The effect of context‐specific versus nonspecific subconscious goals on employee performance

We investigated the effect of context‐specific versus general subconscious goals on job performance in a call center. Employees (n = 54) were randomly assigned to a condition where they were primed by (a) a photograph of people making telephone calls in a call center, (b) a woman winning a race, or (c) a control group. Job performance was measured by the (1) number of and (2) monetary value of pledges from donors. None of the participants in the two experimental conditions showed conscious awareness of a prime. Analysis of variance indicated that both a subconscious context‐specific and a subconscious general goal aroused the implicit need for achievement as assessed by a projective measure, the Thematic Apperception Test (TAT). Both types of primed goals led to a significant increase in the number of pledges during a four‐day workweek. Consistent with goal‐setting theory, employees in the context‐specific condition raised more money than those in the general achievement (one‐tailed t‐test) and control (two‐tailed t‐test) conditions. © 2012 Wiley Periodicals, Inc.     

Measuring performance, development and growth when restricting flexibility

In industries with heterogeneous firms and fast-changing productivity, it is necessary to consider both firm and time effects if one is interested in technology adoption and efficiency. Since it is not possible to estimate individual firm effects for each time period due to degree-of-freedom problems, a trade-off is necessary with respect to where one is employing a flexible specification and where one is more restrictive. While most studies choose to account for variation, either in cross-sectional or temporal dimensions, we are in this paper, by clustering firms together in groups, allowing for variation in both cross-sectional and temporal dimensions. By modifying a shadow cost system, we are able to measure group-specific temporal patterns of technical and allocative efficiency and technological change without restricting the patterns to any parametric form.     

Welcome to the bright side: Why,how, and when overqualification enhances performance

We nuance the overtly negative image of overqualified employees by illuminating the brighter side of overqualification. In this extension of current thinking on overqualification, we argue that there are four ways in which overqualification can positively shape the performance of overqualified employees and their fellow group members. Following their theoretical basis, we refer to these as the (1) human capital, (2) status, (3) social learning, and (4) identification advantages. For each advantage we explain the conditions under which they are likely to materialize. We also theoretically explore how the advantages relate to each other as well as to the theories outlining the potential negative consequences of overqualification, and provide an according integrative model on the Relational Effects of Overqualification on Performance (REOP). After discussing the theoretical implications and providing an agenda for future research, we close with a discussion of the managerial implications of leveraging the bright side while acknowledging the dark side of overqualification.     

Linear versus logistic regression when the dependent variable is a dichotomy

The article argues against the popular belief that linear regression should not be used when the dependent variable is a dichotomy. The relevance of the statistical arguments against linear analyses, that the tests of significance are inappropriate and that one risk getting meaningless results, are disputed. Violating the homoscedasticity assumption seems to be of little practical importance, as an empirical comparison of results shows nearly identical outcomes for the two kinds of significance tests. When linear analysis of dichotomous dependent variables is seen as acceptable, there in many situations exist compelling arguments of a substantive nature for preferring this approach to logistic regression. Of special importance is the intuitive meaningfulness of the linear measures as differences in probabilities, and their applicability in causal (path) analysis, in contrast to the logistic measures.     

A graphically based machine learning approach to predict secondary schools performance in Tunisia

The main purpose of this paper is to identify the key factors that impact schools' academic performance and to explore their relationships through a two-stage analysis based on a sample of Tunisian secondary schools. In the first stage, we use the Directional Distance Function approach (DDF) to deal with undesirable outputs. The DDF is estimated using Data Envelopment Analysis method (DEA). In the second stage we apply machine-learning approaches (regression trees and random forests) to identify and visualize variables that are associated with a high school performance. The data is extracted from the Program for International Student Assessment (PISA) 2012 survey. The first stage analysis shows that almost 22% of Tunisian schools are efficient and that they could improve their students’ educational performance by 15.6% while using the same level of resources. Regression trees findings indicate that the most important factors associated with higher performance are school size, competition, class size, parental pressure and proportion of girls. Only, school location appears with no impact on school efficiency. Random forests algorithm outcomes display that proportion of girls at school and school size have the most powerful impact on the predictive accuracy of our model and hence could more influence school efficiency. The findings disclose also the high non-linearity of the relationships between these key factors and school performance and reveal the importance of modeling their interactions in influencing efficiency scores.     

