Performance Objectives Research Articles

Does diversity matter? Explaining the effects of team diversity on team performance using data from a student’s business simulation game

Purpose The purpose of this study is to determine the impact of different diversity criteria (age and gender) on team performance using a business simulation game, where performance is operationalized by market valuation (Tobin’s Q) and profitability (cumulative net income). Design/methodology/approach The authors collected data of a business simulation game at a German public university (128 student groups and 645 individuals). Using multiple linear regression models, they investigated the impact of the different diversity criteria on team performance. Findings The authors found no significant effects of gender diversity on market valuation and profitability. However, the results of this study indicate a positive relationship between age diversity and both performance variables. Originality/value This paper contributes to the debate on the relationship between diversity and team performance by using data from a business simulation game. This study adds value by considering different diversity factors, objective performance indicators and the endogeneity of team formation (the seminar leaders randomly assign students to teams) which has not been applied in similar studies yet. Because of the setting of a simulated business game, the results could also be applied to the real economy where we observe working teams every day.

A new era for stress research: supporting user performance and experience in the digital age

Stress is both a driver of objective performance impairments and a source of negative user experience of technology. This review addresses future directions for research on stress and ergonomics in the digital age. The review is structured around three levels of analysis. At the individual user level, stress is elicited by novel technologies and tasks including interaction with AI and robots, working in Virtual Reality, and operating autonomous vehicles. At the organisational level, novel, potentially stressful challenges include maintaining cybersecurity, surveillance and monitoring of employees supported by technology, and addressing bias and discrimination in the workplace. At the sociocultural level, technology, values and norms are evolving symbiotically, raising novel demands illustrated with respect to interactions with social media and new ethical challenges. We also briefly review the promise of neuroergonomics and emotional design to support stress mitigation. We conclude with seven high-level principles that may guide future work.

Linear parameter varying μ synthesis robust control of CDC semi‐active suspension system for nonlinearity and uncertainty

AbstractBecause the continuous damping control (CDC) semi‐active suspension system has complex nonlinear and uncertain characteristics that can significantly affect the performance of the system. And in the controller design process, achieving a balance of multiple performance objectives is often difficult. Therefore, this paper designs a linear parameter varying (LPV) μ synthesis robust controller for the vibration suppression and multi‐objective control of a 7‐degree‐of‐freedom (7‐DOF) vehicle suspension system equipped with CDC dampers. Firstly, the nonlinear force model of the solenoid valve CDC damper is developed based on the actual damping variation. Considering the nonlinear forces of the coil springs, the nonlinear model of the 7‐DOF suspension system is expressed as LPV form. Further, the linear fractional transformation (LFT) method is used to analyze and reconstruct the system model for multiple parameter uncertainties of the suspension. The actuator time delays are also simulated through a frequency domain transfer function, which is considered to be an unmodeled dynamic at the input of the system. Finally, an LPV‐μ synthesis robust controller based on nonlinearity and mixed uncertainty is designed to improve car ride comfort and achieve the best relationship between comfort and handling stability. Simulation and experimental results under random disturbance conditions show that the LPV‐μ synthesis controller has better anti‐jamming performance and controllability compared to the H∞ controller and μ synthesis controller.

Self-evaluations and the language of the beholder: objective performance and language solidarity predict L2 and L1 self-evaluations in bilingual adults

