传统企业广告投入与企业绩效的非线性关系研究——基于工业企业数据的门槛分析

北京邮电大学学报(社科版) ›› 2016, Vol. 18 ›› Issue (4): 92-100.

传统企业广告投入与企业绩效的非线性关系研究——基于工业企业数据的门槛分析

北京邮电大学经济管理学院，北京100876

收稿日期:2016-03-13 出版日期:2016-08-31

Nonlinear Relationship Between Traditional Advertising Investment and Enterprise Performance#br# ——Threshold Analysis Based on Industrial Enterprises Data

School of Economics and Management, Beijing University of Posts and Telecommunications,
Beijing 100876, China

Received:2016-03-13 Online:2016-08-31

摘要/Abstract

摘要：

广告支出的决策，是现代企业营销活动中十分重要的一个环节。在资源总量一定的情况下，如果广告费用投入过多，势必会影响企业在其他方面的支出，此时过量的广告投入反而不利于企业经营绩效的改善。采用门槛回归的方法，验证广告费用投入强度与企业绩效之间并不是简单的线性关系，传统企业广告投入对经营绩效的影响呈现出明显的阶段性特征，并且广告投入强度的最佳区间范围是［0,00463］，在此范围内广告投入的效用达到最大。高技术产业制造业由于企业特性决定其广告投入强度的最佳区间范围是［0,00275］，小于普通制造业。

关键词: 广告投入, 企业绩效, 门槛回归

Abstract:

Making advertising investment decisions is an important step of modern enterprise marketing activities In the case of a certain amount of resource, if advertising costs too much, it will definitely affect spending in other areas In that situation, excessive advertising will be detrimental to the business performance improvement Therefore, this paper adopts threshold regression and verifies that there is not a simple linear relationship between advertisement investment intensity and firm performance The impact of traditional advertisement investment on enterprises operating performance shows a clear stage characteristic What’s more, the best range of advertising investment is ［0, 00463］ In other words, advertising investment maximizes its effectiveness in this range The best rang of high technology industry manufacturing is ［0,00275］, less than ordinary manufacturing, which is determined by its enterprise features

Key words: advertising investment, business performance, threshold regression

中图分类号:

F2725

赵保国,阙人超. 传统企业广告投入与企业绩效的非线性关系研究——基于工业企业数据的门槛分析[J]. 北京邮电大学学报(社科版), 2016, 18(4): 92-100.

ZHAO Bao-guo, QUE Ren-chao. Nonlinear Relationship Between Traditional Advertising Investment and Enterprise Performance#br# ——Threshold Analysis Based on Industrial Enterprises Data[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 18(4): 92-100.

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