Rationality is an elusive and increasingly debated concept in entrepreneurship research. We offer a novel conceptualization of rationality based on reasoning motivations. We posit that logical, probabilistic, and heuristic reasoning logics are motivationally rational because the decision-maker attempts to accurately perceive the external world and problem-solve (even if rapidly and approximately). By contrast, when the reasoning ignores an assessment of reality and accuracy in problem-solving and instead is deluded by psychological (e.g., hedonic) urges that prompt self-serving inferences, we categorize such decisions as motivationally irrational. We develop a theoretical account for how motivational irrationality is adaptive under extreme uncertainty as it enables entrepreneurs to dare action when even heuristic reasoning is inconclusive or entirely ineffective.
We show that in countries with more societal trust shareholders cast fewer votes at shareholder meetings and are more supportive of management proposals. This result is confirmed by instrumental variable regressions. It also holds at the U.S.-county level and for voting by U.S. institutional investors. Lower monitoring via voting relates less negatively to future firm performance in high-trust countries, suggesting that managers do not exploit greater discretion when trust is high. We also find a negative relation between trust and bond spreads. Our evidence supports theory arguing that trust substitutes for monitoring and has implications for investors’ optimal monitoring effort.
Malayalam-language newspapers published in the southern Indian state of Kerala are known for their contribution to the vernacular public sphere. However, while the popularity of commercial Malayalam-language newspapers has steadily increased over the past decades, “community-based” publications such as Deepika have experienced decline. Using the case of Deepika and drawing on the New Literacy Studies scholarship, we examine how print newspapers enter into the daily lives of people and analyze the reading practices associated with this tactile form of media in this part of the world. Our article contributes to Global South journalism studies by illuminating how literacy practices can provide an alternative theoretical framework to better understand the cultural meanings of print news media.
Cognitive diagnosis models (CDMs) are used in educational, clinical, or personnel selection settings to classify respondents with respect to discrete attributes, identifying strengths and needs, and thus allowing to provide tailored training/treatment. As in any assessment, an accurate reliability estimation is crucial for valid score interpretations. In this sense, most CDM reliability indices are based on the posterior probabilities of the estimated attribute profiles. These posteriors are traditionally computed using point estimates for the model parameters as approximations to their populational values. If the uncertainty around these parameters is unaccounted for, the posteriors may be overly peaked, deriving into overestimated reliabilities. This article presents a multiple imputation (MI) procedure to integrate out the model parameters in the estimation of the posterior distributions, thus correcting the reliability estimation. A simulation study was conducted to compare the MI procedure with the traditional reliability estimation. Five factors were manipulated: the attribute structure, the CDM model (DINA and G-DINA), test length, sample size, and item quality. Additionally, an illustration using the Examination for the Certificate of Proficiency in English data was analyzed. The effect of sample size was studied by sampling subsets of subjects from the complete data. In both studies, the traditional reliability estimation systematically provided overestimated reliabilities, whereas the MI procedure offered more accurate results. Accordingly, practitioners in small educational or clinical settings should be aware that the reliability estimation using model parameter point estimates may be positively biased. R codes for the MI procedure are made available
Transportation is a backbone of modern globalized societies. It also causes approximately one third of all European Union and U.S. greenhouse gas emissions, represents a major health hazard for global populations, and poses significant economic costs. However, rapid innovation in vehicle technology, mobile connectivity, computing hardware, and artificial intelligence (AI)-powered information systems heralds a deep socio-technical transformation of the sector. The emergence of connected, autonomous, shared, and electric (CASE) vehicle technology has created a digital layer that resides on top of the traditional physical mobility system. This article contributes a framework to direct research and practice toward leveraging the opportunities afforded by CASE for a more efficient and less environmentally problematic mobility system. The authors propose seven overarching dimensions of action. These range from designing real-time digital coordination mechanisms for the management of mobility systems to developing AI-powered real-time decision support for mobility resource planning and operations. Per each dimension, concrete angles of attack are suggested which, we hope, will spur structured engagement from both researchers and practitioners in the field.