People are often asked to self-evaluate their abilities, and these evaluations may not always reflect objective reality. Here, we investigated this issue for bilingual adults’ self-evaluations of language proficiency and usage. We specifically examined how people’s self-reported language solidarity impacted their first- (L1) and second-language (L2) self-evaluations, while statistically controlling for their objective language performance (i.e. LexTALE). We also investigated whether this impact varied for value-laden evaluations (e.g. how “good” am I at my L2) vs. usage-based evaluations (e.g. how often do I use my L2) for two sociolinguistically distinct groups (i.e. English-L1 speakers vs. French-L1 speakers in Montreal). Starting with value-laden self-evaluations, we found that French-L1 speakers with more favourable L2-English solidarity tended to underestimate their objective L2 ability, whereas French-L1 speakers with less favourable L2-English solidarity more accurately estimated their objective L2 ability. In contrast, English-L1 speakers with more favourable L2-French solidarity more accurately estimated their objective L2 ability than those with less favourable L2-French solidarity who underestimated their L2-French abilities. Turning to usage-based self-evaluations, we found that participants' self-evaluations were generally more accurate reflections of their performance, in a manner that was less affected by individual differences in self-reported language solidarity. This implies that language solidarity (or perhaps language attitudes more generally) can implicitly or explicitly impact bilingual adults’ language self-evaluations when these evaluations are value-laden. These data suggest that people’s language attitudes can bias how they perceive their abilities, although self-evaluations based on language use may be less susceptible to bias than those that are value-laden. These data have implications for the study of language and cognition that depend on self-assessments of individual differences and are relevant to work on how people self-assess their abilities generally.

Situational and Dispositional Achievement Goals and Measures of Sport Performance: A Systematic Review with a Meta-Analysis

The purposes of this systematic review (PROSPERO ID: CRD42024510614, no funding source) were to quantify relationships between situational and dispositional dichotomous achievement goals and sport performance and explore potential relationship moderators. Published studies that reported at least one situational or dispositional achievement goal and a performance score were included. Studies without performance scores or based in a non-sport context were excluded. Information sources consisted of studies found in relevant published meta-analyses and EBSCOhost databases (finalized September 2024). The following statistics were conducted to assess the risk of bias: class-fail-safe n, Orwin’s fail-safe n, and funnel plots with trim and fill estimates. The summary statistics were r and d. Thirty studies from 1994 to 2024 met all inclusion criteria with 8708 participants from Europe, Asia, North America, and Oceania. The majority of samples were non-elite male youths and adolescents. The random-effects relationships (r) between task climate, 0.20 [0.14, 0.25], task orientation, 0.17 [0.12, 0.23], ego orientation, 0.09 [0.03, 0.16], and sport performance were small and significantly different (p < 0.05) from zero, while the ego motivational climate relationship was not, −0.00 [−0.48, 0.05]. The random-effects standard differences in means (d) for both the task orientation, 0.08 [0.02, 0.14], and ego orientation, 0.11 [−0.05, 0.26] were minimal in meaningfulness. Mixed-effects moderator analyses resulted in the following significant (p < 0.05) sub-group differences: subjective compared to objective performance measures (task orientation), elite compared to non-elite samples (task climate), and athlete-completed compared to coach-completed performance measures and performance records (task orientation). Finding only 30 studies meeting the inclusion criteria, which limited sub-group samples for moderation analyses, was the main limitation. Despite this limitation, AGT provides athletes and practitioners performance enhancement strategies. However, caution is warranted regarding relationship expectations given the small mean effect size values and the true prediction interval ranging from negative to positive, perhaps as a result of the heterogeneous samples and performance measures. A clear line of future research, considering the reviewed studies, with elite athletes is needed to verify the performance benefits of the task climate and ego orientation as well as the use of the ego goal orientation in selection decisions.

Controller influence on the fatigue of a floating wind turbine and load case impact assessment

Abstract The assessment of fatigue in Floating Offshore Wind Turbines (FOWT) is a complex and widely debated topic within the industry. The dynamic behaviour of FOWT is heavily influenced by sea and wind conditions, which can significantly challenge the achievement of structural integrity and performance objectives. Additionally, the interaction of blade aerodynamics and control strategies further complicates this assessment. This study evaluates the impact of different control strategies and blade formulations on both the expected real performance and the estimated fatigue of FOWT. While control strategies can influence both the expected real performance and fatigue estimations, the blade formulation primarily affects the accuracy of performance estimations without altering the actual expected performance. However, the blade formulation can significantly modify the fatigue estimation of the FOWT. Damage Equivalent Loads (DEL) calculation is a commonly used method for assessing fatigue. However, while individual DEL calculations are useful, they do not show the contribution of each load case to the overall fatigue, since they do not consider the probability of occurrence. Therefore, this paper proposes a new method of normalising DEL, allowing for comparison of loading cases and identifying operational areas that pose greater fatigue risks. The findings underscore the substantial impact that different control strategies and blade designs can have on fatigue and overall performance. Essentially, there is a trade-off between maximising power output and minimising fatigue damage.

