança caracterizado por culturas organizacionais receptivas, comunidade de interesse e conhecimento suplementar contínuo para fins de colaboração em inovações tecnológicas de grande sucesso (Dodgson, 1993). Assim, este pode ser um novo capítulo para as inovações tecnológicas colaborativas.
Inovação colaborativa: uma perspectiva tecnológica
No full textCollaborative innovation become one of the most strategy decision across firms and a well-defined phenomenon that became popular among practitioners and researchers (A. S. Cui & O’Connor, 2012; Liu et al., 2017). Many theories were considered to explain collaboration phenomena such as resources-based view, organization theory, strategy, information processing theory, the economic theory of complementarities among others (Barney, 1991; Cassiman & Veugelers, 2006; Daft & Lengel, 1986; Milgrom & Roberts, 1995; Tushman & Nadler, 1978). However, technology advances provide new variations in collaboration to innovativeness. For example, collaborative activities with suppliers and customers (Karhade & Dong, 2021), community source projects (Liu et al., 2017) or collaboration with distant partners (T. Cui et al., 2020), corporate engagement with startups (Shankar & Shepherd, 2019), innovation networks (Aarikka-Stenroos et al., 2017), and innovation ecosystems (Granstrand & Holgersson, 2020).Collaborative innovation takes over the existence of an inter-organizational activities executed by people that together perform with high level of interdependence something innovative (T. Cui et al., 2020; Davis & Eisenhardt, 2011). Some authors (Adner & Kapoor, 2010; T. Cui et al., 2020; Rico et al., 2008) highlight that this interdependence is characterized along two dimensions: technological and behavioral. Technological interdependence is linked to knowledge and the exchange of resources for research and development, and behavioral interdependence is associated with the field of communication, social interaction between collaborative actors and the coordination of these relationships to innovate.Other perspectives in the literature explain and theorize about collaborative innovation as knowledge-sharing trajectories (Majchrzak & Malhotra, 2016; Trkman & Desouza, 2012), or multi-actor collaboration (Torfing, 2019), or building collaborative capabilities (Swink, 2006) among other approaches. In this editorial, we bring some thoughts and idea about collaborative innovation under a technological perspective to incentive researchers to go beyond in innovative technologies research embedded in collaboration.Collaboration efforts also became a common way of firms to enhance innovations and its technological development with clear determinants about their beneficial effects, and therefore, the literature is well stablished in this subject (Pereira et al., 2018). However, collaboration only succeeds when technological resources and capabilities are combined, and parties define jointly how to enhance and use them accordingly (Snow, 2015).Collaborative innovation as a new technological paradigm refers to a network innovation model supported by interactions of multiple parties such as enterprises, universities and research institutions as core elements and government, financial institutions, nonprofit organizations, intermediaries as auxiliary elements (W. Zhang et al., 2021). Notwithstanding, collaboration networks operating in different organizational levels are present in various patterns and characteristics of evolution, they require different actors and capabilities in the network composition to become a remarkable asset in developing technologies to be patented afterwards in some cases (Gomes et al., 2017).In facing of risks of failures during innovative trajectories, firms invest in collaborative initiatives as an attempt to mitigate cost impacts, share responsibilities and greater technical performance in the process of technology lifecycle development. Thus, technological alliances are useful means to attend these goals (Kim & Song, 2007). Technological alliances are critical to enable digital transformation and innovation. Briefly, Zhang et al. (2021) highlight technological alliance as a voluntary interfirm cooperation involving codeveloping technologies through sharing and exchanging of these technologies to meet business needs (W. Zhang et al., 2021).The collaborations in various technological domains help to bring heterogenous knowledge, complementary resources, and capabilities for a better innovation performance (Swink, 2006; W. Zhang et al., 2021). Under the perspective that innovation is essentially knowledge creation (Nonaka, 1994), collaborative innovation through a technological perspective may be configured by different activities, processes, or routines of generation, sharing, integration, and utilization of knowledge produced during the innovation process lifecycle (Nonaka, 1994; W. Zhang