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When identifying or comparing organizations with a competitive intelligence function, one should first identify their competitive intelligence systems and then classify the maturity of their competitive intelligence functions. This paper presents four different classification levels based on different focus from different authors.
The establishment of a common ground for basic definitions and tools of competitive intelligence to practitioners and academics can be partially achieved by fully recognize and comprehend the inputs and outputs of the intelligence cycle. Nevertheless there are some additional issues to address in order to reach the so looked-for shared definitions and knowledge base.
Abstract: This paper proposes a model aiming at quantifying the impact that volume uncertainty may produce on construction projects’ value and on the optimal bid price. Volume uncertainty is present in most construction projects since managers do not know, during the bid preparation stage, the exact volume of work that will be executed during the project’s life cycle. Volume uncertainty leads to profit uncertainty and hence the model integrates a discrete-time stochastic variable, designated as “additional value”, i.e., the value that does not directly derive from the execution of the tasks specified in the bid documents, and which can only be properly quantified by undertaking an incremental investment in human capital and technology. The model determines that, even only recurring to the skills of their own experienced staff, contractors will produce a more competitive bid provided that the expected amount for the additional profit is greater than zero. However, construction managers often need to hire specialized firms and highly skilled professionals in order to quantify the expected amount of additional value and, hence, the impact of such additional value in the optimal bidding price. Based on the option to sign the contract and to perform the project by the selected bidder, identified and evaluated by Ribeiro et al. (2017), the model’s outcome is the threshold value for this incremental investment. A decision rule is then reached: construction managers should invest in human capital and technology provided that the cost of such incremental investment does not exceed the predetermined threshold value.
Abstract: Algorithms have played an increasingly important role in economic activity, as they becoming faster and smarter. Together with the increasing use of ever larger data sets, they may lead to significant changes in the way markets work. These developments have been raising concerns not only over the rights to privacy and consumers’ autonomy, but also on competition. Infringements of antitrust laws involving the use of algorithms have occurred in the past. However, current concerns are of a different nature as they relate to the role algorithms can play as facilitators of collusive behavior in repeated games, and the role increasingly sophisticated algorithms can play as autonomous implementers of pricing strategies, learning to collude without any explicit instructions provided by human agents. In particular, it is recognized that the use of ‘learning algorithms’ can facilitate tacit collusion and lead to an increased blurring of borders between tacit and explicit collusion. Several authors who have addressed the possibilities for achieving tacit collusion equilibrium outcomes by algorithms interacting autonomously, have also considered some form of ex-ante assessment and regulation over the type of algorithms used by firms. By using well-known results in the theory of computation, I show that such option faces serious challenges to its effectiveness due to undecidability results. Ex-post assessment may be constrained as well. Notwithstanding several challenges face by current software testing methodologies, competition law enforcement and policy have much to gain from an interdisciplinary collaboration with computer science and mathematics.
Abstract:This paper investigates the capital allocative behavior of firms’ integrating active internal capital markets (ICM). Specifically, examines the investment-cash flow sensitivity and its relationship with factors, such as, financial flexibility, suboptimality of investment expenditure, and crosssubsidization, using a matched sample design of two comparable panel data sets of 636 subsidiaries and stand-alone firms of the euro area, over the 2004–2013 sampling period. Results from panel data regression document that ICM firms exhibit lower sensitivity to the availability of internal funding than pure-play stand-alone firms, and that for stand-alone firms the effect of financial flexibility on investment-cash flow sensitivity is larger than for ICM cohorts. Findings also document that, on average, subsidiaries experience lower levels of investment suboptimality, and that subsidiaries with poor growth opportunities, ceteris paribus, invest less than pure-play stand-alone firms, consistent with lower cross-subsidization problems within ICMs. These findings are consistent with the propositions that centralized capital budgeting systems can potentially mitigate informational and incentive problems associated with investment behavior, and that subsidiary firms may use internal capital markets as a substitute for financial slack.