Task Complexity: A Review and Analysis

Campbell, Donald J.

doi:10.5465/amr.1988.4306775

Cited by 978 publications

(442 citation statements)

References 28 publications

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“…A large body of research has attempted to conceptualize and define tasks and task characteristics (e.g., [7,9,17,26,27] [17] present a faceted classification of tasks. This classification includes generic facets of tasks (e.g., source of task, time, product, process and goal) and common facets of tasks including characteristics (e.g., objective task complexity and interdependence) as well as users' perception of task (e.g., salience, urgency, difficulty, subjective task complexity and knowledge of task topic).…”

Section: Tasks and Task Complexitymentioning

confidence: 99%

“…We used three conceptualizations of task complexity to guide the creation of our search tasks (presented in Section 3.3). The first was described by Campbell [9] and later used by Li and Belkin [17]. In this model, four attributes determine complexity: (1) the number of potential paths to the desired outcome; (2) the presence of multiple desired outcomes; (3) the presence of conflicting interdependencies between paths; and (4) uncertainty regarding paths.…”

Section: Tasks and Task Complexitymentioning

confidence: 99%

“…We propose that the different dimensions in Table 2 reflect increasing levels of cognitive complexity. Our definition of complexity is similar to that proposed by Li and Belkin [17] and Campbell [9] who related objective task complexity to the number of possible paths that can be taken to solve a task, the number of possible solutions and outcomes, and the amount of uncertainty. Since we are using learning tasks, we also propose that task complexity is related to the amount of cognition and effort required to complete the tasks.…”

Section: Tasksmentioning

confidence: 99%

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Task complexity, vertical display and user interaction in aggregated search

Arguello

Kelly

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Aggregated search is the task of blending results from specialized search services or verticals into the Web search results. While many studies have focused on aggregated search techniques, few studies have tried to better understand how users interact with aggregated search results. This study investigates how task complexity and vertical display (the blending of vertical results into the web results) affect the use of vertical content. Twenty-nine subjects completed six search tasks of varying levels of task complexity using two aggregated search interfaces: one that blended vertical results into the web results and one that only provided indirect vertical access. Our results show that more complex tasks required significantly more interaction and that subjects completing these tasks examined more vertical results. While the amount of interaction was the same between interfaces, subjects clicked on more vertical results when these were blended into the web results. Our results also show an interaction between task complexity and vertical display; subjects clicked on more verticals when completing the more complex tasks with the interface that blended vertical results. Subjects' evaluations of the two interfaces were nearly identical, but when analyzed with respect to their interface preferences, we found a positive relationship between system evaluations and individual preferences. Subjects justified their preference using similar rationales and their comments illustrate how the display itself can influence judgments of information quality, especially in cases when the vertical results might not be relevant to the search task. Categories and Subject Descriptors General TermsPerformance, Experimentation, Human Factors. KeywordsAggregated search interfaces, search behaviors, evaluation, user study, interaction, task complexity INTRODUCTIONIn addition to Web search, commercial search companies (e.g., Google, Bing, Yahoo!) provide access to a wide range of specialized services known as verticals (e.g., images, video, news Most published research in aggregated search has focused on automatic methods for predicting which verticals to present (vertical selection) [4,5,11,19] and where in the Web results to present them (vertical presentation) [2,3,23]. Evaluation of these systems has typically been conducted by using editorial vertical relevance judgements as the gold standard [2,3,4,5,19], or by using user-generated clicks on vertical results as a proxy for relevance [11,23]. While these studies have greatly advanced the state of the art in aggregated search techniques, because users are far removed from the evaluation, they have contributed little insight about how users' higher-level objectives influence their engagement with vertical search results.A few published studies have investigated user behavior with aggregated search interfaces [24,25,28]. Thus far, these studies show two major trends. First, when a vertical is relevant, users prefer to see its results towards to the top of the blended results [...

