scholarly journals Entrepreneurial Orientation, Innovation and Performance of Value-system Actors

2021 ◽  
Vol 8 (2) ◽  
pp. 50
Author(s):  
Simon Kamuri
Keyword(s):  
Entrepreneurial Orientation ◽  
Principal Component ◽  
Decision Makers ◽  
Leather Industry ◽  
Value System ◽  
Reliability Factor ◽  
Growth Opportunity ◽  
Calculated Risk ◽  
And Performance ◽  
Instrument Reliability

The purpose of this study was to explore entrepreneurial orientation as a cognitive construct attributable to individuals and its relationship with innovation and performance from an industry ecosystem perspective. The study adopted a mixed design approach involving exploration of the factors and a diagnosis of their hypothesized relationships. A mixed sampling of members of a leather industry association and the linked industry institutions was carried out with a 76% response rate achieved. Quantitative data was collected from key decision-makers as informants of firms in Kenya’s leather industry using a questionnaire for guided interviews. The Delphi Technique and a pilot study (Cronbach’s Alpha 0.700 – 0.772) were used to establish instrument reliability. Factor analysis was performed on the study variables using Principal Component Analysis before inferential analysis. Entrepreneurial orientation showed validity as a second-order latent construct comprising three cognitive dimensions, namely vision for growth, opportunity recognition and calculated risk-taking. Entrepreneurial orientation and its antecedents were established as determinants of performance of value-system actors in an industry (R2=0.422, F=13.417, p=0.000). It further showed that this relationship is partially mediated by innovation by the firms (Sobel test Z-value = 3.30449610, p=0.00095147). The study recommends extension of this research to other industries.

scholarly journals An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuanyuan Xu ◽  
Genke Yang ◽  
Jiliang Luo ◽  
Jianan He
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Electronic Component ◽  
Support Vector ◽  
Learning Networks ◽  
Component Recognition ◽  
And Performance

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6   level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

CAN BAYESIAN METHODS MAKE DATA AND ANALYSES MORE RELEVANT TO DECISION MAKERS?

2001 ◽  
Vol 17 (1) ◽  
pp. 114-122 ◽  
Author(s):  
Steven H. Sheingold
Keyword(s):  
Decision Making ◽  
Bayesian Methods ◽  
Paradigm Shift ◽  
Relevant Information ◽  
Decision Makers ◽  
Evidence Based ◽  
Critical Importance ◽  
Bayesian Approaches ◽  
Bayesian Analyses ◽  
And Performance

Decision making in health care has become increasingly reliant on information technology, evidence-based processes, and performance measurement. It is therefore a time at which it is of critical importance to make data and analyses more relevant to decision makers. Those who support Bayesian approaches contend that their analyses provide more relevant information for decision making than do classical or “frequentist” methods, and that a paradigm shift to the former is long overdue. While formal Bayesian analyses may eventually play an important role in decision making, there are several obstacles to overcome if these methods are to gain acceptance in an environment dominated by frequentist approaches. Supporters of Bayesian statistics must find more accommodating approaches to making their case, especially in finding ways to make these methods more transparent and accessible. Moreover, they must better understand the decision-making environment they hope to influence. This paper discusses these issues and provides some suggestions for overcoming some of these barriers to greater acceptance.

Jumping to conclusions – an analysis of the NBA Draft Combine athleticism data and its influence on managerial decision-making

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tobias Berger ◽  
Frank Daumann
Keyword(s):  
Decision Making ◽  
Design Methodology ◽  
Predictive Power ◽  
Principal Component ◽  
Decision Makers ◽  
Decision Quality ◽  
Managerial Behavior ◽  
Managerial Decision ◽  
Content Type ◽  
Selection Behavior

PurposeThe NBA Draft policy pursues the goal to provide the weakest teams with the most talented young players to close the gap to the superior competition. But it hinges on appropriate talent evaluation skills of the respective organizations. Research suggests the policy might be valid but to date unable to produce its intended results due to the “human judgement-factor”. This paper investigates specific managerial selection-behavior-influencing information to examine why decision-makers seem to fail to constantly seize the opportunities the draft presents them with.Design/methodology/approachAthleticism data produced within the NBA Draft Combine setting is strongly considered in the player evaluations and consequently informs the draft decisions of NBA managers. Curiously, research has failed to find much predictive power within the players pre-draft combine results for their post-draft performance. This paper investigates this clear disconnect, by examining the pre- and post-draft data from 2000 to 2019 using principal component and regression analysis.FindingsEvidence for an athletic-induced decision-quality-lowering bias within the NBA Draft process was found. The analysis proves that players with better NBA Draft Combine results tend to get drafted earlier. Controlling for position, age and pre-draft performance there seems to be no proper justification based on post-draft performance for this managerial behavior. This produces systematic errors within the structure of the NBA Draft process and leads to problematic outcomes for the entire league-policy.Originality/valueThe paper delivers first evidence for an athleticism-induced decision-making bias regarding the NBA Draft process. Informing future selection-behavior of managers this research could improve NBA Draft decision-making quality.

scholarly journals Assessment of the Relationship between Involvement, Entrepreneurial Orientation, and Performance of Christian Faith based Hotels in Kenya

