Evaluation Review最新文献

Although the United States has safe haven laws, which allow mothers to safely relinquish their babies to designated safety points, rates of infant mortality remain significantly higher in the United States than other similarly developed countries. The current study is seeking to explore the state-level association between safe haven laws and infant mortality in the United States utilizing a legal epidemiological approach. Several sources of publicly available data were combined to examine the state-level association between safe haven laws and rates of infant mortality. A backward stepwise regression was used to determine whether certain safe haven laws significantly predicted rates of infant mortality, while controlling for demographic variables. After controlling for demographic variables including rates of health insurance and poverty, safe haven laws stating that only the mother can relinquish a child, laws that protect parents from criminal liability, and laws requiring the provider to provide legal information and referrals were associated with infant mortality, with the total model accounting for 70.1% of variance in infant mortality. These results have important implications for policymakers considering the reform of the safe haven laws. It is especially important to evaluate the effectiveness and origins of safe haven laws in the wake of the overturn of Roe v. Wade. Future studies should longitudinally examine how changes in safe haven laws lead to changes in rates of infant mortality.

With the continuous advancement of digital technology, traditional manufacturing and service industries are facing dynamic changes with diversified demand and business models. Digital transformation (DT) and dynamic business modeling have become increasingly necessary for companies to evaluate and prioritize better opportunities to improve strategic planning and productivity. This study demonstrates that the system dynamics modeling process, based on the principles of sustainable system development, can fully reflect the comprehensiveness of systems thinking and avoid the drawbacks of linear thinking failing to integrate various departments. The proposed model enables the comprehensive integration of technology, production, marketing, management, and capital, thereby identifying and testing opportunities for successful DT with minimal costs and budgetary constraints. A real-world case study demonstrates the value of simulation-based dynamic business modeling with scientific data analysis for evaluating DT strategies.

Research on evaluation has mapped the landscape of quantitative evaluation methods. There are far fewer overviews for qualitative methods of evaluation. We present a review of scholarly articles from five widely read evaluation research journals, examining the types of methods used and the transparency of their quality criteria. We briefly look at a large sample of 1070 articles and then randomly select 50 for in-depth study. We document a remarkable variety of qualitative methods, but some stand out: Case studies and stakeholder analysis, often combined with interview techniques. Articles rarely define and conceptualize their methods explicitly. This is understandable from a practical point of view, but it can make it difficult to critically interrogate findings and build systematic knowledge. Finally, we find that the transparency of qualitative criteria required in the literature is not always sufficient, which can hinder the synthesis of results.

High-performance computing platforms (HPCP) serve as a critical component of institutional research infrastructure at universities, enhancing innovation efficiency. However, there is an absence of empirical research quantifying their impacts. Acknowledging this void, a difference-in-differences (DID) approach is employed in this study to systematically evaluate the influence of HPCP on university innovation efficiency. The findings reveal that: (1) universities with access to HPCP experience a significant improvement in innovation efficiency, with heterogeneities observed across regions, institutional rankings and university types; (2) HPCP drives innovation efficiency by attracting and cultivating top-tier researchers, expanding the scope of research activities and enabling the production of high-quality research outputs; and (3) the relationship between HPCP and innovation efficiency exhibits non-linearity, influenced by the quality and volume of research outputs as well as the presence of elite researchers. The contributions of this study are threefold viz: (1) providing empirical evidence quantifying HPCP's role in university innovation efficiency; (2) elucidating the mechanisms through which HPCP enhances innovation; and (3) identifying the non-linear relationship between HPCP and innovation efficiency, offering nuanced insights for policymakers to engender targeted strategies.

This paper analyzes the effects of caseworker screening of doctors' medical sick-leave certificates. The analysis uses data from people appealing caseworkers' denial of their benefit claims. Caseworkers at four units made decisions on the appeals. The distribution of the cases to the four units was based on the appealing person's birth date. One of the units was much stricter than the others (7.8% approved in contrast to 16.1% for the others). This allows us to use birth date as an instrument to estimate the effects of being denied sickness benefits. We find that the denial of sickness benefits, on average, has (i) positive effects on the labor-market outcomes and (ii) no negative effects on health outcomes. As the more liberal units deny sickness benefits for most screened medical certificates, we conclude that caseworker screening is very important in separating meritorious from non-meritorious claims. Doctors' conflicting roles make it difficult for them to act in the best of interest of both their patients and society.

