Debora Di Caprio

Digitalization strategies emerge through interactions among producers, consumers, and institutions across value chains, leading to gains in productivity, the introduction of new patents, and innovations in products and processes. Dynamic Capabilities theory extends the static resource-based view of the firm by incorporating learning and innovation in evolving environments. Its collaborative extension has recently been introduced to analyze the behavior of corporate dynamics within evolving ecosystems shaped by Information and Communication Technologies and firms' digitalization strategies. However, empirical evaluations of how digital value chains influence firm level productivity and innovation remain limited, relying on static efficiency evaluation frameworks. We apply a dynamic Slacks-Based Data Envelopment Analysis model to a panel of 1,369 Spanish manufacturing firms progressing through the emergence and consolidation phases of digitalization that have characterized the early decades of the twenty-first century. We examine how efficiency and competitiveness evolve, finding that larger firms and those in more complex technological sectors show more varied behavior but lower inefficiency. The findings offer implications for theory, best practices, and digitalization policies. The robustness of our analysis has been validated by demonstrating substantial differences between the patterns obtained under the standard static and our dynamic evaluation environments.

Accepted by: Ali Emrouznejad The information retrieval behaviour of decision-makers (DMs) dealing with alternatives defined by multiple characteristics is generally determined by the expectations operator. Even when considering decision trees in sequential evaluation environments, the central assumptions imposed state that given a probability function defined on the set of potential realizations from a given alternative, the expected value suffices as a decision-making operator. However, suppose DMs evaluate the combinations of multiple characteristics relative to their expected values when selecting an alternative. Why do not they incorporate these combinatorial possibilities beforehand into their information retrieval process? We analyse the consequences of incorporating this combinatorial behaviour within an online information retrieval environment and illustrate the differences in utility that arise when evaluating sets of alternatives of different cardinality. We simulate 1,000,000 runs for different selection settings determined by the information retrieved by DMs before choosing and computing the utility received from implementing a standard expected utility and a combinatorial forward-looking strategy. We illustrate how this latter strategy pays off if DMs behave according to the postulates defining rational behaviour through the whole decision process. This requirement implies evaluating the entire set of characteristics composing the alternatives before making a decision and considering their corresponding certainty equivalents as a benchmark to evaluate the alternatives. Finally, we use the information retrieval profiles generated to analyse the capacity of an artificial neural network to categorize DMs across the different evaluation and selection settings correctly.

The standard expected utility model applied by economists and decision scientists assumes both that decision makers (DMs) are rational and that their information retrieval behavior and choices are determined by the observed and potential values of the multiple characteristics defining the alternatives. In this regard, if DMs can formalize the information acquisition structures determined by the main postulates of expected utility theory, they should also be able to perform standard operations regarding the potential combinatorial outcomes that may be obtained when evaluating the alternatives. We define an information retrieval scenario where DMs account for the different combinatorial possibilities arising among the realizations of the characteristics defining the alternatives before evaluating them. We demonstrate the indifference that arises among risk-neutral DMs endowed with standard expected utilities within sequential information acquisition environments such as those defined by online search engines. We also illustrate the reticence of DMs to acquire information on new alternatives when increasing their aversion to risk or modifying the relative importance assigned to the different characteristics defining the alternatives. The main strategic consequences that follow from the enhanced information retrieval scenario proposed are also analyzed.

Data labeling systems are designed to facilitate the training and validation of machine learning algorithms under the umbrella of crowdsourcing practices. The current paper presents a novel approach for designing a customized data labeling system, emphasizing two key aspects: an innovative payment mechanism for users and an efficient configuration of output results. The main problem addressed is the labeling of datasets where golden items are utilized to verify user performance and assure the quality of the annotated outputs. Our proposed payment mechanism is enhanced through a modified skip-based golden-oriented function that balances user penalties and prevents spam activities. Additionally, we introduce a comprehensive reporting framework to measure aggregated results and accuracy levels, ensuring the reliability of the labeling output. Our findings indicate that the proposed solutions are pivotal in incentivizing user participation, thereby reinforcing the applicability and profitability of newly launched labeling systems.