A new method for judging thermal image quality with applications

Infrared thermal imaging, a non-destructive testing technology, measures the surface temperature of objects. Assessing thermal image quality is crucial for image monitoring, system design, algorithm optimization, and benchmarking. However, developing objective metrics that align with human perception is challenging due to the distinct structure of thermal images, which often feature high background temperatures and minimal variance between objects and the background. Existing methods typically target specific local features or overall image contrast, but new measures are needed to bridge the gap between objective performance and the unique characteristics of thermal images.We propose a novel image quality assessment (IQA) method inspired by the human vision system, specifically designed for thermal images, harmonizing local and global data. The primary contributions include (1) innovative local, global, and hybrid thermal quality assessment methods that deliver precise image quality predictions without needing reference images, (2) an experimental analysis evaluating the developed blind thermal IQA measure’s applicability to various thermal images, and (3) a comprehensive analysis of traditional IQA measure-based methods applied to publicly accessible thermal databases. Extensive simulations demonstrate our method’s competitive performance and strong alignment with human perception of image quality.

La valutazione della performance individuale del pubblico dipendente. Un progetto di ricerca basato su strumenti costruttivisti

In the Italian public entities, the assessment of employees involves the use of two parameters: the organizational performance objectives and the individual performance factors. The last ones concern the personal behaviors required in the workplace by employees under evaluation, and their measurement affects the construction of the roles of the evaluator and the evaluated. Difficulties invest both: the evaluator considers that he/she does not possess technical tools and relational skills to manage this process; the evaluated tends to focus mainly on the economic and career consequences. The aim of this work is to help the manager to recognize the meaning dimensions underlying his evaluator activity and to become more effective and consistent as evaluator through the use of some constructivist tools. In this way it will be possible to encourage the evaluator to become more aware about the dimensions involved and the impact of his activity on the assigned personnel and on the whole organization.

When and Why Do Supervisors’ Evaluations Overweight Subordinates’ Performance Outcomes? Evidence from a Team Setting in the Field

ABSTRACT To better understand supervisors’ outcome bias, I use a regression discontinuity design that compares coaches’ performance assessments of professional football players involved in narrow wins and losses. I document that supervisors over-react to negative outcomes, sharply lowering performance ratings and tripling subordinate turnover. I find that supervisors’ evaluations that subjectively incorporate information from more incomplete objective performance measures are more prone to outcome bias than those that draw on less incomplete objective measures. I also document that supervisors’ evaluations of high-performing team members are more prone to outcome bias than supervisors’ evaluations of low performers. Finally, I find that outcomes affect supervisors’ ex post information collection. My findings, consistent with predictions that I derive from the theory of cognitive reconstruction, shed light on supervisors’ outcome bias in team settings and how effectively firms’ use of objective performance measures, direct monitoring, and information gathering can mitigate this bias. JEL Classifications: M50; D91; Z20.

