Decision最新文献

Working capital management (WCM) concerns decisions on the levels and turnover of the inventories, receivables, cash and current liabilities of a company. Consequently, WCM affects the profitability of an enterprise. This paper aims to determine the relationship between profitability and WCM, characterised by components of the company’s operating cycle. The research is based on meta-analysis and meta-regression methods that allow for the combination and analysis of the outcomes of individual empirical studies using statistical methods. Our final research sample consists of 43 scientific papers from 2003 to 2018. These studies covered almost 62,000 enterprises in 35 countries from 1992 to 2017. Our results indicate that there is a common, negative relationship between profitability and the cash conversion cycle (CCC). This relationship is conspicuous in various countries and in different economic contexts. A negative, statistically significant relationship was also detected between profitability and average collection period (ACP), the accounts payable period (APP) and inventory turnover cycle (ITC) as well. We also identified moderators of the diagnosed dependencies on the grounds of macroeconomic and institutional factors. The richer the economy, the weaker a negative impact of CCC on profitability. The higher the protection of creditors and debtors, the weaker the negative relationship between profitability and ITC. The opposite is applicable to inflation and ACP and APP, unemployment and CCC, ACP and APP, the availability of credit and APP and the degree of capital market development and CCC and ACP. The aforementioned macroeconomic and institutional factors cause the negative relationship between particular components of the operating cycle and profitability to deepen even further. Our research contributes to the existing knowledge by confirming that the negative relationship between profitability and all components of the operating cycle is dominant in the global economy. It also indicates that there are macroeconomic and institutional moderators of the strength and direction of these relationships.

Abstract

The role of information-processing strategies in defining individual attitudes toward decision-making has been little investigated and, in general, has almost always been explored via self-reported measures. This study investigates how different strategies for information processing are used to make decisions, via a new task based on realistic decision-making scenarios and observation of actual decisional behavior. Three levels of information processing are considered: (i) low-level information related to decision details; (ii) mid-level information concerning the task and the goals to be accomplished; (iii) high-level information including situational aspects and features of the context. General decision-making style questionnaire was also administered. Hierarchical cluster analysis parsed out three emerging profiles characterized by attention to low-level, mid-level, and high-level pieces of information, which was linked to detail-focused, task-oriented, and situation-aware approaches to gather and process information in supporting decision. Such emerging profiles also proved to differ in terms of primary general decision-making styles, a finding that depose in favor of the robustness of the latent classification. These findings allowed us to delineate a model in which different information-processing strategies provide the basis for identifying different profiles of decision-makers.

The paper aims to study relationships between results obtained by two instruments, the rational-experiential inventory, in its modified version named REI-20, and the general decision-making style (GDMS). Although both instruments differ in concept and construction of decision styles, they refer to two very similar constructs—rationality and experientiality or intuition, resulting from the dual concept of cognitive-experiential self-theory. Using the same experimental sample, we examined the relationships between the REI-20 modes, i.e., rational and experiential, and GDMS modes, i.e., rational, intuitive, dependent, avoidant, and spontaneous. We checked how rational and experiential decision-making styles identified by REI-20 correspond to the rational and intuitive modes of GDMS. We also examined the relationships between clusters of decision-making profiles, defined as combinations of various levels of rational and intuitive/experiential modes determined from both instruments. Finally, we analyzed the gender differences between the styles identified by both inventories. The between-tool analysis showed that rationality determined from REI-20 and GDMS correlate only weakly; however, the correlation between experientiality and intuitiveness is strong. Both tools produced inconclusive results when comparing gender differences. REI-20 differentiated significantly between genders, indicating that women are less rational and more experimental than men, while GDMS considered these differences insignificant. It implies that using a particular decision-making style inventory in advanced analyses of the process and outcomes of the decision-making requires exceptional caution as various tools may produce a different classification of decision-makers and lead to different, if not contradictory, conclusions.

There is an increasing emphasis on digital health. However, success of digital health depends on voluntary adoption, which requires good product–market fit for a wide range of users. A national-level survey through snowball sampling was conducted from November 2020 to March 2021 among all MBBS doctors willing to participate. A total of 1010 doctors from different sectors, locations, qualifications with wide range of experience and patient load participated. Doctors from across the board felt going digital would entail long learning curves, additional workload, more screen time and that they do not improve overall quality of care. Majority feel digital solutions do not help in increasing net revenue and consequently prefer free-of-cost digital solutions. Among those willing to pay, onetime investment for hardware/equipment (38%) followed by annual subscription for software licenses (34%) are the preferred modalities. Seventy-four percent of doctors expressed not being comfortable with government providing digital solutions or controlling the data. In order to make the findings more practical and relevant, digital health adoption curve and market intelligence grid have been proposed. Digital health companies can use the adoption curve to understand how adoption can fluctuate with cost, ease of use and data policy. The grid can help companies identify the requirements of their target segment of doctors and therefore achieve better product–market fit.

Too many choices result in choice overload, the effects of which are generally negative. The capacity of short-term memory also impacts the ability and efficiency of people to process multiple options or information, thus affecting the experience of choice. Using three different stimuli for measuring short-term memory capacity and an actual choice task, we provide converging evidence that short-term memory has a moderating effect on the choice overload effect (which is more likely to occur in individuals with poor short-term memory ability), but it is important to note that this moderation is stimulus-specific. That is to say, the choice overload effect of individuals in the picture choosing task is impacted solely by their short-term memory capacity of pictures, and has nothing to do with other stimulus types. It can help consumers and businesses better process the way information is presented to maximize short-term memory and reduce the occurrence of choice overload effect.