We examine the effect of uncertainty shocks on the level of fiscal guidance – the guidance issued by governments on the expected evolution of the fiscal and economic outlook. Because uncertainty makes governments’ expectations less precise but potentially more valuable to users, we hypothesize that a disclosure dilemma leads governments to balance a higher demand for guidance with a higher probability of issuing inaccurate forecasts. Using natural disasters to randomize uncertainty shocks in our sample, we find that on average, governments issue less guidance in periods of uncertainty. The effect is driven by a reduction in the number of forecasts on the future evolution of balance sheet items, but only when governments have low refinancing needs and face a relatively quiet bond market. Instead, governments that maintain a stable level of guidance in periods of uncertainty appear to cater to coercive isomorphic pressures stemming from creditors. We further document that the relative ‘transparency’ of governments in periods of uncertainty is negatively related to indicators of fiscal reporting quality. Collectively, the evidence indicates that in the public sector, uncertainty leads to a trade-off between disclosure quantity and quality.
Mobile shopping is on the rise, and the main channel of social media has now shifted from online to mobile. We aim to understand the role of social media apps in driving mobile shopping. Specifically, we examine two performance metrics for mobile shopping—shopping app stickiness and usage time—and classify social media apps into broadcasting and narrowcasting ones. Our empirical analyses using mobile panel data reveal that the usage time of both types of social media apps increases shopping app stickiness. As for shopping app usage time, broadcasting app usage time has a positive impact, while narrowcasting app usage time has a negative impact. We also find that the impact of broadcasting app usage is greater than that of narrowcasting app usage. Furthermore, offline social interactions weaken the effect of social media usage on shopping app stickiness and that of broadcasting app usage on shopping app usage time.
Sensitivity analysis in probabilistic discrete graphical models is usually conducted by varying one probability at a time and observing how this affects output probabilities of interest. When one probability is varied, then others are proportionally covaried to respect the sum-to-one condition of probabilities. The choice of proportional covariation is justified by multiple optimality conditions, under which the original and the varied distributions are as close as possible under different measures. For variations of more than one parameter at a time and for the large class of discrete statistical models entertaining a regular monomial parametrisation, we demonstrate the optimality of newly defined proportional multi-way schemes with respect to an optimality criterion based on the I-divergence. We demonstrate that there are varying parameters' choices for which proportional covariation is not optimal and identify the sub-family of distributions where the distance between the original distribution and the one where probabilities are covaried proportionally is minimum. This is shown by adopting a new geometric characterization of sensitivity analysis in monomial models, which include most probabilistic graphical models. We also demonstrate the optimality of proportional covariation for multi-way analyses in Naive Bayes classifiers.
This paper draws on event system theory and the literatures on career orientations and career shocks to examine the impact of the COVID-19 pandemic on employees' career orientations. Factor analyses in three samples allow us to group seven career orientations into two dimensions: needs-based career orientations (those related to security, lifestyle, and health) and talent- and value-based career orientations (related to job content). We use a three-wave survey of Chinese employees to examine how these two broad orientations evolved in two time windows—one representing high, the other low event strength. We find that the two types of career orientations evolved in different ways during the pandemic: employees' needs-based career orientations were more salient during the COVID crisis than their talent- and value-based career orientations, and the salience of needs-based career orientations did not decrease as event strength abated. Employees' personal exposure to the crisis was positively related to the salience of their needs-based career orientations, but not to the salience of talent- and value-based career orientations. We also show that the salience of needs-based career orientations differed across employee groups: it was weaker among more experienced and successful employees (those higher in the managerial hierarchy and with steeper past pay increases).
Although the functional effects of turnover have been argued from the earliest research in the field, empirical evidence so far supports a general negative effect on unit performance, and attempts to explore its potential benefits are scarce. It has been argued that one reason for the absence of positive effects has to do with a lack of specificity of the turnover construct. The present study focuses on two sources of specificity: the reason for turnover and the job level of the departing employees. Our objective is to perform integrative research to analyze their joint effects and discuss how the four turnover scenarios created by their combination make their potential benefits of departures salient. We integrate arguments from human and social capital theories with the literature on team adaptation and change to develop our conceptual framework, and test our hypotheses using longitudinal monthly data from 5,202 stores of a large fashion multinational retailer in 39 countries. Our results provide evidence of a curvilinear relationship between staff quits and unit performance, and show that discharges are linearly beneficial both for managerial and staff positions, although at different degrees. Our findings demonstrate that differentiating between quits and discharges matters, and that the relative value conveyed by the job level of the departing employees is a relevant contingency in this distinct effect over performance.