PREDICTING STUDENT PERFORMANCE IN ONLINE LEARNING PLATFORMS: ANALYZING ENGAGEMENT METRICS WITH MACHINE LEARNING MODELS

The emergence of online learning platforms harbours both opportunities and challenges, this paper focuses on machine learning algorithms for deep diving into key predictors of students success in online environments, captured by engagement metrics such as quiz scores, participation in forums, and time spent on learning tasks. Data was collected through a survey of 50 students and then run through the Random Forest model. Major predictors of success include the assignment approach, quiz score, and motivation. The prediction from this model can be interesting because it can pick important features, but the overall accuracy is quite low at about 20%, which implies that larger datasets and more features might be necessary to enhance the predictions. Time management, motivation, and staying in touch are such variables that the research holds key to determining results for students. The results are important not only for online learning platforms but also for interventions suggested, which include early warning systems, personalised learning paths, and elements of gamification to support performance for improved student support at the right time for those most at risk. Future studies should consider more sophisticated machine learning models and the use of more objective performance data in their attempt to make more refined predictions.

Depression Symptoms Moderate Associations between Daily Functioning and Neuropsychological Performance in Mexican American Adults.

The influence of depression on the relationship between cognition and daily living in aging Mexican American adults requires further investigation, given projected trends in general growth as well as prevalence of depression and Alzheimer's disease and related dementias (ADRD) in this population. In the present study, we examined the extent to which depression (assessed using the Geriatric Depression Scale - 15 item version) moderated the association between reported daily functioning (assessed using the Functional Activities Questionnaire [FAQ]) and objective neuropsychological performance in aging Mexican American adults in the National Alzheimer's Coordinating Center cohort. Worse reported daily functioning (higher FAQ scores) was associated with worse memory and language performance among participants without elevated depression symptoms (ps < .05), but no associations were observed among those with elevated symptoms (ps > .05). Moreover, depression did not moderate associations of reported daily functioning with performance in attention and working memory or executive functioning and processing speed (ps > .05). Aging Mexican American adults with elevated depression symptoms may have subjective informant reports of daily functioning that are discrepant from their objective performance on formal tests of memory and language. Within the context of neuropsychological evaluations for ADRD, failure to adequately assess mood to inform the nature of cognitive and/or functional changes could result in misdiagnosis and lead to delayed, premature, and/or improper intervention with this population.

EHealth Communication Intervention to Promote Human Papillomavirus Vaccination Among Middle-School Girls: Development and Usability Study.

As the age of initiating sexual intercourse has gradually decreased among South Korean adolescents, earlier vaccination of adolescents for human papillomavirus (HPV) is necessary before their exposure to HPV. Health communication includes "cues to action" that lead to preventive health behaviors, and recently, social networking services, which operate with fewer time and space constraints, have been used in various studies as a form of eHealth communication. This study aims to investigate the feasibility and usability of an eHealth communication intervention for HPV vaccination in middle-school girls aimed at the girls and their mothers. The eHealth communication intervention for HPV vaccination was developed using a 6-step intervention mapping process: needs assessments, setting program outcomes, selection of a theory-based method and practical strategies, development of the intervention, implementation plan, and testing the validity of the intervention. A review of 10 studies identified effective health communication messages, delivery methods, and theories for HPV vaccination among adolescents. Barriers including low knowledge, perceived threat, and the inconvenience of taking 2 doses of the vaccine were identified through focus groups, suggesting a need for youth-friendly and easy-to-understand information for adolescents delivered via mobile phones. The expected outcomes and the performance objectives are specifically tailored to reflect the vaccination intention. Behavior change techniques were applied using trusted sources and a health belief model. Health messages delivered through a KakaoTalk chatbot improved awareness and self-efficacy. Quality control was ensured with the use of a log system. The experts' chatbot usability average score was 80.13 (SD 8.15) and the average score of girls was 84.06 (SD 7.61). Future studies need to verify the effectiveness of health communication strategies in promoting HPV vaccination and the effectiveness of scientific intervention using a chatbot as a delivery method for the intervention.

