销售绩效评分与关键跨职能因素的计算:IT/ITES中的绩效动态

IF 1.1 Q4 BUSINESS
S. Saha, Subhasree Kar
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引用次数: 0

摘要

本研究的目的是通过探索性因素分析(EFA)来识别跨职能因素及其影响,特别是在B2B背景下,并构建一个模型来解释和量化对IT/ITES公司的影响(销售绩效评分)。设计/方法/方法为公司在行业中的当前地位提供答案,这对制定战略非常重要,尤其是对销售团队而言。很少有学术研究指明跨职能销售因素的方向,但要回答我们是否可以量化销售业绩,并确定数字基准值是什么,是困难的。对于公司来说,了解需要关注哪些跨职能因素以及何时关注也是困难的。研究结果:经过1079 +文献研究,包括25个在之前的研究中强烈使用的前因,以及8个以上的焦点小组研究、试点研究和与行业领导者的讨论,在10个月的时间里,从310名销售专业人员、90多家IT公司收集了35个问题,涉及33个指标。使用“马氏距离检验”进行多变量分析,去除三个样本作为异常值,使用“缺失值非随机”(MNAR)删除两个变量。最终15个跨职能销售绩效指标的决定因素形成4个可靠性非常高的最佳因素,经过EFA形成未来的形成模型和销售绩效评分。(1)本研究未分析调节效应和中介效应。(2)本研究是最终模型构建的前奏。(3)由于经济衰退,全球流行病,恐怖主义,地震,战争等原因导致的业务下降,在此分析和研究中没有考虑。只考虑和分析了自然业务下降的跨职能原因。(4)经过模型形成、调整和验证性因子分析,计算出准确的“销售绩效评分(SPS)”。(1)本研究的主要意义在于IT/ITES公司。对他们来说,将销售业绩量化并科学地衡量是很容易的。(2)在公司销售业绩下滑的情况下,有一种方法来衡量、预测和采取可衡量的行动。(3)公司的高层管理人员也会提前知道这种影响,以便他们制定适当的战略。在当前的情况下,这将是非常有用的,因为衡量业务成果和提前制定战略是任何公司的首要任务。专注于这些确定的因素,公司可以提高其销售业绩。作者在创建统计模型和计算销售业绩得分方面做出了贡献,基于最终因素负载值,通过量化其标准或基准值来衡量当前行业绩效,从而更好地为实现目标提供战略支持,这将是独一无二的,也是前所未有的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computation of sales performance score and key cross-functional factors: a performance dynamics in IT/ITES
PurposeThe purpose of this research is to identify the cross-functional factors and their impact after exploratory factor analysis (EFA), especially in B2B context and constructing a model to interpret and quantify the influences (sales performance score) specifically to the IT/ITES companies.Design/methodology/approachGetting answer for a corporate that where its current stand in the industry is important for the strategy making, especially for the sales team. Few academic researches charted direction toward cross-functional sales factors, but getting answer whether we can quantify that sales performance and identify what is the numeric benchmark value, is difficult. For the companies to understand the need to focus on which cross-functional factors and when, is also difficult.FindingsAfter 1,079+ literature study, concluded with the 25 antecedents strongly used in previous studies and 8 more on after focused group study, pilot study and discussion with the industry leaders, 35 questions addressing 33 indicators collected in 10 months duration from 310 sales professionals, 90+ IT companies. Three samples were removed as outliers using “Mahalanobis Distance Test” for multivariate analysis, dropped two variables by “Missing value Not at Random” (MNAR). Final 15 determinants of cross-functional sales performance indicators forming four best factors with very high reliability after EFA to form a future formative model and sales performance score.Research limitations/implications(1) In this study no moderator and mediator effect are analyzed. (2) This study is the precursor to the final model construction. (3) Business down due to recession, global pandemic, terrorism, earthquake, war etc. are not considered during this analysis and study. Only the cross-functional reasons for natural business down have been considered and analyzed. (4) Exact “Sales Performance Score (SPS)” should be calculated after model forming, adjusting and confirmatory factor analysis.Practical implications(1) The major implication of this study would be for IT/ITES companies. It will be very easy for them to quantify the sales performance and measure that scientifically. (2) There will be a way to measure, predict and take measurable actions in case sales performance of the company downfalls. (3) