Can digital transformation change a firm's green innovation strategy? Evidence from China's heavily polluting industries

Enterprises are facing the superimposed challenges of digitalization and greening. The shift from reactive green technology innovation (RGT) to proactive green technology innovation (PGT) has special significance for sustainable economic development. Which strategies will companies choose? Can digital transformation (DT) motivate companies to transform their green innovation strategies? Enterprises' green innovation strategy choices must be explained with regard to digitalization. The purpose of this paper is to reveal how digitalization affects the choice of green innovation strategies and to provide a realistic basis for the sustainable development of heavily polluting enterprises. We formulated a "DT-capability-strategy" theoretical framework incorporating external constraints and internal attitudes to empirically test the microdata of 505 heavily polluting enterprises. The results show that: (1) DT can shift the heavily polluting enterprises' green innovation strategies from RGT to PGT. Endogenous tests and robustness tests support this conclusion. (2) A mechanism test shows that environmental regulations cannot significantly regulate a green innovation strategy. Only a company's capabilities and attitudes can influence PGT but their effects on RGT are not statistically significant. (3) The influence of DT on green innovation strategies shows multi-dimensional heterogeneity in the digital infrastructure, scale, and innovation level of the enterprise. The conclusions provide implications for enterprises to integrate their digital and green behaviors.

Overreliance on inefficient computer-mediated information retrieval is countermanded by strategy advice that promotes memory-mediated retrieval

With ubiquitous computing, problems can be solved using more strategies than ever, though many strategies feature subpar performance. Here, we explored whether and how simple advice regarding when to use which strategy can improve performance. Specifically, we presented unfamiliar alphanumeric equations (e.g., A + 5 = F) and asked whether counting up the alphabet from the left letter by the indicated number resulted in the right letter. In an initial choice block, participants could engage in one of three cognitive strategies: (a) internal counting, (b) internal retrieval of previously generated solutions, or (c) computer-mediated external retrieval of solutions. Participants belonged to one of two groups: they were either instructed to first try internal retrieval before using external retrieval, or received no specific use instructions. In a subsequent internal block with identical instructions for both groups, external retrieval was made unavailable. The 'try internal retrieval first' instruction in the choice block led to pronounced benefits (d = .76) in the internal block. Benefits were due to facilitated creation and retrieval of internal memory traces and possibly also due to improved strategy choice. These results showcase how simple strategy advice can greatly help users navigate cognitive environments. More generally, our results also imply that uninformed use of external tools (i.e., technology) can bear the risk of not developing and using even more superior internal processing strategies.

Impact analysis of renewable portfolio standard on retail power market considering quota heterogeneity

To promote the development of the renewable energy (RE) industry, China officially implemented renewable portfolio standard (RPS) in 2020, the policy effect of which is closely related to the amount of renewable power offered to users by power-selling enterprises. We use evolutionary game theory to analyze the behavioral strategies of regional governments, regulatory authorities, and power-selling enterprises under RPS, and build a system dynamics (SD) model to determine the influence of the relevant parameters on stakeholders' strategy making considering quota heterogeneity. The results show that: (1) enterprises evolve to being stable earlier in high-quota area than in low-quota area, which infers that RE development has a certain bottleneck in the initial stage and that RPS can play an effective role; (2) a high certificate price can not only help power selling companies evolve to being stable, but also promote the withdrawal of governments subsidies; (3) to increase the proportion of renewable electricity, the net profit of RE power should not be lower than that of conventional energy; and (4) the incentive effect of subsidy income is not stronger than that of resale income, while when compared with penalty, the incentive effect is stronger, and penalty is not more severe and actually better. Importantly, the results provide policy suggestions for the development of RPS.

Identifying Methods to Select and Tailor Implementation Strategies to Context-Specific Determinants in Child Mental Health Settings: A Scoping Review

This scoping review describes the state of the literature regarding Implementation Strategy Mapping Methods (ISMMs) within the context of child mental health practice delivery. Goals included (a) identify and describe ISMMs addressing determinants of implementing mental health evidence-based interventions (MH-EBIs) for children and (b) describe the scope of the literature (e.g., outcomes, remaining gaps) related to identified ISMMs. Following PRISMA-ScR guidelines, 197 articles were identified. After removing 54 duplicates, 152 titles and abstracts were screened, yielding 36 articles that were screened during the full-text review. The final sample included four studies and two protocol papers (n = 6). A data charting codebook was developed a priori to capture relevant information (e.g., outcomes) and content analysis was utilized to synthesize findings. Six ISMMs were identified: innovation tournament, concept mapping, modified conjoint analysis, COAST-IS, focus group, and intervention mapping. ISMMs were successful in leading to the identification and selection of implementation strategies at participating organizations, and all ISMMs included stakeholders throughout these processes. Findings revealed the novelty of this research area and highlighted numerous areas for future investigation. Implications related to implementation, service, and client outcomes are discussed, including the possible impact of utilizing ISMMs to increase access to MH-EBIs for children receiving services in community settings. Overall, these findings contribute to our understanding of one of the five priority areas within implementation strategy research-enhancing methods used to design and tailor implementation strategies-by providing an overview of methods that may be utilized to facilitate MH-EBI implementation in child mental health care settings. Trial Registration: Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43477-023-00086-3.