A trophic modelling framework: Key metrics for the ecological assessment of artificial structures

As the global population expands, marine coastal ecosystems face mounting pressures from human activities, that have led to habitat deterioration and dwindling fishery resources. In this context, Artificial Reefs (ARs) have emerged as one of the promising solutions. They are generally implemented to provide habitat, to create a protective, physical boundary, to support sustainable fisheries and to facilitate ecosystem rehabilitation. Evaluating their ecological performance is crucial to ensuring they meet their objectives. Initially, assessment relied on comparing ARs to natural reefs using mainly ecological metrics which focused on fish assemblage and dynamics. Despite there being more research and documentation on effectiveness today, assessing ARs remains challenging due to the number of environmental factors that can affect the ecological systems. Moreover, ecological studies mainly used metrics that investigated the reef fish populations or ecological metrics such as fish assemblages or trophic structure that are often overlooked in studies that primarily focus on commercial fishery dynamics. Therefore, new ways of assessing artificial reef performance and the set-up of comprehensive metrics which integrate this level of complexity are needed. In this study, we focused on the "Rade de Cherbourg" in the English Channel, employing a trophic modeling approach using Ecopath with Ecosim (EwE). The study emphasizes the importance of Ecological Network Analysis (ENA) metrics for evaluating changes in the systems’ properties—such as complexity, flow diversity, and recycling capacity— which result from AR implementation. Furthermore, we identified which metrics are suitable for assessing specific AR objectives. The proposed metrics serve as a command-and-control tool for AR site managers, enabling them to evaluate the performance of each AR objective effectively. With the anticipated increase in AR projects, especially those which compensate for human impact like the Cherbourg ARs, this research offers valuable insights and future perspectives to continuously improve the ecological performance of ARs.

BGF-YOLOv10: Small Object Detection Algorithm from Unmanned Aerial Vehicle Perspective Based on Improved YOLOv10

With the rapid development of deep learning, unmanned aerial vehicles (UAVs) have acquired intelligent perception capabilities, demonstrating efficient data collection across various fields. In UAV perspective scenarios, captured images often contain small and unevenly distributed objects, and are typically high-resolution. This makes object detection in UAV imagery more challenging compared to conventional detection tasks. To address this issue, we propose a lightweight object detection algorithm, BGF-YOLOv10, specifically designed for small object detection, based on an improved version of YOLOv10n. First, we introduce a novel YOLOv10 architecture tailored for small objects, incorporating BoTNet, variants of C2f and C3 in the backbone, along with an additional small object detection head, to enhance detection performance for small objects. Second, we embed GhostConv into both the backbone and head, effectively reducing the number of parameters by nearly half. Finally, we insert a Patch Expanding Layer module in the neck to restore the feature spatial resolution. Experimental results on the VisDrone-DET2019 and UAVDT datasets demonstrate that our method significantly improves detection accuracy compared to YOLO series networks. Moreover, when compared to other state-of-the-art networks, our approach achieves a substantial reduction in the number of parameters.

Identifying vehicle characteristic target based on subjective ratings from drivers

In virtual tire development, defining vehicle characteristic target is crucial to achieving the desired vehicle performance with the equipped tires. Vehicle performance is generally evaluated through subjective ratings by skilled drivers following SAE J1441 guidelines, as well as the objective performance metrics obtained via vehicle dynamics analysis. However, due to the scarcity and bias in real-world testing dataset, the vehicle characteristic target cannot be determined definitively. To address this issue, we propose a recommendation system that searches the unexplored vehicle characteristic domains. This suggested method provides virtual subjective ratings to enrich the limited real-world dataset from the skilled drivers. Moreover, we highlight the correlation between subjective and objective data using vehicle and tire dynamics simulation software. Based on this augmented data, we predict the vehicle characteristic target according to the subjective rating requirements. The proposed approach was validated through various experiments involving skilled drivers and various vehicle-tire combinations, showing 91.26% to 96.2% consistency in generated steering parameter domains. Domains of stability parameters show that one fewer domain was generated by a conservative driver compared to more lenient driver, which confirms the robustness of the proposed approach in accommodating individual driver preferences.