et al., 2021). Further, this configuration of activities, processes, or routines support the development of evolutionary technological capabilities (Sampson, 2007).In the field of technological innovations, the evolution now is more collaborative in nature (J. Zhang et al., 2019). Collaboration is a trend for technological prosperity. Analyzing collaborative innovation in the literature is a great challenge even if the focus on technologies is defined because various aspects and applications of collaboration to innovate invade the academic literature in many forms. For instance, Zhou and Ren (2021) analyzed low-carbon technology collaborative innovation in industrial cluster; Shen et al. (2021) studied collaborative innovation in supply chain systems; Wan et al. (2022) highlight that blockchain application intensify collaborative innovation through distributed computing, cryptography and game theory; Li and Zhou (2022) researched on the mechanism of Government–Industry–University–Institute collaborative innovation in green technology; and Fan et al. (2022) pointed out that collaborative innovation also may act as a driver to mobilize and coordinate scientific and technological resources within a city, further promoting innovative development among cities.On the other hand, technological collaborative innovations has its own dark side for firms: it has been costly, it demands money, efforts, and time (Torugsa & Arundel, 2016; Wegrich, 2019), and, further, it provokes operational adjustments, technological reconfiguration, and legal barriers to overcome to be effective for innovation (McGuire & Agranoff, 2011; Vivona et al., 2022). To address this side of collaborative innovation, Vivona et al. (2022) developed the cost theory to systematize all insights from the literature in four main factors: governance, compactness, reliability, and institutionalization to shed light on a broader range of costs for innovation incurred by collaborative arrangements. Governance refers to relationships in hierarchical level and the number of collaborators involved, reliability refers to relationships’ quality; compactness is about the degree of formality in relationships that connect collaborators; and institutionalization that measure what the extent the relationships in practice have been pre-established. This cost perspective may be explored empirically.The decentralization of technological collaborative innovation, its nonlinear, globalized, and networked form transformed its process to more collaborative approaches among entities (Fan et al., 2020). Lopes and Farias (2022) showed that technology tools support the establishment of relationships of trust promoted by leaders committed to well-established goals, being a characteristic of governance that has a positive influence on collaborative innovation processes. Hwang (2020) mentioned that several countries have implemented policies to facilitate technological convergence by supporting collaborative innovations. The author also mentions that collaborative innovation is a crucial strategy to facilitate technological convergence. In sum, firms have been increased collaboration in technological activities and collaboration works as an enabling to learn about turbulent technological change and uncertainties to enhance the ability to deal with innovations (Dodgson, 1993).Technological collaborative innovation is considered essential to promote the flow of resources, knowledge, and technology among entities, considering that innovation is no longer a closed and isolated system. The main premise is technologies do not exist in isolation. Only by exchanging materials, energy, and information with the environment the innovation system be renewed and developed. Therefore, the integrator condition of technological collaborative innovation is also conducive to a more comprehensive disclosure of the collaborative mode and overall performance of technological innovation activities (Fan et al., 2020).Technological collaborative innovation is not a merely coordination of an orderly arrangements of efforts to pursue a common technological purpose (Mooney, 1953), or a merely cooperation to join agreed-on goals into a share comprehension about design systems or reconfigure technological resources (Gulati et al., 2012). It merges cooperation (commitment towards same end) with coordination (complexity to work together effectively) (Vivona et al., 2022). This view may be much more explored by the researchers to enhance the practical aspects of this perspective.In general, collaboration itself does not survive in the face of inevitable behavioral problems which requires an establishment of trust characterized by receptive organizational cultures, community of interest, and continually supplement knowledge for the purpose of collaboration in highly successful technological innovations (Dodgson, 1993). Thus, this can