The effect of primed goals on employee performance: Implications for human resource management

Overwhelming evidence in the behavioral sciences shows that consciously set goals can increase an employee's performance. Thus, HR professionals have had little, if any, reason to be interested in subconscious processes. In the past decade, however, laboratory experiments by social psychologists have shown that goals can be primed. That is, people's behavior is affected by goals of which they are unaware. Because a conscious goal consumes cognitive resources, this finding has important implications for employee efficiency in the workplace. This paper discusses the results of priming a performance goal in two organizational settings. Call center employees who were primed using a photograph of a woman winning a race raised significantly more money from donors than those who were randomly assigned to a control group. A meta‐analysis revealed that a photograph can prime the subconscious to increase job performance. The results of the present study demonstrate that subconscious motivation is a concept worthy of exploration for both human resource scholars and practitioners. © 2011 Wiley Periodicals, Inc.     

A theory of entrepreneurial learning from performance errors

This paper develops a theory of entrepreneurial learning from performance errors. The paper explains how entrepreneurs generate outcomes, and based on these, detect and correct errors in their own knowledge about the activities involved in creating and operating a new venture. The model developed in this paper reflects the major cognitive functions leading to outcome generation, error detection and error correction. We draw testable propositions about the effects of entrepreneurs’ domain-specific knowledge and cognitive ability on each stage of the learning process, which ultimately determine how much the entrepreneurs can learn from a given performance error.     

How firms compete when they set identical prices: Nonprice strategies in the Indian biscuit industry

How do firms compete when all firms in an industry set identical prices? Using Nielsen data on India's biscuit manufacturers, we document productivity-based competition on nonprice strategies under industry-wide uniform pricing. Products with one standard deviation higher quantity-based productivity contain, on average, 13% more quantity per pack for the same price. Productivity also positively correlates with promotions on pack size, availability, and variety. A higher price (per pack size) sensitivity in rural markets combined with industry-wide uniform pricing imposes a greater burden on rural consumers. Additional analyses show that firms can reduce this burden by selling different pack sizes in urban and rural areas.     

Group versus Individual Performance Pay in Relational Employment Contracts when Workers are Envious

I compare group to individual performance pay when workers are envious and performance is nonverifiable. Avoiding payoff inequity, the group reward scheme is optimal as long as the firm faces no credibility problem. The individual reward scheme may, however, become superior albeit introducing the prospect of unequal pay. This is due to two reasons: Group incentives are relatively low‐powered compared to individual incentives, requiring higher incentive pay and impeding credibility of the firm. Moreover, with individual rewards, the firm benefits from the incentive‐strengthening effect of envy, allowing for yet smaller overall incentive pay and further softening the credibility constraint. I also show that contracts combining both individual and group rewards are often optimal, depending on the firm's credibility problem. These contracts include joint and relative performance pay schemes.     

Bayesian estimation based on ranked set sample from Morgenstern type bivariate exponential distribution when ranking is imperfect

In this paper we consider Bayes estimation based on ranked set sample when ranking is imperfect, in which units are ranked based on measurements made on an easily and exactly measurable auxiliary variable X which is correlated with the study variable Y. Bayes estimators under squared error loss function and LINEX loss function for the mean of the study variate Y, when (X, Y) follows a Morgenstern type bivariate exponential distribution, are obtained based on both usual ranked set sample and extreme ranked set sample. Estimation procedures developed in this paper are illustrated using simulation studies and a real data.     

Assessing the performance of matching algorithms when selection into treatment is strong

This paper investigates the method of matching regarding two crucial implementation choices: the distance measure and the type of algorithm. We implement optimal full matching—a fully efficient algorithm—and present a framework for statistical inference. The implementation uses data from the NLSY79 to study the effect of college education on earnings. We find that decisions regarding the matching algorithm depend on the structure of the data: In the case of strong selection into treatment and treatment effect heterogeneity a full matching seems preferable. If heterogeneity is weak, pair matching suffices. Copyright © 2007 John Wiley & Sons, Ltd.     