YOLO-DroneMS: Multi-Scale Object Detection Network for Unmanned Aerial Vehicle (UAV) Images

In recent years, research on Unmanned Aerial Vehicles (UAVs) has developed rapidly. Compared to traditional remote-sensing images, UAV images exhibit complex backgrounds, high resolution, and large differences in object scales. Therefore, UAV object detection is an essential yet challenging task. This paper proposes a multi-scale object detection network, namely YOLO-DroneMS (You Only Look Once for Drone Multi-Scale Object), for UAV images. Targeting the pivotal connection between the backbone and neck, the Large Separable Kernel Attention (LSKA) mechanism is adopted with the Spatial Pyramid Pooling Factor (SPPF), where weighted processing of multi-scale feature maps is performed to focus more on features. And Attentional Scale Sequence Fusion DySample (ASF-DySample) is introduced to perform attention scale sequence fusion and dynamic upsampling to conserve resources. Then, the faster cross-stage partial network bottleneck with two convolutions (named C2f) in the backbone is optimized using the Inverted Residual Mobile Block and Dilated Reparam Block (iRMB-DRB), which balances the advantages of dynamic global modeling and static local information fusion. This optimization effectively increases the model’s receptive field, enhancing its capability for downstream tasks. By replacing the original CIoU with WIoUv3, the model prioritizes anchoring boxes of superior quality, dynamically adjusting weights to enhance detection performance for small objects. Experimental findings on the VisDrone2019 dataset demonstrate that at an Intersection over Union (IoU) of 0.5, YOLO-DroneMS achieves a 3.6% increase in mAP@50 compared to the YOLOv8n model. Moreover, YOLO-DroneMS exhibits improved detection speed, increasing the number of frames per second (FPS) from 78.7 to 83.3. The enhanced model supports diverse target scales and achieves high recognition rates, making it well-suited for drone-based object detection tasks, particularly in scenarios involving multiple object clusters.

Target-specified reference-based deep learning network for joint image deblurring and resolution enhancement in surgical zoom lens camera calibration

Background and objectiveFor the augmented reality of surgical navigation, which overlays a 3D model of the surgical target on an image, accurate camera calibration is imperative. However, when the checkerboard images for calibration are captured using a surgical microscope having high magnification, blur owing to the narrow depth of focus and blocking artifacts caused by limited resolution around the fine edges occur. These artifacts strongly affect the localization of corner points of the checkerboard in these images, resulting in inaccurate calibration, which leads to a large displacement in augmented reality. To solve this problem, in this study, we proposed a novel target-specific deep learning network that simultaneously enhances both the blur and spatial resolution of an image for surgical zoom lens camera calibration. MethodsAs a scheme of an end-to-end convolutional deep neural network, the proposed network is specifically intended for the checkerboard image enhancement used in camera calibration. Through the symmetric architecture of the network, which consists of encoding and decoding layers, the distinctive spatial features of the encoding layers are transferred and merged with the output of the decoding layers. Additionally, by integrating a multi-frame framework including subpixel motion estimation and ideal reference image with the symmetric architecture, joint image deblurring and enhanced resolution were efficiently achieved. ResultsFrom experimental comparisons, we verified the capability of the proposed method to improve the subjective and objective performances of surgical microscope calibration. Furthermore, we confirmed that the augmented reality overlap ratio, which quantitatively indicates augmented reality accuracy, from calibration with the enhanced image of the proposed method is higher than that of the previous methods. ConclusionsThese findings suggest that the proposed network provides sharp high-resolution images from blurry low-resolution inputs. Furthermore, we demonstrate superior performance in camera calibration by using surgical microscopic images, thus showing its potential applications in the field of practical surgical navigation.

Investigating the impact of mental rehearsal on prefrontal and motor cortical haemodynamic responses in surgeons using optical neuroimaging.