Strategy selection in decisions from givens: Deciding at a glance?

People deciding between alternatives have at their disposal a toolbox containing both compensatory strategies, which take into account all available attributes of those alternatives, and noncompensatory strategies, which consider only some of the attributes. It is commonly assumed that noncompensatory strategies play only a minor role in decisions from givens, where attribute information is openly presented, because all attributes can be processed automatically "at a glance." Based on a literature review, however, I establish that previous studies on strategy selection in decisions from givens have yielded highly heterogeneous findings, including evidence of widespread use of noncompensatory strategies. Drawing on insights from visual attention research on subitizing, I argue that this heterogeneity might be due to differences across studies in the number of attributes and in whether the same or different symbols are used to represent high/low attribute values across attributes. I tested the impact of these factors in two experiments with decisions from givens in which both the number of attributes shown for each alternative and the coding of attribute values was manipulated. An analysis of participants' strategy use with a Bayesian multimethod approach (taking into account both decisions and response-time patterns) showed that a noncompensatory strategy was more frequently selected in conditions with a higher number of attributes; the type of attribute coding scheme did not affect strategy selection. Using a compensatory strategy in the conditions with eight (vs. four) attributes was associated with rather long response times and a high rate of strategy execution errors. The results suggest that decisions from givens can incur cognitive costs that prohibit reliance on automatic compensatory decision making and that can favor the adaptive selection of a noncompensatory strategy.

Evaluation of government strategies against COVID-19 pandemic using q-rung orthopair fuzzy TOPSIS method

The COVID-19 outbreak, which emerged in China and continues to spread rapidly all over the world, has brought with it increasing numbers of cases and deaths. Governments have suffered serious damage and losses not only in the field of health but also in many other fields. This has directed governments to adopt and implement various strategies in their communities. However, only a few countries succeed partially from the strategies implemented while other countries have failed. In this context, it is necessary to identify the most important strategy that should be implemented by governments. A decision problem based on the decisions of many experts, with some contradictory and multiple criteria, should be taken into account in order to evaluate the multiple strategies implemented by various governments. In this study, this decision process is considered as a multi-criteria decision making (MCDM) problem that also takes into account uncertainty. For this purpose, q-rung orthopair fuzzy sets (q-ROFSs) are used to allow decision-makers to their assessments in a wider space and to better deal with ambiguous information. Accordingly, two different Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches are recommended under the q-ROFS environment and applied to determine the most appropriate strategy. The results of the proposed approaches determine the A1 — Mandatory quarantine and strict isolation strategy as the best strategy. Comparisons with other q-rung orthopair fuzzy MCDM methods and intuitionistic fuzzy TOPSIS method are also presented for the validation of the proposed methods. Besides, sensitivity analyses are conducted to check the robustness of the proposed approaches and to observe the effect of the change in the q parameter.

Developmental trajectories of strategy use in children with mathematical anxiety

The main goal of the present study was to explore strategy selection in high mathematical anxiety (MA) individuals, and to test the role of development in the selection of strategy. We tested 2nd, 3rd and 5th graders with high or low MA in simple and complex addition problems. Participants first solved the problems and were then asked to report the strategy that they used. During elementary school, typically developing children change strategy use. In the first years backup strategies of counting are very frequent, but with maturation and schooling, they can shift to memory- based strategies. Hence, we tested finger counting and advanced memory based strategies in high MA children. In finger counting, high MA children showed developmental delay. For example, in the third grade, low MA children stopped using finger counting, while high MA participants continued to use it. However, in the case of advanced strategy use, we found a different pattern: regardless of age, high MA children used less advanced strategies than low MA participants. Moreover, usage of advanced memory based strategies was modulated by visuospatial working memory abilities in the two groups. The present results suggest that the MA participant has atypical developmental trajectories in strategy use.

Problem Solvers Adjust Cognitive Offloading Based on Performance Goals

When incorporating the environment into mental processing (cf., cognitive offloading), one creates novel cognitive strategies that have the potential to improve task performance. Improved performance can, for example, mean faster problem solving, more accurate solutions, or even higher grades at university.¹ Although cognitive offloading has frequently been associated with improved performance, it is yet unclear how flexible problem solvers are at matching their offloading habits with their current performance goals (can people improve goal-related instead of generic performance, e.g., when being in a hurry and aiming for a "quick and dirty" solution?). Here, we asked participants to solve a cognitive task, provided them with different goals-maximizing speed (SPD) or accuracy (ACC), respectively-and measured how frequently (Experiment 1) and how proficiently (Experiment 2) they made use of a novel external resource to support their cognitive processing. Experiment 1 showed that offloading behavior varied with goals: Participants offloaded less in the SPD than in the ACC condition. Experiment 2 showed that this differential offloading behavior was associated with high goal-related performance: fast answers in the SPD, accurate answers in the ACC condition. Simultaneously, goal-unrelated performance was sacrificed: inaccurate answers in the SPD, slow answers in the ACC condition. The findings support the notion of humans as canny offloaders who are able to successfully incorporate their environment in pursuit of their current cognitive goals. Future efforts should be focused on the finding's generalizability, for example, to settings without feedback or with high mental workload.