Linking capacities of high‐quality relationships to team learning and performance in service organizations

This study examines the impact of intrateam and external high‐quality relationships (HQRs) on learning processes and performance. Data collected from 178 teams in the service sector indicate that (a) intrateam HQRs (i.e., between team members and between team members and their manager) are related to psychological safety, which in turn facilitates learning processes; (b) external HQRs are associated directly with team learning; and (c) team learning is positively associated with enhanced team performance. The findings highlight the importance of both internal and external HQRs to facilitate learning and enhance performance in service organizations. © 2011 Wiley Periodicals, Inc.     

Online banking performance evaluation using data envelopment analysis and axiomatic fuzzy set clustering

This paper presents a hybrid model to conducting performance measurements for Internet banking by using data envelopment analysis (DEA) and axiomatic fuzzy set (AFS) clustering. For each bank, DEA is applied to select a best combination by computing an aggregated efficiency score based on outputs, such as web metrics and revenue; and inputs, such as equipment, operation cost and employees. Then AFS clustering method is used to classify the best combination into different groups and provide the definitely semantic interpretations for the evaluation results. Identification of operational fitness and business orientation of each firm, in this way, will yield insights into understanding the weaknesses and strengths of banks, which are considering moving into Internet banking.     

The effects of internal versus external integration practices on time-based performance and overall firm performance

This paper examines the effects of integration practices on time-based performance and on overall firm performance (financial and market share). Integration practices are grouped into two categories: (1) external strategic design integration, which reaches across firm boundaries to involve suppliers and customers and (2) internal design-process integration, which comprises more tactically oriented, integration practices that match design requirements and process capabilities. First, regression results show that both internal and external integration are related to time-based performance, which in turn is related to firm performance. Thus, two indirect routes to firm performance are identified. Second, hierarchical regression reveals that integration directly affects firm performance even after time-based performance is accounted for. Finally, we found that the interaction of internal and external integration is significantly related to both market share and financial performance (after controlling for all other effects). This latter result suggests that the joint use of external and internal integration practices has a synergistic effect on firm performance.     

What to do when Stakeholders matter

This article focuses specifically on how and why managers might go about using stakeholder identification and analysis techniques in order to help their organizations meet their mandates, fulfill their missions and create public value. A range of stakeholder identification and analysis techniques is reviewed. The techniques cover: organizing participation; creating ideas for strategic interventions, including problem formulation and solution search; building a winning coalition around proposal development, review and adoption; and implementing, monitoring and evaluating strategic interventions. The article argues that wise use of stakeholder analyses can help frame issues that are solvable in ways that are technically feasible and politically acceptable and that advance the common good. The article concludes with a number of recommendations for management research, education and practice.     

Outsourcing logistics operations in circular economy towards to sustainable development goals

One of novel manners to achieve sustainable development is concentrating on circular economy in order to manage greenhouse gas emissions, energy consumption, and waste. It also helps to protect the environment and optimize usage of input resources. The main element of circular economy is closed-loop supply chain which covers both forward and reverse products flows. This study aims to outsource logistics operations in a closed-loop supply chain. An innovative analytical multi-step fuzzy decision-making method is proposed to rank sustainable third-party logistics service providers (3PLSPs). Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is first used to evaluate the relationships among the main criteria. Then, fuzzy analytic network process (ANP) is applied to weight the determined set of criteria. In order to develop the hybrid fuzzy DEMATEL-ANP method and to simplify the computation, the expert judgment method is used between the two techniques as a middle step to reduce the number of criteria. As many of the criteria overlap with each other on content and context, expert opinions are used to shortlist and rank the criteria. Finally, the 3PLSPs are ranked through the fuzzy complex proportional assessment (COPRAS) method. To validate the practicability and applicability of the improved decision-making model, a household appliance case study is applied with data obtained from industry experts. In this case, five service providers are considered and ranked, while the first one gets the best score. A sensitivity analysis is performed by altering the criteria weights to validate the proposed approach, data, and the obtained results. In terms business and the environment, this study provides success critical criteria for decision-making problems in circular economy for addressing sustainable development.     

Evolving to organizational learning

To transform in stride with the business changes, organizations need to think of development as "organizational learning" rather than "training." Companies need to manage learning as a strategic competitive advantage for current and future business rather than as a perk for individuals. To position themselves for success in a dynamic business environment, companies need to reframe their concept of learning and development to a mindset of organizational learning.