Abstract. Forced to provide results consistent results to shareholders the organizations turned to Robotic Process Automation (RPA)
PurposeThe purpose of this paper is to analyse the influences of different types of knowledge and their inherent dynamics on the effectiveness of the decision-making (DM) process. Knowledge dynamics (KD) is envisioned through the lens of the knowledge fields theory while effective DM process is objectivised via organisational appreciation and reward, higher business performance, sustainable partnerships and managerial satisfaction with previous achievements.Design/methodology/approachA questionnaire-based survey was conducted with 275 middle managers from companies operating in the business consulting field. The conceptual and structural model was tested using the partial least squares structural equation modelling technique.FindingsThe study advances novel insights into the significant positive influences of various knowledge fields on KD on the DM process within real-life business environments. Even though rational knowledge exerts a noteworthy effect on DM, its influence is exceeded by the KD, which proves that integrating emotional and spiritual knowledge in the decisional equation may become a pivotal input to making good managerial decisions regardless of the level of regulation and standardisation in the field.Research limitations/implicationsThe research relied on threefold knowledge fields as predictors for the DM process, thus providing a starting point for the development of more complex models.Originality/valueThe study emerges as a groundbreaking approach via the integration and application of the knowledge fields theory within a more comprehensive and empirical outlook on the DM process. Simultaneously, it places DM beyond the unidimensional outcomes of rationality and intuition by urging its intricate and interactional nature.
Robotic Process Automation (RPA) is going into a “maturity market”. The main vendor providers surpassed USD 1 billion in evaluation and the research they are launching these days on the market will change again radically the business landscape. It can be seen already what is coming next to RPA: intelligent optical character recognition (IOCR), chat-bots, machine learning, big data analytics, cognitive platforms, anomaly detection, pattern analysis, voice recognition, data classification and many more. As a result the top vendors developed partnerships with the main leading artificial intelligence providers, such as: IBM Watson, Microsoft Artificial Intelligence, Microsoft Cognitive services, blockchain, Google etc. On the business part, the consulting companies who are implementing the RPA solution are moving from developing Proof-of-Concepts (POCs) and Pilots to helping clients with RAP global roll-outs and developing Centre of Excellences (CoE). As a result, the experiences gathered so far by the author on this kind of projects will be tackled also in this paper. In this article will we will present also some data related to automation for different business areas (eg. Accounts Payable, Accounts Receivable etc) and how an assessment can be done correctly in order to decide if a process can be automatized and, if yes, up to which extent (ie. percent). Moreover, through the case studies we will provide (1) how now the RPA is integrated with Artificial Intelligence and Cloud, (2) how can be scaled in order to face hypes, (3) how can interpret data and (4) what savings these technologies can bring to the organizations. All the aforementioned services made Robotics Process Automation a very powerful tool since a year ago when the author did the last research. A process that was mainly not recommended for automation or was partially automated can be now fully automated with more advantages, such as: money, non-FTE savings and fulfillment time.
We propose here a method to analyze whether financial and macroeconomic shocks influence the entropy of financial networks. We derive a measure of entropy using the correlation matrix of the stock market components of the DOW Jones Industrial Average (DJIA) index. Using VAR models in different specifications, we show that shocks in production or the DJIA index lead to an increase in the entropy of the financial markets.
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