be a new chapter for technological collaborative innovations.A inovação colaborativa tornou-se uma das decisões mais estratégicas entre as empresas e um fenômeno bem definido que se tornou popular entre profissionais e pesquisadores (A. S. Cui & O’Connor, 2012; Liu et al., 2017). Muitas teorias foram consideradas para explicar fenômenos de colaboração como visão baseada em recursos, teoria da organização, estratégia, teoria do processamento da informação, teoria econômica das complementaridades entre outras (Barney, 1991; Cassiman & Veugelers, 2006; Daft & Lengel, 1986; Milgrom & Roberts, 1995; Tushman & Nadler, 1978). No entanto, os avanços tecnológicos proporcionam novas variações na colaboração para a inovação. Por exemplo, atividades colaborativas com fornecedores e clientes (Karhade & Dong, 2021), projetos comunitários (Liu et al., 2017), colaboração com parceiros distantes (T. Cui et al., 2020), engajamento corporativo com startups (Shankar & Shepherd, 2019), redes de inovação (Aarikka-Stenroos et al., 2017) e ecossistemas de inovação (Granstrand & Holgersson, 2020).A inovação colaborativa assume a existência de atividades entre organizações executadas por pessoas que, juntas, realizam algo inovador com alto nível de interdependência (T. Cui et al., 2020; Davis & Eisenhardt, 2011). Alguns autores (Adner & Kapoor, 2010; T. Cui et al., 2020; Rico et al., 2008) destacam que essa interdependência se caracteriza em duas dimensões: tecnológica e comportamental. A interdependência tecnológica está atrelada ao conhecimento e a troca de recursos para pesquisa e desenvolvimento e a comportamental está associada ao campo da comunicação, da interação social entre os atores colaborativos e a coordenação dessas relações para inovar.Outras perspectivas na literatura explicam e teorizam sobre a inovação colaborativa como trajetórias de compartilhamento de conhecimento (Majchrzak & Malhotra, 2016; Trkman & Desouza, 2012), ou colaboração entre vários atores (Torfing, 2019), ou construção de capacidades colaborativas (Swink, 2006), entre outras abordagens. Neste editorial, trazemos alguns pensamentos e ideias sobre inovação colaborativa sob uma perspectiva tecnológica para incentivar pesquisadores a irem além na pesquisa sobre tecnologias inovadoras incorporadas em colaboração. Os esforços de colaboração também se tornaram uma forma comum das empresas potencializarem as inovações e seu desenvolvimento tecnológico com claros determinantes sobre seus efeitos benéficos e, portanto, a literatura está bem estabelecida neste assunto (Pereira et al., 2018). No entanto, a colaboração só é bem-sucedida quando recursos e capacidades tecnológicas são combinados e as partes definem conjuntamente como melhorá-los e usá-los de acordo (Snow, 2015).A inovação colaborativa como um novo paradigma tecnológico refere-se a um modelo de inovação em rede apoiado por interações de múltiplas partes, como empresas, universidades e instituições de pesquisa como elementos centrais e governo, instituições financeiras, organizações sem fins lucrativos, intermediários como elementos auxiliares (W. Zhang et al., 2021). Não obstante, as redes de colaboração que operam em diferentes níveis organizacionais estão presentes em vários padrões e características de evolução, elas requerem diferentes atores e capacidades na composição da rede para se tornarem um ativo notável no desenvolvimento de tecnologias a serem posteriormente patenteadas em alguns casos (Gomes et al., 2017).Diante dos riscos de falhas durante as trajetórias inovadoras, as empresas investem em iniciativas colaborativas na tentativa de mitigar os impactos de custos, compartilhar responsabilidades e maior desempenho técnico no processo de desenvolvimento do ciclo de vida da tecnologia. Assim, as alianças tecnológicas são meios úteis para atender a esses objetivos (Kim & Song, 2007). As alianças tecnológicas são fundamentais para permitir a transformação digital e a inovação. W. Zhang et al. (2021) destacam a aliança tecnológica como uma cooperação voluntária entre empresas envolvendo o codesenvolvimento de tecnologias por meio do compartilhamento e troca dessas tecnologias para atender às necessidades de negócios.As colaborações em vários domínios tecnológicos ajudam a trazer conhecimento heterogêneo, recursos complementares e capacidades para um melhor desempenho de inovação (Swink, 2006; W. Zhang et al., 2021). Sob a perspectiva de que inovação é essencialmente criação de conhecimento (Nonaka, 1994), a inovação colaborativa por meio de uma perspectiva tecnológica pode ser configurada por diferentes atividades, processos ou rotinas de geração, compartilhamento, integração e utilização do conhecimento produzido durante o ciclo de vida do processo de inovação (Nonaka, 1994; W. Zhang et al., 2021). Além disso, essa configuração de atividades, processos ou rotinas suporta o desenvolvimento de capacidades tecnológicas