The Asymmetric Nature of Decision Errors in Multi-Criteria, Satisficing Decisions

This paper centers on an asymmetry, or bias, in the accuracy of multi-criteria, conjunctive and disjunctive decisions, which originates from fundamental properties of the logical conjunction and disjunction operations. A series of Monte Carlo simulations demonstrates that, as we keep adding criteria to a multi-criteria satisficing decision rule, errors in the data produce decision errors asymmetrically. As a result, in conjunctive decisions, the probability of a false negative increases steadily while the probability of a false positive decreases. In contrast, in disjunctive decisions, as we keep adding criteria, the probability of a false positive increases while that of a false negative decreases. Take, for instance, a conjunctive business decision where the individual decision criteria do not exhibit a substantially higher probability of a false positive than a false negative. In such a decision, the probability of overlooking a bargain can be far greater than the probability of misjudging an unattractive offer to be a good one

Designing IS service strategy: an information acceleration approach

Information technology-based innovation involves considerable risk that requires insight and foresight. Yet, our understanding of how managers develop the insight to support new breakthrough applications is limited and remains obscured by high levels of technical and market uncertainty. This paper applies a new experimental method based on “discrete choice analysis” and “information acceleration” to directly examine how decisions are made in a way that is behaviourally sound. The method is highly applicable to information systems researchers because it provides relative importance measures on a common scale, greater control over alternate explanations and stronger evidence of causality. The practical implications are that information acceleration reduces the levels of uncertainty and generates a more accurate rationale for IS service strategy decisions

Measurement in marketing

We distinguish three senses of the concept of measurement (measurement as the selection of observable indicators of theoretical concepts, measurement as the collection of data from respondents, and measurement as the formulation of measurement models linking observable indicators to latent factors representing the theoretical concepts), and we review important issues related to measurement in each of these senses. With regard to measurement in the first sense, we distinguish the steps of construct definition and item generation, and we review scale development efforts reported in three major marketing journals since 2000 to illustrate these steps and derive practical guidelines. With regard to measurement in the second sense, we look at the survey process from the respondent's perspective and discuss the goals that may guide participants' behavior during a survey, the cognitive resources that respondents devote to answering survey questions, and the problems that may occur at the various steps of the survey process. Finally, with regard to measurement in the third sense, we cover both reflective and formative measurement models, and we explain how researchers can assess the quality of measurement in both types of measurement models and how they can ascertain the comparability of measurements across different populations of respondents or conditions of measurement. We also provide a detailed empirical example of measurement analysis for reflective measurement models

Corporate governance, Islamic governance and earnings management in Oman: A new empirical insights from a behavioural theoretical framework

Purpose: This paper examines the impact of corporate (CG) and Islamic (IG) governance mechanisms on corporate earnings management (EM) behaviour in Oman. Design/Methodology/Approach: We employ one of the largest and extensive datasets to-date on CG, IG and EM in any developing country, consisting of a sample of 116 unique Omani listed corporations from 2001 to 2011 (i.e.,1,152 firm-year observations) and a broad CG index containing 72 CG provisions. We also employ a number of robust econometric models that sufficiently account for alternative CG/EM proxies and potential endogeneities. Findings: First, we find that, on average, better-governed corporations tend to engage significantly less in EM than their poorly-governed counterparts. Second, our evidence suggests that corporations that depict greater commitment towards incorporating Islamic religious beliefs and values into their operations through the establishment of an IG committee tend to engage significantly less in EM than their counterparts without such a committee. Finally and by contrast, we do not find any evidence that board size, audit firm size, the presence of a CG committee and board gender diversity have any significant relationship with the extent of EM. Originality: To the best of our knowledge, this is a first empirical attempt at examining the extent to which CG and IG structures may drive EM practices that explicitly seeks to draw new insights from a behavioural theoretical framework (i.e., behavioural theory of corporate boards and governance). Keywords: Corporate governance, Islamic governance, earnings management, behavioural theory, endogeneity, Oman. Paper type: Research pape

Determining systematic differences in human graders for machine learning-based automated hiring

Firms routinely utilize natural language processing combined with other machine learning (ML) tools to assess prospective employees through automated resume classification based on pre-codified skill databases. The rush to automation can however backfire by encoding unintentional bias against groups of candidates. We run two experiments with human evaluators from two different countries to determine how cultural differences may affect hiring decisions. We use hiring materials provided by an international skill testing firm which runs hiring assessments for Fortune 500 companies. The company conducts a video-based interview assessment using machine learning, which grades job applicants automatically based on verbal and visual cues. Our study has three objectives: to compare the automatic assessments of the video interviews to assessments of the same interviews by human graders in order to assess how they differ; to examine which characteristics of human graders may lead to systematic differences in their assessments; and to propose a method to correct human evaluations using automation. We find that systematic differences can exist across human graders and that some of these differences can be accounted for by an ML tool if measured at the time of training

Filtering Survey Responses from Crowdsourcing Platforms: Current Heuristics and Alternative Approaches

Information Systems research continues to rely on survey participants from crowdsourcing platforms (e.g., Amazon MTurk). Satisficing behavior of these survey participants may reduce attention and threaten validity. To address this, the current research paradigm mandates excluding participants through filtering heuristics (e.g., time, instructional manipulation checks). Yet, both the selection of the filter and the filtering threshold are not standardized. This flexibility may lead to suboptimal filtering and potentially “p-hacking”, as researchers can pick the most “successful” filter. This research is the first to tests a comprehensive set of established and new filters against key metrics (validity, reliability, effect size, power). Additionally, we introduce a multivariate machine learning approach to identify inattentive participants. We find that while filtering heuristics require high filter levels (33% or 66% of participants), machine learning filters are often superior, especially at lower filter levels. Their “black box” character may also help prevent strategic filtering

Integração de fatores cognitivos na tomada de decisão

Decisions are pervasive phenomena in everyday life. Two predominant factors in decision-making theory are uncertainty and risk. Nevertheless, there are other cognitive aspects whose roles have recently gained attention in the literature. Those aspects include information acquisition strategies, scenario complexity, the dynamic and/or static aspect of choice scenarios, and the time-frame for the tasks. We propose a selective review and an integrative account as a way to argue for the theoretical importance of logically combining those different factors. The main argument is that the factors involved do not play independent roles, rather they can modulate each other´s influence given varying levels of the interactions between factors. The importance of better understanding such interactions lays on the choice of strategies that decision makers use to choose. Finally, theoretical, methodological and managerial contributions and implications relating to such approach are discussed.A decisão é um fenômeno pervasivo na vida cotidiana. Dois fatores predominantes na teoria de tomada de decisão são a incerteza e o risco. No entanto, existem outros aspectos cognitivos cujos papéis ganharam atenção na literatura. Esses aspectos incluem a aquisição de informações, complexidade do cenário, o aspecto dinâmico e / ou estático dos cenários de escolha e o tempo das tarefas. Propomos uma revisão seletiva e uma visão integrativa como forma de argumentar a importância teórica de combinar logicamente esses diferentes fatores. O principal argumento é que os fatores envolvidos não desempenham papéis independentes, ao contrário, podem modular a influência uns dos outros, dados os níveis variáveis das interações entre os fatores. A importância de entender melhor essas interações se baseiam na escolha de estratégias que os tomadores de decisão usam para escolher. Finalmente, as contribuições teóricas, metodológicas e gerenciais e implicações relativas a tal abordagem são discutidas