This research considers the innovative educational strategy known as the liquid learning system, which allows students attending classes either online or face-to-face. This system was implemented for the first time at a private European university in 2020 as a reaction to the Covid-19 pandemic. Emphasis is placed on the effect of the online choice on student academic performance. Using Instrumental Variables to control for self-selection bias, our findings show a significant gap in the form of lower grades for online students. Quantile regressions reveal that those in the lower tail of the grade distribution are the most adversely affected.
This article investigates the effect of hiring temporary workers on the voluntary turnover of permanent employees. It argues that inflows of temporary workers erode the working conditions of permanent employees, prompting their voluntary departure. Using a unique panel dataset of individual-level monthly payroll data over an eight-year period in a sample of Spanish companies, a positive association between temporary worker inflows and the voluntary turnover of permanent workers is found. The results are robust to diverse specifications and are strongest for firms in non-manufacturing sectors and for firms that hire proportionally more low-skilled workers, contexts where the hiring of temporary workers may be more disruptive for permanent employees. Since the hiring of temporary workers is unlikely to threaten the employment of permanent employees in the dual labour market of Spain, the results indicate serious disruption costs associated with temporary hiring in organisations.
Perceived supervisor support is widely studied in terms of its positive outcomes. This paper, in contrast, investigates employees’ unethical pro-supervisor behavior as a negative consequence of perceived supervisor support. Drawing upon the multifoci approach of social exchange theory and the reciprocity principle, we hypothesized that perceived supervisor support can engender unethical pro-supervisor behavior via employees’ feelings of reciprocity towards the supervisor. Building on the instrumental reasons that underlie social exchanges, we further hypothesized that this mediation relationship is stronger for employees high in Machiavellianism. We collected data for three experimental studies from full-time MBA students of a European business school (Study 1: N = 72) and from U.S. working professionals (Study 2: N = 320; Study 3: N = 325), and the results provided consistent support for our proposed model. Taken together, the current study highlights the “dark side” of perceived supervisor support, in that it can lead to unethical behavior and that this effect can be accentuated by employees’ Machiavellianism.
Entry in new technological domains is essential for the long-term performance of firms. Therefore, it is important to understand the conditions that increase the likelihood that firms enter, and further explore, new technological domains. Some recent studies have started to unpack these issues by looking at the environmental conditions in a new technological domain that pull firms into it. In this paper, we complement these studies by looking at the environmental conditions in the firm's current technological domain that push firms into new domains. We do it from the perspective of technological ecology, by looking at how technological diversity and crowding in the firm's current technological niche, as well as firm's knowledge generalism, affect the likelihood that the firm enters, and further explores, new technological niches. To test our hypotheses, we rely on an empirical setting based on U.S. patents by 340 firms in the pharmaceutical industry. We propose a novel and advanced approach that, by leveraging a vast set of technological classifications, extracts technological niches from the patent system as they evolve over time.
Although recognized as a defining feature of the current political era, populism and its implications for nonmarket strategy remain undertheorized. We offer a framework that (a) conceptualizes populism and its progression over time; (b) outlines the risks populism generates for firms; and (c) theorizes effective nonmarket strategies under populism. Our framework anchors the political risk profile of populism in three interdependent elements: anti‐establishment ideology, de‐institutionalization, and short‐term policy bias. These elements jointly shape the policymaking dynamics and institutional risks for firms under populism. Our analysis shows how firms can calibrate two nonmarket strategies – political ties and corporate social responsibility – to mitigate populism‐related risks. We specify how particular configurations of political ties and CSR activities, aimed at the populist leadership, bureaucrats, political opposition, and societal stakeholders, minimize risk under populism. Further, we theorize how the effectiveness of specific attributes of political ties and CSR – namely their relative covertness (more vs. less concealed) and their relative focus (narrowly vs. widely targeted) – varies as a function of firm type (insiders vs. outsiders) and the probability of populist regime collapse. Finally, we address how motivated reasoning may bias firms’ assessments of regime fragility and resulting strategy choices.