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Section: Tasks and Task Complexitymentioning

confidence: 99%

Section: Tasks and Task Complexitymentioning

confidence: 99%

Section: Tasksmentioning

confidence: 99%

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Task complexity, vertical display and user interaction in aggregated search

Arguello

Kelly

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Decision making literature suggests that task complexity influences decision maker's strategies (Payne et al, 1990), information seeking behaviors (Bystrom and Jarvelin, 1995), DSS use (Adya and Lusk, 2016), and performance (Campbell, 1988). In the forecasting literature, a small but consistent set of studies provide similar evidence that the complexity of a time series can challenge the forecasting process and detrimentally influence forecast accuracy (Goodwin and Wright, 1993).…”

Section: Introductionmentioning

confidence: 91%

Replication and Extension of a Forecasting Decision Support System: An Empirical Examination of the Time Series Complexity Scoring Technique

Adya¹,

Lusk²

2018

AIS TRR

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Abstract:This study presents a conceptual replication of Adya and Lusk's (2016) forecasting decision support system (FDSS) that identifies the complexity or simplicity of a time series. Prior studies in forecasting have argued convincingly that the design of FDSS should incorporate the complexity of the forecasting task. Yet, there existed no formal way of determining time series complexity until this FDSS, referred to as the Complexity Scoring Technique (CST). The CST uses characteristics of the time series to trigger 12 rules that score the complexity of a time series and classify it along the binary dimension of Simple or Complex. The CST was originally validated using statistical forecasts of a small set of 54 time series as well as judgmental forecasts from 14 representative participants to confirm that the FDSS successfully distinguished Simple series from Complex ones. In this study, we (a) replicate the CST on a much larger set of data from both statistical and judgmental forecasting methods, and (b) extend and validate the series classification categories from the binary Simple-Complex used in the original CST to Very Simple, Simple, Complex, and Very Complex thus adding an ordinal link between the two previous binary designations. Findings suggest that both the replication and extension of the CST further validate it, thereby greatly enhancing its use in the practice of forecasting. Implications for research and practice are discussed.

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“…First, past research suggests that more information contributes to task complexity (e.g., Steinmann 1976;Campbell 1988;Bonner 1994).…”

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confidence: 99%

The Effect of Accounting Reporting Complexity on Financial Analysts

Hoitash

Yezegel

2017

SSRN Journal

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We investigate the association between a new XBRL based measure of accounting reporting complexity (ARC) and analyst behavior. We find that analysts are less likely to cover firms with complex accounting. Further, higher ARC is associated with lower forecast accuracy, higher forecast dispersion, and lower informativeness of recommendation revisions and responsiveness to earnings announcements. This association is attenuated when analysts have longer tenure, greater firm-specific experience, and are focused on fewer industries. Investigating several complex accounts, we find that the complexity of derivatives, fair value, and pension accounts are each negatively associated with forecast accuracy, suggesting that understanding these complex accounts requires specialization. We propose a new measure of analysts' account-specific expertise and find that expertise with derivative and fair value accounts attenuates the negative effects of complexity in these accounts to a greater extent than general analyst experience. Overall, our findings suggest that analysts' expertise plays an important role in mitigating the adverse effects of ARC. JEL Classification: G24, G29, M41Keywords: XBRL, accounting complexity, financial analysts' performance, financial analysts' expertise Data Availability: Data are publicly available from sources identified in the paper. 1 The Effect of Accounting Reporting Complexity on Financial Analysts I. IntroductionRegulators and standard setters have long recognized that financial reporting has become overly complicated (SEC 2008; FRC 2009). As a result, the Securities and Exchange Commission (SEC) and the Financial Accounting Standards Board (FASB) undertook several initiatives to understand and simplify the financial reports (e.g. SEC 2008; FASB 2016). In this study, we examine how accounting reporting complexity (hereafter, ARC), measured as the count of accounting items disclosed in eXtensible Business Reporting Language (XBRL) 10-K filings, is associated with financial analysts' performance and coverage decisions. Since each accounting item is based on authoritative standards and regulations, understanding the financial reports of firms with higher ARC necessitates broader and more in-depth knowledge of accounting.While Hoitash and Hoitash (2017) find that more accounting disclosure complicates the work of preparers and auditors, it is unclear how disclosure volume will be associated with the work of sophisticated market participants such as financial analysts. Greater volume of accounting disclosures can help analysts understand past performance and generate more accurate forecasts.Indeed, several studies find that more disaggregated disclosures can lead to better analyst performance (Lang and Lundholm 1996;Chen et al. 2015). In contrast, more disclosure requires greater knowledge of accounting rules and regulations on part of financial analysts and a need to collect, analyze, and incorporate more information into their predictions. As a result, ARC may hurt overall analyst perfor...

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Task Complexity: A Review and Analysis

Cited by 978 publications

References 28 publications

Task complexity, vertical display and user interaction in aggregated search

Task complexity, vertical display and user interaction in aggregated search

Replication and Extension of a Forecasting Decision Support System: An Empirical Examination of the Time Series Complexity Scoring Technique

The Effect of Accounting Reporting Complexity on Financial Analysts

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