Hierarchical relationships of building partnership competency: the use of nominal group technique and interpretive structural modelling

IF 1.8 Q3 MANAGEMENT
Noraini Abdul Latiff, K. Hoque, M. F. A. Ghani
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引用次数: 0

Abstract

Purpose This paper aims to determine the hierarchical relationship between building partnership competencies for public sector educational leaders (ELs) administering and running the education system. Design/methodology/approach An interpretive structural modelling (ISM) technique was used to develop a hierarchical structural model for building partnership competencies. Nominal group technique (NGT) was used with the help of experts’ suggestions and opinions at the beginning of ISM to identify building partnership competencies. Also, the NGT was used to rank the competencies. A structural self-integration matrix was developed based on experts’ voting and agreement. Cross-impact matrix multiplication applied to classification (MICMAC) analysis was used to analyse the relationship among the building partnership competencies. A total of 11 experts were chosen for NGT and ISM sessions. Findings A total of 16 building partnership competencies were identified for this study. The competencies were compartmentalised into four domains: creative collaboration, create network, develop collective culture and encouraging constructive dialogue. MICMAC analysis shows each domain of the model of its key competencies ranked at the highest level in the ISM model and dependent competencies. Research limitations/implications ISM is a modelling approach that is based solely on expert opinions and responses. Its limitation can be overcome with the help of empirical analysis. Practical implications This study supports the public sector ELs’ professional development and upskilling. In addition, the model developed in the study will be helpful for stakeholders, human resources division and policymakers to incorporate building partnership competencies in the training and development of ELs. Originality/value This study helps to identify and prioritise building partnership competencies using NGT and ISM. Literature shows that numerous authors have used the ISM approach. Still, the combination of NGT approach is limited. Therefore, the model developed in the study was based solely on experts’ opinions and suggestion based on their experiences and knowledge.
建立伙伴关系能力的层次关系:名义小组技术和解释结构模型的使用
目的本文旨在确定公共部门教育领导者(EL)管理和运行教育系统的伙伴关系能力之间的层级关系。设计/方法论/方法解释性结构建模(ISM)技术用于开发建立伙伴关系能力的层次结构模型。名义小组技术(NGT)是在ISM开始时在专家建议和意见的帮助下使用的,以确定建立伙伴关系的能力。此外,NGT用于对能力进行排名。在专家投票和同意的基础上,制定了一个结构自整合矩阵。交叉影响矩阵乘法应用于分类(MICMAC)分析用于分析建立伙伴关系能力之间的关系。NGT和ISM会议共选出11名专家。研究结果本研究共确定了16项建立伙伴关系的能力。这些能力被划分为四个领域:创造性合作、创建网络、发展集体文化和鼓励建设性对话。MICMAC分析显示了模型的每个领域的关键能力在ISM模型中排名最高,以及相关能力。研究局限性/含义ISM是一种仅基于专家意见和回应的建模方法。它的局限性可以通过实证分析加以克服。实践意义本研究支持公共部门ELs的专业发展和技能提升。此外,该研究中开发的模型将有助于利益相关者、人力资源部门和政策制定者将建立伙伴关系能力纳入ELs的培训和发展。Originality/value该研究有助于确定并优先考虑使用NGT和ISM建立伙伴关系能力。文献表明,许多作者都使用了ISM方法。然而,NGT方法的组合是有限的。因此,研究中开发的模型完全基于专家根据他们的经验和知识提出的意见和建议。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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