回顾 SmartPLS 4 软件:最新功能和增强功能

IF 4 Q2 BUSINESS
Jun-Hwa Cheah (Jacky), Francesca Magno, Fabio Cassia
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引用次数: 0

摘要

偏最小二乘结构方程模型(PLS-SEM)是一种非常流行的多元数据分析方法。SmartPLS 3 软件程序帮助许多营销研究人员分析了潜变量(即中介、调节等)之间的复杂关系,他们通过一组观察变量来衡量这些关系。该程序直观的图形用户界面和各种功能,如新指标(如 HTMT、模型拟合指数)、高级技术(多组分析、PLSpredict)和补充技术(如确证四分法分析、重要性-绩效图分析),对许多商业学科产生了影响。SmartPLS 4 的图形用户界面经过全面改造,数据估算处理速度更快,并具有新的模型评估功能(即交叉验证预测能力测试、内生性评估和必要条件分析),是一次重大飞跃。本文回顾了 SmartPLS 4 并讨论了其各种功能,从而为研究人员提供符合其分析研究目标的具体指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Reviewing the SmartPLS 4 software: the latest features and enhancements

Reviewing the SmartPLS 4 software: the latest features and enhancements

Partial least squares structural equation modeling (PLS-SEM) is a highly popular multivariate data analysis method. The SmartPLS 3 software program helped many marketing researchers analyze the complex relationships between latent variables (i.e., mediation, moderation, etc.), which they measured by means of sets of observed variables. This program’s intuitive graphical user interface and various features, such as new metrics (e.g., HTMT, model fit indexes), advanced techniques (multigroup analysis, PLSpredict), and complementary techniques (e.g., confirmatory tetrad analysis, importance-performance map analysis), which impacted many business disciplines. SmartPLS 4 represents a significant leap forward in development with its completely revamped graphical user interface, faster processing speed for data estimation, and new model assessment features (i.e., cross-validated predictive ability test, endogeneity assessment, and a necessary condition analysis). This paper reviews SmartPLS 4 and discusses its various features, thereby providing researchers with concrete guidance that fits their analytical research goals.

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来源期刊
CiteScore
5.40
自引率
16.70%
发文量
46
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.
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