Inadequate exposure to real-life operating can impede timely acquisition of technical competence among surgical residents, and is a major challenge faced in the current training climate. Mental rehearsal (MR)-the cognitive rehearsal of a motor task without overt physical movement-has been shown to accelerate surgical skills learning. However, the neuroplastic effect of MR of a complex bimanual surgical task is unknown. The aim of this study is to use functional near-infrared spectroscopy (fNIRS) to assess the impact of MR on prefrontal and motor cortical activation during a laparoscopic knot tying task. Twelve surgical residents performed a laparoscopic knot tying task before and after either mental rehearsal (MR, intervention group) or textbook reading (TR, control group). In both groups, fNIRS was used to measure changes in oxygenated hemoglobin concentration (HbO2) in the prefrontal (24 channels) and motor cortices (22 channels). Technical performance was measured using leak volume, objective performance score and task progression score. MR led to a decrease in HbO2 (reduced activation) in the bilateral prefrontal cortex (PFC), and an increase in HbO2 (increased activation) in the left middle frontal gyrus, left precentral gyrus, and left postcentral gyrus. No discernible changes in activation were observed after TR in either the PFC or motor cortex. Moreover, smaller ΔHbO2 responses in the right PFC and greater ΔHbO2 responses in the left motor cortex were observed in the MR group compared with the TR group. Leak volume was significantly less following MR (p = 0.019), but not after TR (p = 0.347). Mean objective performance score was significantly higher following MR compared with TR (p = 0.043). Mental rehearsal may enhance surgical skill acquisition and technical proficiency by reducing utilization of attentional resources in the prefrontal cortex and improving neural efficiency in motor areas during a laparoscopic surgical task.

Perceptions and contradictions: a study of market orientation and innovation among Cuban entrepreneurs

PurposeThis study aims to examine whether market orientation or innovation is a more significant mediator between an entrepreneur’s culture and perceived success in Cuba, where innovation is highly regarded because of economic challenges and US embargoes, but doubts persist about market orientation because of its socialist economy.Design/methodology/approachHavana entrepreneurs were surveyed on culture, market orientation and innovation. Data were analyzed using structural equation modeling with the partial least squares approach in SmartPLS 4.FindingsThe results showed partial mediation for both market orientation and innovation. Culture significantly affects performance directly. Surprisingly, a less market-oriented culture enhances performance, with innovation acting as the stronger mediator, despite the inverse relationship between market orientation and innovation.Research limitations/implicationsThe limitations include reliance on self-reported measures, lack of objective performance verification and a narrow focus on entrepreneurs rather than clients. Despite these constraints, the study provides valuable exploratory insights into Cuba’s closed economy, following the principle of being “approximately right than exactly wrong.”Practical implicationsThe findings confirm the positive impact of Cubans’ innovative spirit on performance but reveal a contradiction: less market-oriented cultures perceive themselves as market-oriented and perform better. This misperception suggests the need for further investigation and training to promote market-oriented business culture. Collaboration with international business schools may be necessary, as this distortion could harm customer satisfaction.Originality/valueThe originality of the research lies in that, to the best of the authors’ knowledge, this is the first study on Cuban entrepreneurs’ market orientation. It has the value to illustrate how socialists’ ideology can contradict assumed theoretical established approaches about market functioning.

Development of Self-Powered Prosthetic Finger with Pneumatic Passive Joints for Distal Interphalangeal Joint Amputees

Myoelectric prosthetic hands and fingers with grasping functions have challenges such as weight, cost, and grasping performance of flexible objects owing to the use of electric actuators. To resolve these problems, we propose a self-powered movable prosthetic finger using pneumatic pressure. This prosthetic finger utilizes the flexion/extension of the remaining finger of the user to drive a wire and flex or extend the finger joints. The use of a tendon (wire) and belt drives reduces the space occupied by the prosthetic finger unit compared with conventional linked prosthetic fingers. Furthermore, a sealed-air bellows is used for the joint for flexible grasping owing to passive variable-compliance and damping, which is impossible with only a tendon drive. These features have resulted in the stable grasping of various objects that are difficult to grasp using prosthetic fingers based on conventional technologies.