Can fuzzy Multi-Criteria Decision Making improve Strategic planning by balanced scorecard?

Strategic management is momentous for organizational success and competitive advantage in an increasingly turbulent business environment. Balanced scorecard (BSC) is a framework for evaluating strategic management performance which translates strategy into action via various sets of performance measurement indicators. The main objective of this research is to develop a new fuzzy group Multi-Criteria Decision Making (MCDM) model for strategic plans selection process in the BSC. For this to happen, the current study has implemented linguistic extension of MCDM model for robust selection of strategic plans. The new linguistic reasoning for group decision making is able to aggregate subjective evaluation of the decision makers and hence create an opportunity to perform more robust strategic plans, despite of the vagueness and uncertainty of strategic plans selection process. A numerical example demonstrates possibilities for the improvement of BSC through applying the proposed model

Using crowdsourcing tools for implementing open strategy: A case study in education

Following critiques on the conventional methods of strategic planning, and the stream of research on the effect of more participation on the success of strategy process, the new concept of open strategy has been introduced to the literature. Based on the notion of open innovation, this new concept covers two principles of inclusiveness and transparency. The current study introduces an in-progress case study of using the crowdsourcing model to implement the open strategy concept in an Australian university. We use the principles of Design Science Research Methodology (DSRM) for open strategic planning by using the crowdsourcing model and evaluate the method by comparing the quality of resultant plan in delivering its objective. This study explains our approach and a conceptual design for the proposed method as well as our plan for conducting future phases of the research. The introduced process can be used in similar practices of open strategic planning

A Review of Critical Factors for Communicating With Customers on Social Networking Sites

This paper undertakes a systematic review to gain insight into existing studies on the application of Social Network Sites (SNS). Our systematic review of studies from 1995 to 2012 examines the background and trend of research in the area and provides critical factors that organizations should consider for effectively use social networking sites to communicate with their customers. We note a huge growth in the number of academic papers on the topic since 1998. Seventeen factors were identified as a result of review, which shaped two main themes: (i) A customercentric organizational culture, and (ii) SNS Know-How. The findings show that for a successful and effective use of SNSs, and in particular Facebook, a combination of good understanding of SNSs tools and capabilities as well as a constant and transparent relationship with customers are essential. The findings show that for a successful and effective use of SNSs, and in particular Facebook, a combination of good understanding of SNSs tools and capabilities as well as a constant and transparent relationship with customers are essential

Strategic information system planning in healthcare organizations

Copyright © 2015, IGI Global. The healthcare industry is a critical and growing part of economies worldwide. To provide better quality of care, and value for money, billions of dollars are being spent on bettering information systems in healthcare organizations. Strategic Information System Planning (SISP) is instrumental in making informed decisions to achieve the health organizations' goals and objectives. This paper undertakes a systematic review to gain insight into existing studies on SISP in healthcare organizations. Our systematic review of papers on SISP from 1985 to 2011 examines the background and trend of research into SISP in the healthcare industry, classification of topics in SISP, as well as sets of tools and guidelines to aid practitioners and the research community alike

Empirical Evaluation of the Influence of EMR Alignment to Care Processes on Data Completeness

Data completeness is an important dimension of data quality in electronic medical records (EMR). There are many constructs that influence data completeness in EMR. In this paper, we investigate three of these constructs: Clinical staff participation, EMR integration, and EMR alignment to care processes. We use these constructs from related studies as theoretical support to propose a conceptual model of factors influencing data completeness in EMR. The conceptual model is empirically validated using a survey with clinical staff participants. The results reveal that a high level of clinical staff’s participation influences the data completeness in EMR. Furthermore, the alignment of EMR to the care processes has an impact on the data completeness in EMR

Empirical study of Data Completeness in Electronic Health Records in China

Background: As a dimension of data quality in electronic health records (EHR), data completeness plays an important role in improving quality of care. Although many studies of data management focus on constructing the factors that influence data quality for the purpose of quality improvement, the constructs that are developed for interpreting factors influencing data completeness in the EHR context have received limited attention. Methods: Based on related studies, we constructed the factors influencing EHR data completeness in a conceptual model. We then examined the proposed model by surveying clinical practitioners in China. Results: Our results show that the data quality management literature can serve as a starting point to derive a conceptual model of factors influencing data completeness in the EHR context. This study also demonstrates that “resources” should be added as a factor that influences data completeness in EHR. Conclusion: Our resulting conceptual model shows a substantial explanation of data completeness in EHR assessed in this study. Although the proposed relationships between the included factors were previously supported in the literature, our work provides the beginning empirical evidence that some relationships may not be always significantly supported. The possible explanation of these differences has been discussed in the present research. This study thus benefits decision makers and EHR program managers in implementing EHR as well as EHR vendors in the EHR integration by addressing data completeness issues

Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables

© 2018 Elsevier B.V. Background: The present study aims to identify the patients at risk of type 2 diabetes (T2D). There is a body of literature that uses machine learning classification algorithms to predict development of T2D among patients. The current study compares the performance of these classification algorithms to identify patients who are at risk of developing T2D in short, medium and long terms. In addition, the list of predictor variables important for prediction for T2D progression is provided. Methods: This study uses 10,911 records generated in 36 clinics from the 15th of November 2008–15th of November 2016. Syntactic minority oversampling and random under sampling were used to create a balanced dataset. The performance of Neural Networks, Support Vector Machines, Decision Tress and Logistic Regression to identify patients developing T2D in short, medium and long terms was compared. The measures were Area Under Curve, Sensitivity, Specificity, Matthew correlation coefficient and Mean Calibration Error. Through importance analysis and information fusion techniques the predictors of developing T2D were identified for short, medium and long-term risk analysis. Results: The findings show that the performance of analytics techniques depends on both period and purpose of prediction whether the prediction is to identify people who will not develop T2D or to determine at risk patients. Oversampling as opposed to under sampling improved performance. 16 predictors and their importance to determine patients at risk of T2D in short, medium and long terms were identified. Conclusions: This study provides guidelines for an automated system to prompt patients for screening. Several predictors are reportable by patients, others can be examined by physicians or ordered for further lab examination, which offers a potential reduction of the burden placed upon the clinical settings

Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment.

BACKGROUND:The literature in statistics presents methods by which autocorrelation can identify the best period of measurement to improve the performance of a time-series prediction. The period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a limitation to the length of the measurement period that can offer meaningful and valuable predictions. OBJECTIVE:This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts. METHODS:The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. The disease counts for chlamydia, salmonella, respiratory syncytial virus, gonorrhea, viral meningitis, and influenza A were predicted. RESULTS:Our results showed that autocorrelation could not guarantee the best performance for prediction of disease counts. However, the proposed method with the change-point analysis suggests a period of measurement that is operationally acceptable and performance that is not significantly different from the best prediction. CONCLUSIONS:The use of change-point analysis with autocorrelation provides the best and most practical period of measurement