We theorize that employees use the performance feedback they receive to reassess their beliefs about the marginal benefit of their effort, which may lead them to increase or reduce their effort. To test our model, we conduct a field experiment at the distribution center of a Fortune 500 firm where employees receive individual performance pay, and we study two types of feedback, individual and relative. The results show that employees react to feedback content in a way that is consistent with the model: they increase their effort if the information provided implies that the marginal benefit of increasing effort is high and decrease it if they learn that it is low. Moreover, performance feedback has a greater impact on the lower quantiles of the distribution of productivity. This article is protected by copyright. All rights reserved
Refugee workers struggle to find employment because they are stigmatized. Research suggests that organizations can help destigmatize actors such as refugees by recognizing them and confirming their worth in society. Here, we explore pictures that refugee job-placement organizations in Austria and Germany used to redefine refugees’ moral worthiness – that is, their worth in relation to higher-order normative principles such as civic duty, efficiency and creativity. Analysing images used in organizations’ destigmatization efforts is essential, as pictures visualize and materialize refugees rather than abstractly describing them. Hence, visualization shapes the worthiness of refugee workers in the eyes of prospective employers. Combining social semiotics with the economies of worth framework, we found that job-placement organizations use three visualization practices – professionalizing, domesticizing and stylizing – that draw on distinct moral orders. We found that although these practices were intended to destigmatize, they also – counterintuitively – restigmatize. By leveraging social semiotic studies of visualization, our results advance stigmatization studies by showing how visualization can unintendedly restigmatize and by revealing that the visualization practices we identified are built upon multiple forms of worth. Our analysis also theoretically and methodologically extends studies of organizational morality by explaining how moral dimensions are expressed through visual registers.
We investigate the effectiveness of two types of impression management tactics implemented around negative attributes: egocentric (claiming the absence or low presence of a negative attribute in a focal organization) and alter-centric tactics (claiming the greater presence of a negative attribute in an organization’s competitor). We claim that the effectiveness of each tactic depends on the risk of audiences’ skepticism, which stems from the incongruence between the information conveyed in the tactic and audiences’ default expectations about the presence of the attribute among members of a given market segment. Audiences expect a conspicuous presence of the attribute, we propose, the more stakeholders contest a market segment for that very attribute. Thus, we advance that egocentric (alter-centric) tactics are less likely to be effective for contested (uncontested) attributes because the information conveyed in such tactics clashes with audiences’ default expectations, triggering skepticism. We find support for our predictions looking at the impact of nutrient content claims on product sales in the U.S. food retail industry between 2006 and 2015.
Workers are increasingly being managed by technologies. Before spreading to larger segments of the labour market, algorithmic management systems were a signature feature of platform work. The exercise of power through digital labour platforms is one cause of the precarious working conditions in this area, an issue that could soon concern a wider group of workers in traditional economic sectors. This article elucidates the provisions regulating algorithmic management in the proposed EU Directive on improving working conditions in platform work, which tackles automated surveillance and automated decision-making practices. The proposed Directive mandates the disclosure of their adoption and sets out information and explanation rights regarding the categories of actions monitored and the parameters considered. Unlike rules concerning the presumption of employment status, the provisions on algorithmic management apply to all platform workers, including genuinely self-employed persons. Before offering a reasoned overview of the legal measures envisaged in the proposed text, this article grapples with the process leading to the proposed Directive in order to reveal the background and alternatives to the current formulation. It addresses the interplay between the text and other instruments regulating the deployment of technologies for managing workers. The steps intended to hold platforms to account are remarkable, but the regulatory technique could result in partially overlapping models, thereby increasing legal uncertainty and arbitrage.
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