Possible applications in practice, adaptions, adjustments, extensions and propositions for future research, are discussed.Ī scale measuring real-time strategy (RTS) game experience was created and used to determine whether RTS experience acted as a nuisance or confounding variable in a serious game examining sunk cost effects (SCE). The study at hand has been explorative but, might give new possibilities for companies and research. Thus, with this new key indicator, the perspectives on product classification, replacement, shelves-arrangement or further purpose might be renewed. Based on current data, it has been pushed either by items with high impact present in shelf or by products with low impact out-of-shelf, and it has been reduced by products with a high AIF not present, and one with a low impact in shelf, respectively. Furthermore, depending on the presence of a product in shelf, interacting with AIF has been associated with the daily product-family revenue. Analysis have permitted indication for independence from a product's own revenue, and its corresponding family (and shelves, respectively). For theoretical, practical and, in particular, quantifiable interpretation of mutual influence of products arranged in shelves, a new key indicator has been introduced-the averaged impact factor (AIF) per product. asymmetrical domination and attraction) to be given, and has analysed real data made available by EuTrade. The study at hand has assumed human decision-irrationalities (e.g. Thus, big data analysis need thorough and comprehensible theory too, to interpret and understand its results. However, most of these methods remain correlative and may be un-theoretical.
Often this data is not meant for mining, and therefore great hope is with modern algorithms to find underlying, non-trivial patterns. However, collecting and describing might not be sufficient, and one must analyse and interpret the mass of information. In sunk cost procedures with human participants, verbal behavior appears to play an important role.īig data analysis, machine learning and data mining is almost contemporary, and many companies aspire profitable outcomes. It is possible that many supposed instances of the sunk cost effect are better conceptualized as behavior produced by contingencies in contexts with uncertain or probabilistic outcomes. Results from the present study align with previous studies suggesting that stimulus discriminability is a major determinant of non-optimal persistence on a task. One explanation for the difference between these tasks is that the behavior-based task may have more discriminable consequences the consequences for the hypothetical tasks were unknown. Overall, participants responded optimally on the behavior-based task and engaged in sunk cost behavior for the hypothetical scenario-based tasks. They were asked at various points of completion how likely they were to invest the remaining funds in the project. For the hypothetical scenario-based tasks, participants were given a scenario in which continuing on a present course of action entailed losses or was otherwise non-optimal. For the behavior-based task, participants played a video game and chose between continuing to engage an initial monster or switching to attack a new monster that arrived. Participants (n = 25) completed behavior-based and hypothetical scenario-based tasks to investigate the effects of manipulating percent of task completed on sunk cost behavior.
(2012) and compare performance on behavior-based and hypothetical scenario-based sunk cost procedures. The present study was designed to extend research by Pattison et al. This study is quite unique/original considering the research methodology and generalizability of study findings at strategic level decisions. However, this study was conducted on frequently occurring phenomena of mergers and acquisitions. There are a number of studies on the topic based on hypothetical surveys and/or lab experiments. The study further finds that underperforming M&As experienced more capital spending in the post-M&A period which implies that management is allocating more resources to failed mergers to prove them worthy. Research findings favour the sunk cost bias. Univariate and multivariate analyses were employed to capture the impact of firm performance on capital expenditures for 3 years period after M&A deals. However, Firm-specific data for acquirer and target firms were gathered from annual reports and websites of the respective companies. For this study, the deal-specific data of 184 mergers and acquisitions transactions, from SAARC and ASEAN regions, were collected from Bursa Malaysia Library. The aim of the study is to examine the sunk cost bias in underperforming mergers and acquisitions (M&A) deals characterized by capital spending in the post-M&A period.
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