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As one of the most influential theories in strategic management, Hambrick and Mason’s Upper Echelons Theory has yielded significant conceptual and empirical advancements linking executive characteristics and perceptions to decision-making. Specifically, work on this theory consistently shows that CEOs’ decisions are biased by personal characteristics to the benefit and detriment of firms. While this stream of research links executive decision processes to outcomes such as executive dismissals, analyst evaluations, and press coverage, surprisingly little is understood about if and whether the information CEOs convey is subject to the same filtering process by a firm’s key evaluators. Thus, in this chapter, we aim to extend Upper Echelons Theory by positing that a double filtering process occurs whereby the cognitive aids CEOs use can be informed by not only their cognitive base and values but also the characteristics and priorities of those who evaluate the nonverbal and verbal signals they send. To do so, we build on recent conceptual and empirical advancements to make a case for the decision-making biases and tendencies that influence signal interpretation by three key evaluator groups internal and external to the firm: boards of directors, financial analysts, and the media. We conclude by considering the implications of evaluators’ information filtering and how this more holistic view of Upper Echelons decision-making can enable executive teams to be strategic with the cognitive aids they use to influence evaluations.

A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

Relative performance evaluation (RPE) is a widely practiced employee appraisal process in the services industry. In a global delivery model, teams are spread across different geographical locations. The team members work on various tasks under the guidance of different managers and at times under more than one manager for performing the same task. Such complexities make RPE of the team members quite challenging. The paper proposes a methodical step-by-step approach to simplify the evaluation process without compromising on the rigour.

RPE has followed three different approaches. First is the traditional way, wherein evaluators had a common meeting to discuss and arrive at relative evaluation and ranking of members of the peer group employees. In the second, the number of evaluators and employees in a peer group were split in to 2 subgroups. The evaluators provided independent ratings and rankings. Simple mathematical tool then derived the combined ranking. In the third approach, each evaluator evaluated each employee in the peer group and provided the relative ranking for each employee. Again, mathematical tools provided the final ranking considering inputs from all evaluators. All the three evaluation approaches were analysed through an inter-rater agreement method.

All the three approaches for evaluation provided similar results giving confidence that less time-consuming methods could be adopted by evaluators without compromising on the rigour of the evaluation. The outcome of the exercise proved effective as the complaints reaching the ombudsmen reduced as compared to the earlier years. Considerable evaluation time was also saved. The study described in this paper is carried out in a non-unionized, Indian private sector services firm. Its effectiveness in other set ups is yet to be tested.

The research is carried out in the Indian Engineering services firm operating in the Knowledge based sector. Though study results are encouraging, the adaptability of methodology across different sectors and geographies is yet to be tested. More broad based studies are needed to evaluate suitability across firms and regions.

Relative evaluation exercise is challenging for evaluators. Although openness in evaluation is desired, it also makes evaluators uncomfortable in appearing to be taking sides or being opposing a candidate's ranking. The proposed approach brings in anonymity to each evaluator without scarifying individual evaluation.

The proposed methodology can be deployed across different services industries as the proposed methodology is business domain agnostic. It can be easily ported and tailored to align with an individual organization's evaluation philosophy. The suitability and effectiveness of the method can be studied under various types of firms like manufacturing, private, public, NGO, labour oriented, etc. As the proposed method reduces efforts, the stake holders can focus on understanding the relation between employee performance measurement, employee engagement, and long-term outcomes related to employee performance evaluation.

The proposed employee evaluation method leverages inter-rater reliability and agreement tool as a consensus approach to the relative performance ranking exercise. Such an approach to relative performance ranking is original as no prior studies with such an approach are found in the existing Literature.

Will consumers accept artificial intelligence (AI) products that evaluate them? New consumer products offer AI evaluations. However, previous research has never investigated how consumers feel about being evaluated by AI instead of by a human. Furthermore, why do consumers experience being evaluated by an AI algorithm or by a human differently? This research aims to offer answers to these questions.

Three laboratory experiments were conducted. Experiments 1 and 2 test the main effect of evaluator (AI and human) and evaluations received (positive, neutral and negative) on fairness perception of the evaluation. Experiment 3 replicates previous findings and tests the mediation effect.

Building on previous research on consumer biases and lack of transparency anxiety, the authors present converging evidence that consumers who got positive evaluations reported nonsignificant difference on the level of fairness perception on the evaluation regardless of the evaluator (human or AI). Contrarily, consumers who got negative evaluations reported lower fairness perception when the evaluation was given by AI. Further moderated mediation analysis showed that consumers who get a negative evaluation by AI experience higher levels of lack of transparency anxiety, which in turn is an underlying mechanism driving this effect.

To the best of the authors' knowledge, no previous research has investigated how consumers feel about being evaluated by AI instead of by a human. This consumer bias against AI evaluations is a phenomenon previously overlooked in the marketing literature, with many implications for the development and adoption of new AI products, as well as theoretical contributions to the nascent literature on consumer experience and AI.

To address the key problem of lack of use in the advancement of open government data (OGD) portals from the aspect of good usability, which is an essential prerequisite to the acceptance and usage of a portal, this paper aims to develop a usability framework including design principles and criteria for OGD portals and to discover problems in the present usability design.