A manipulability analysis of human walking

From the literature of biomechanics, it is now clear that humans use elevating, lowering and delayed-lowering strategies in order to maintain stability during perturbed walking. The main purpose of this study is to provide insights into the role of manipulability in selection of these strategies. A 37 degrees of freedom (DoFs) model of the human body is developed to evaluate the manipulability indices during walking. The model is considered as a tree-like structure and its forward kinematics equations and the Jacobian are derived based on the Denavit-Hartenberg (DH) convention. A hybrid genetic algorithm (HGA) is then employed to map the experimental kinematics of a human to the model. The kinematic and dynamic manipulability indices of the swing phase of walking are evaluated concentrating on early, mid and late swing phases. The results indicate that the manipulability indices can characterize well the selection of elevating, lowering and delayed-lowering strategies at different stages of the swing phase. The results kinematically describe the reason of selecting delayed-lowering strategy at mid-swing phase that was not obvious in previous studies. Moreover, the results show that at mid-swing phase of walking the kinematic maneuverability is lower than that of the early and late swing phases.

Hippocampal development and the dissociation of cognitive-spatial mapping from motor performance

The publication of a recent article in F1000Research has led to discussion of, and correspondence on a broader issue that has a long history in the fields of neuroscience and psychology.  Namely, is it possible to separate the cognitive components of performance, in this case spatial behavior, from the motoric demands of a task?  Early psychological experiments attempted such a dissociation by studying a form of spatial maze learning where initially rats were allowed to explore a complex maze, termed "latent learning," before reinforcement was introduced.  Those rats afforded the latent learning experience solved the task faster than those that were not, implying that cognitive map learning during exploration aided in the performance of the task once a motivational component was introduced.  This form of latent learning was interpreted as successfully demonstrating that an exploratory cognitive map component was acquired irrespective of performing a learned spatial response under deprivation/motivational conditions.  The neural substrate for cognitive learning was hypothesized to depend on place cells within the hippocampus.  Subsequent behavioral studies attempted to directly eliminate the motor component of spatial learning by allowing rats to passively view the distal environment before performing any motor response using a task that is widely considered to be hippocampal-dependent.  Latent learning in the water maze, using a passive placement procedure has met with mixed results.  One constraint on viewing cues before performing a learned swimming response to a hidden goal has been the act of dynamically viewing distal cues while moving through a part of the environment where an optimal learned spatial escape response would be observed.  We briefly review these past findings obtained with adult animals to the recent efforts of establishing a "behavioral topology" separating cognitive-spatial learning from tasks differing in motoric demands in an attempt to define when cognitive-spatial behavior emerges during development.

How does aging affect recognition-based inference? A hierarchical Bayesian modeling approach

The recognition heuristic (RH) is a simple strategy for probabilistic inference according to which recognized objects are judged to score higher on a criterion than unrecognized objects. In this article, a hierarchical Bayesian extension of the multinomial r-model is applied to measure use of the RH on the individual participant level and to re-evaluate differences between younger and older adults' strategy reliance across environments. Further, it is explored how individual r-model parameters relate to alternative measures of the use of recognition and other knowledge, such as adherence rates and indices from signal-detection theory (SDT). Both younger and older adults used the RH substantially more often in an environment with high than low recognition validity, reflecting adaptivity in strategy use across environments. In extension of previous analyses (based on adherence rates), hierarchical modeling revealed that in an environment with low recognition validity, (a) older adults had a stronger tendency than younger adults to rely on the RH and (b) variability in RH use between individuals was larger than in an environment with high recognition validity; variability did not differ between age groups. Further, the r-model parameters correlated moderately with an SDT measure expressing how well people can discriminate cases where the RH leads to a correct vs. incorrect inference; this suggests that the r-model and the SDT measures may offer complementary insights into the use of recognition in decision making. In conclusion, younger and older adults are largely adaptive in their application of the RH, but cognitive aging may be associated with an increased tendency to rely on this strategy.

Expert intuitions: how to model the decision strategies of airport customs officers?

How does expertise impact the selection of decision strategies? We asked airport customs officers and a novice control group to decide which passengers (described on several cue dimensions) they would submit to a search. Additionally, participants estimated the validities of the different cues. Then we modeled the decisions using compensatory strategies, which integrate many cues, and a noncompensatory heuristic, which relies on one-reason decision making. The majority of the customs officers were best described by the noncompensatory heuristic, whereas the majority of the novices were best described by a compensatory strategy. We also found that the experts' subjective cue validity estimates showed a higher dispersion across the cues and that differences in cue dispersion partially mediated differences in strategy use between experts and novices. Our results suggest that experts often rely on one-reason decision making and that expert-novice differences in strategy selection may reflect a response to the internal representation of the environment.