The difference in differences design is widely used to assess treatment effects in natural experiments or other situations where random assignment cannot, or is not, used (see, e.g., Angrist & Pischke, 2009). The researcher must make important decisions about which comparisons to make, the measurements to make, and perhaps the number of individuals whose data is included in each timepoint. Also, interpretation of any statistical results, particularly null results, is improved by understanding the sensitivity of the design. This paper describes methods for computing the statistical power for tests of treatment effects in the difference in differences design. We describe alternative approaches to the analysis of the design, show which are equivalent, and provide expressions for computing statistical power and determining minimum detectable effect sizes. We then discuss how these methods could be generalized to unbalanced designs, designs with covariates, and designs more than two timepoints including difference in difference in differences designs.

Environmental, social, and governance (ESG) considerations are garnering increasing interest from various stakeholders. Nevertheless, the impact of ESG evaluation policies-whether voluntary or mandatory-are complex, particularly in competitive markets. Consequently, this study develops a game-theoretic model to investigate the propensity of competing manufacturers to invest in ESG initiatives. This study demonstrates that ESG evaluations reduce information asymmetry regarding ESG levels in competitive dynamics. However, this phenomenon may lower manufacturers' incentives to invest in ESG. Interestingly, this study finds that although mandatory ESG evaluations boost the overall profitability of competing manufacturers, they do not invariably lead to enhanced ESG outcomes.

Many evaluations of human services programs rely heavily on survey follow-up to collect outcome data from randomized controlled trials. Evidence clearinghouses will give their highest grade of evidence quality to such evaluations if attrition is low. Declining survey response rates, particularly in the control group, are making it more difficult for evaluators to meet this clearinghouse standard. Hendra and Hill (2019) suggested that it might be better to settle for substantially lower response rates, finding that the pursuit of high target response rates adds time and money while reducing nonresponse bias little if at all. This paper reports on a new approach to boosting response rates that allows reluctant respondents to complete a shortened version of the data collection instrument. Although the method is effective at increasing the response rate and costs less than traditional approaches for boosting response rates, this paper suggests that the data collected from reluctant respondents with the new method probably did not substantially reduce nonresponse bias on impacts, a finding broadly consistent with that of Hendra and Hill. On the other hand, the method did find some important differences in the means of some outcomes within the study arms, a finding that is could be important for descriptive surveys.

Social organizations' participation in the provision of urban community-based elderly care services serves as a critical pathway to address the challenges posed by an ageing society and to enhance service quality. To answer the livelihood-related question of 'How social organizations can contribute to urban community elderly care services?', this study surveys older adults in City N and uses satisfaction with community-based elderly care services delivered by social organizations as the performance indicator. Drawing on the conceptual framework of government external empowerment and internal capacity building within social organizations, the study develops a supply-side model of social organizations' participation in urban community-based elderly care and conducts an empirical analysis. The results show that government external empowerment, internal capacity building, and supply-demand alignment each have a significant and positive effect on satisfaction with community-based elderly care. Moreover, the integrated 'empowerment + capacity building' model-combining government external empowerment with social organizations' internal capacity building-not only infuses endogenous vitality and external support into their effective participation in providing elderly care services, but also offers a practical and feasible reference for City N to pursue the high-quality and sustainable development of urban community-based elderly care.

This study investigates how students engage with GenAI during a business data analysis assessment, drawing on Social Constructivist Theory and the human-AI co-agency model. Within the assessment, students used GenAI tools to support their data analysis and reflected on their experiences by comparing AI-generated and manually derived results. Thematic analysis of 258 students' reflection, triangulated with academic performance data, revealed four key themes: epistemic beliefs, functional cognitive engagement, reflective metacognitive learning, and Human-GenAI co-agency for strategic foresight. Students demonstrated distinct patterns of engagement across performance groups. Higher-performing students approached GenAI as a collaborative partner, engaging in iterative prompt refinement, demonstrating critical evaluation of outputs, and exhibiting strong ethical awareness. In contrast, lower-performing students often showed polarised epistemic beliefs, limited critical reflection, and minimal iteration - accepting or rejecting GenAI outputs prematurely. These findings highlight the role of scaffolded reflection and prompt engineering in enabling students to develop deeper analytical and evaluative capacities. By reconceptualising GenAI as an active co-learner rather than a passive tool, this study extends Social Constructivism perspectives to accommodate emerging forms of human-GenAI interaction. Drawing on rich, in-situ qualitative evidence embedded within an authentic learning context, it offers new insight into how students' beliefs and strategies shape their engagement with GenAI. The study also emphasises the need for differentiated pedagogical designs that cultivate AI literacy, narrow digital divide, and support ethical, adaptive use of GenAI in higher education.