This study builds the usability framework by extending usability principles for general websites to address the specific needs of OGD portals. Criteria for each principle are developed accordingly based on the literature. A comparative heuristic evaluation involving five expert evaluators and 13 Chinese province-level OGD portals has been carried out to test the capability of the usability framework.

A usability framework with 24 principles and 63 criteria has been built. The heuristic evaluation shows OGD portals performed better in meeting general principles than the OGD portals specific ones. Insufficient help functions weakened OGD portals' usability. Similarities and differences were found of Chinese OGD portals compared with similar studies in the United States.

This paper proposed a usability framework for OGD portals and proved its capability in recognizing usability problems and its causes by carrying out a comparative heuristic evaluation in China. By comparing the evaluation results with other studies in the United States, the findings and lessons learnt in this study can thus be shared across international borders.

Although educators would likely agree that values and ethics are important in all disciplines, they have particular importance for practice-oriented fields. These applied professionals need to solve complex social problems that require the application of ethical standards and value perspectives. While the importance of value-engaged practice is known to the applied field, there is little research and conversation about how values can be integrated into teaching. This chapter synthesizes values-education approaches in various practice-based disciplines such as public administration (PA), program evaluation, social work, and public health. This chapter draws from empirical and theoretical works as well as the authors' experiences developing, participating in, and conducting values-based research on professionals and professional education.

This chapter explores the relevance of critical race theory (CRT) and queer theory to the relational aspects of program evaluation. Often conceptual binaries that undergird traditional evaluation theory and practice (e.g., internal versus external evaluation, subjective versus objective analysis, observation versus intervention, and insider versus outsider positionalities) adversely influence rigid social roles between evaluator and participant limit a study's effectiveness in supporting programs for equity in contemporary school districts. To illustrate this approach, an array of problems within a program evaluation of a district-wide ethnic studies reform initiative is presented. Approaches to these challenges rooted in tenets of CRT and queer theory illustrate how the district was able to clarify goals and develop an effective implementation plan that focused on effective ethnic studies curriculum and pedagogy.

Reducing the number of generated mutants by clustering redundant mutants, reducing the execution time by decreasing the number of generated mutants and reducing the cost of mutation testing are the main goals of this study.

In this study, a method is suggested to identify and prone the redundant mutants. In the method, first, the program source code is analyzed by the developed parser to filter out the effectless instructions; then the remaining instructions are mutated by the standard mutation operators. The single-line mutants are partially executed by the developed instruction evaluator. Next, a clustering method is used to group the single-line mutants with the same results. There is only one complete run per cluster.

The results of experiments on the Java benchmarks indicate that the proposed method causes a 53.51 per cent reduction in the number of mutants and a 57.64 per cent time reduction compared to similar experiments in the MuJava and MuClipse tools.

Developing a classifier that takes the source code of the program and classifies the programs' instructions into effective and effectless classes using a dependency graph; filtering out the effectless instructions reduces the total number of mutants generated; Developing and implementing an instruction parser and instruction-level mutant generator for Java programs; the mutant generator takes instruction in the original program as a string and generates its single-line mutants based on the standard mutation operators in MuJava; Developing a stack-based evaluator that takes an instruction (original or mutant) and the test data and evaluates its result without executing the whole program.

Educators strive to develop and implement high impact educational experiences, which are critical to ensuring university courses and curricula serve as memorable and transferable learning experiences for students. It is not clear, however, which experiences are exceptional from a student perspective, or what kinds of illustrative examples exist in applied disciplines. In this chapter, we ground our discussion of high impact educational experiences in the field of program evaluation, contextualize it as organized at the University of Minnesota, describe three experiences that have been repeatedly described as impactful by students, and engage in a collective dialogue as teachers and learners.

As global issues such as climate change, economic growth, social equality and the wealth gap are widely discussed, education for sustainable development (ESD) allows every human being to acquire the knowledge, skills, attitudes and values necessary to shape a sustainable future. It also requires participatory teaching and learning methods that motivate and empower learners to change their behavior and take action for sustainable development. Teachers have begun rating pupils based on peer assessment for open evaluation. Peer assessment enables students to transition from passive to active feedback recipients. The assessors improve critical thinking and encourage introspection, resulting in more significant recommendations. However, the quality of peer assessment is variable, resulting in reviewers not recognizing the remarks of other reviewers, therefore the benefits of peer assessment cannot be fulfilled. In the past, researchers frequently employed post-event questionnaires to examine the effects of peer assessment on learning effectiveness, which did not accurately reflect the quality of peer assessment in real time.

This study employs a multi-label model and develops a self-feedback system in order to use the AIOLPA system in the classroom to enhance students' learning efficacy and the validity of peer assessment.

The research findings indicate that the better peer assessment through the rapid feedback system, for the evaluator, encourages more self-reflection and attempts to provide more ideas, so bringing the peer rating closer to the instructor rating and assisting the evaluator. Improve self-evaluation and critical thinking for the evaluator, peers make suggestions and comments to help improve the work and support the growth of students' learning effectiveness, which can lead to more suggestions and an increase in the work’s quality.

ESD consequently promotes competencies like critical thinking, imagining future scenarios and making decisions in a collaborative way. This study builds an online peer assessment system with a self-feedback mechanism capable of classifying peer comments, comparing them with scores in a consistent manner and providing prompt feedback to critics.