偏最小二乘结构方程建模（PLS-SEM）中的重要性-性能图组合分析（cIPMA）：SmartPLS 4 教程

IF 4 Q2 BUSINESS

Journal of Marketing Analytics Pub Date : 2024-06-04 DOI:10.1057/s41270-024-00325-y

Marko Sarstedt, Nicole F. Richter, Sven Hauff, Christian M. Ringle

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引用次数: 0

摘要

最近关于偏最小二乘结构方程建模（PLS-SEM）的研究将必要条件分析（NCA）的结果考虑在内，从而扩展了经典的重要性-绩效图分析（IPMA）。通过突出必要条件，综合重要性-绩效图分析（cIPMA）提供了一种工具，能够更好地确定管理行动的优先次序，以改善关键目标结构。在本文中，我们将展示使用 SmartPLS 4 软件进行 cIPMA 的主要步骤。我们的说明借鉴了 cIPMA 最初出版时使用的技术接受模型（TAM），该模型在商业研究中占有重要地位。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Combined importance–performance map analysis (cIPMA) in partial least squares structural equation modeling (PLS–SEM): a SmartPLS 4 tutorial

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Combined importance–performance map analysis (cIPMA) in partial least squares structural equation modeling (PLS–SEM): a SmartPLS 4 tutorial

Recent research on partial least squares structural equation modeling (PLS–SEM) extended the classic importance–performance map analysis (IPMA) by taking the results of a necessary condition analysis (NCA) into consideration. By also highlighting necessary conditions, the combined importance–performance map analysis (cIPMA) offers a tool that enables better prioritization of management actions to improve a key target construct. In this article, we showcase a cIPMA’s main steps when using the SmartPLS 4 software. Our illustration draws on the technology acceptance model (TAM) used in the cIPMA’s original publication, which features prominently in business research.

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来源期刊

Journal of Marketing Analytics BUSINESS-

CiteScore

5.40

自引率

16.70%

发文量

期刊介绍： Data has become the new ore in today’s knowledge economy. However, merely storing and reporting are not enough to thrive in today’s increasingly competitive markets. What is called for is the ability to make sense of all these oceans of data, and to apply those insights to the way companies approach their markets, adjust to changing market conditions, and respond to new competitors. Marketing analytics lies at the heart of this contemporary wave of data driven decision-making. Companies can no longer survive when they rely on gut instinct to make decisions. Strategic leverage of data is one of the few remaining sources of sustainable competitive advantage. New products can be copied faster than ever before. Staff are becoming less loyal as well as more mobile, and business centers themselves are moving across the globe in a world that is getting flatter and flatter. The Journal of Marketing Analytics brings together applied research and practice papers in this blossoming field. A unique blend of applied academic research, combined with insights from commercial best practices makes the Journal of Marketing Analytics a perfect companion for academics and practitioners alike. Academics can stay in touch with the latest developments in this field. Marketing analytics professionals can read about the latest trends, and cutting edge academic research in this discipline. The Journal of Marketing Analytics will feature applied research papers on topics like targeting, segmentation, big data, customer loyalty and lifecycle management, cross-selling, CRM, data quality management, multi-channel marketing, and marketing strategy. The Journal of Marketing Analytics aims to combine the rigor of carefully controlled scientific research methods with applicability of real world case studies. Our double blind review process ensures that papers are selected on their content and merits alone, selecting the best possible papers in this field.