evolutivas (Sampson, 2007).No campo das inovações tecnológicas, a evolução agora é muito mais colaborativa por natureza (J. Zhang et al., 2019). A colaboração é uma tendência para a prosperidade tecnológica. Analisar a inovação colaborativa na literatura é um grande desafio mesmo que o foco em tecnologias seja definido, pois vários aspectos e aplicações da colaboração para inovar invadem a literatura acadêmica de várias formas. Por exemplo, Zhou e Ren (2021) analisaram a inovação colaborativa de tecnologia de baixo carbono em um cluster industrial; Shen et al., (2021) estudaram a inovação colaborativa em sistemas de cadeia de suprimentos; Wan et al., (2022) destacam que a aplicação de “blockchain” intensifica a inovação colaborativa por meio de computação distribuída, criptografia e teoria dos jogos; Li e Zhou (2022) pesquisou sobre o mecanismo de inovação colaborativa Governo–Indústria–Universidade–Instituto em tecnologia verde; e Fan et al., (2022) apontaram que a inovação colaborativa também pode atuar como um motor para mobilizar e coordenar recursos científicos e tecnológicos dentro de uma cidade, promovendo ainda mais o desenvolvimento inovador com outras cidades.Por outro lado, as inovações tecnológicas colaborativas têm seu próprio lado obscuro para as empresas: custa caro, demanda dinheiro, esforços e tempo (Torugsa & Arundel, 2016; Wegrich, 2019) e, além disso, provoca ajustes operacionais, reconfiguração tecnológica e barreiras legais a serem superadas para que a inovação seja efetiva (McGuire & Agranoff, 2011; Vivona et al., 2022). Para abordar esse lado da inovação colaborativa, Vivona et al. (2022) desenvolveu a teoria dos custos para sistematizar todos os insights da literatura em quatro fatores principais: governança, compactação, confiabilidade e institucionalização para esclarecer uma gama mais ampla de custos para inovação incorrida por acordos colaborativos. A governança refere-se às relações em nível hierárquico e ao número de colaboradores envolvidos. A confiabilidade refere-se à qualidade das relações. A compactação diz respeito ao grau de formalidade nas relações que conectam os colaboradores. E, a institucionalização mede até que ponto as relações na prática foram pré-estabelecidas. Esta perspectiva de custos pode ser explorada empiricamente.A descentralização da inovação tecnológica colaborativa, sua forma não linear, globalizada e em rede, transformou seu processo em abordagens mais colaborativas entre entidades (Fan et al., 2020). Lopes e Farias (2022) mostraram que as ferramentas tecnológicas auxiliam no estabelecimento de relações de confiança promovidas por líderes comprometidos com metas bem estabelecidas, sendo uma característica da governança que influencia positivamente nos processos de inovação colaborativa. Hwang (2020) mencionou que vários países implementaram políticas para facilitar a convergência tecnológica apoiando inovações colaborativas. O autor também menciona que a inovação colaborativa é uma estratégia crucial para facilitar a convergência tecnológica. Em resumo, as empresas têm aumentado a colaboração em atividades tecnológicas e a colaboração funciona como uma forma de aprender sobre mudanças tecnológicas turbulentas e incertezas para aumentar a capacidade de lidar com as inovações (Dodgson, 1993).A inovação tecnológica colaborativa é considerada essencial para promover o fluxo de recursos, conhecimento e tecnologia entre as entidades, considerando que a inovação não é mais um sistema fechado e isolado. A premissa principal é que as tecnologias não existem isoladamente. Somente trocando insumos e informações com o ambiente o sistema de inovação pode ser renovado e desenvolvido. Portanto, a condição integradora da inovação tecnológica colaborativa também é propícia à uma divulgação mais abrangente do modo colaborativo e do desempenho geral das atividades de inovação tecnológica (Fan et al., 2020).A inovação tecnológica colaborativa não é uma mera coordenação de arranjos ordenados de esforços para buscar um propósito tecnológico comum (Mooney, 1953), ou uma mera cooperação para unir objetivos acordados em uma compreensão compartilhada sobre sistemas de design ou reconfigurar recursos tecnológicos (Gulati et. al., 2012). Mescla cooperação (compromisso com o mesmo fim) com coordenação (complexidade para trabalhar em conjunto efetivamente) (Vivona et al., 2022). Essa visão pode ser muito mais explorada pelos pesquisadores para aprimorar os aspectos práticos dessa perspectiva.Em geral, a colaboração em si não sobrevive diante de problemas comportamentais inevitáveis que exigem um estabelecimento de confiança caracterizado por culturas organizacionais receptivas, comunidade de interesse e conhecimento suplementar contínuo para fins de colaboração em inovações tecnológicas de grande sucesso (Dodgson, 1993). Assim, este pode ser um novo capítulo para as inovações tecnológicas colaborativas.