2019 ◽  
Vol 8 (4) ◽  
pp. 90-104
Author(s):  
Ruth Wanjiku Muriithi ◽  
Kyalo Teresia Ngina ◽  
Kinyanjui Josphat Kamau
Keyword(s):  
Entrepreneurial Orientation ◽  
Organization Culture ◽  
Christian Faith ◽  
P Value ◽  
Cross Sectional Survey ◽  
Faith Based ◽  
Important Relationship ◽  
Significant Change ◽  
And Performance ◽  
The Relationship

The purpose of this study was to determine the relationship between involvement, Entrepreneurial orientation and performance of Christian Faith-Based Hotels (CFBHs) in Kenya. Involvement is the extent to which subordinate staff feels a sense of ownership and responsibility to the organization. Involvement has captured interest throughout the last decade because of its important relationship between the concept itself and its outcomes such as gaining competitive advantage and performance. Entrepreneurial Orientation is key as it determines the success or failure of Christian Faith-Based Hotels. The study was guided by the use of the Denison’s organizational model and used the mixed methods approach guided by a cross-sectional survey research design. The population of the study included 72 managers and 1878 subordinate staff from 24 Christian Faith-Based Hotels in Kenya and the sample size comprised 394 respondents. The data were analyzed using descriptive and inferential statistics. Findings revealed that organization culture involvement significantly influences the performance of Christian Faith-Based Hotels in Kenya. The dimensions of involvement Empowerment, Team Orientation, and Capability development were all found to have significant influences on performance in all critical ratios 2.829, 2.301 and 3.502 respectively which are all greater than the 1.96 Z score at 5% level of significance. Entrepreneurial orientation was also found to significantly moderate the relationship between organization culture involvement significantly influences the performance of Christian Faith-Based Hotels in Kenya. A significant change in R-square of 0.063 was found due to the inclusion of interaction terms between involvement dimensions and entrepreneurial orientation. The p-value of the F-change due to the change in R-square was found to be 0.043 implying a significant change but consequently, there is a significant moderating effect.

scholarly journals Development of Profiling Methods for Contraband Firearm Volatile Odor Signatures

2022 ◽  
Vol 1 ◽  
Author(s):  
Kirsten Nettles ◽  
Cameron Ford ◽  
Paola A. Prada-Tiedemann
Keyword(s):  
Law Enforcement ◽  
National Security ◽  
Solid Phase ◽  
Headspace Analysis ◽  
Principal Component ◽  
Gas Chromatography Mass Spectrometry ◽  
Security Threat ◽  
Local Law Enforcement ◽  
Mass Shooting ◽  
And Performance

The early detection and location of firearm threats is critical to the success of any law enforcement operation to prevent a mass shooting event or illegal transport of weapons. Prevention tactics such as firearm detection canines have been at the front line of security tools to combat this national security threat. Firearm detection canines go through rigorous training regimens to achieve reliability in the detection of firearms as their target odor source. Currently, there is no scientific foundation as to the chemical odor signature emitted from the actual firearm device that could aid in increased and more efficient canine training and performance protocols or a better understanding of the chemistry of firearm-related odorants for better source identification. This study provides a novel method application of solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) as a rapid system for the evaluation of odor profiles from firearm devices (loaded and unloaded). Samples included magazines (n = 30) and firearms (n = 15) acquired from the local law enforcement shooting range. Headspace analysis depicted five frequently occurring compounds across sample matrices including aldehydes such as nonanal, decanal, octanal and hydrocarbons tetradecane and tridecane. Statistical analysis via principal component analysis (PCA) highlighted a preliminary clustering differentiating unloaded firearms from both loaded/unloaded magazines and loaded firearm devices. These results highlight potential odor signature differences associated with different firearm components. The understanding of key odorants above a firearm will have an impact on national security efforts, thereby enhancing training regimens to better prepare canine teams for current threats in our communities.

scholarly journals A Model to Evaluate the Organizational Readiness for Big Data Adoption

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Javaneh Ramezani ◽  
Mahdi Nasrollahi
Keyword(s):  
Principal Component Analysis ◽  
Big Data ◽  
Literature Review ◽  
New Technologies ◽  
Principal Component ◽  
Organizational Readiness ◽  
Decision Makers ◽  
Comprehensive Literature Review ◽  
The Hierarchical Structure ◽  
Best Worst Method

Evaluating organizational readiness for adopting new technologies always was an important issue for managers. This issue for complicated subjects such as Big Data is undeniable. Managers tend to adopt Big Data, with the best readiness. But this is not possible unless they can assess their readiness. In the present paper, we propose a model to evaluate the organizational readiness for Big Data adoption. To accomplish this objective, firstly, we identified the criteria that impact organizational readiness based on a comprehensive literature review. In the next step using Principal Component Analysis (PCA) for criterion reduction and integration, twelve main criteria were identified. Then the hierarchical structure of criteria was developed. Further, Fuzzy Best- Worst Method (FBWM) has been used to identify the weight of the criteria. The finding enables decision-makers to appropriately choose the more important criteria and drop unimportant criteria in strengthening organizational readiness for Big Data adoption. Statistics-based hierarchical model and MCDM based criteria weighting have been proposed, which is a new effort in evaluating organizational readiness for Big Data adoption.

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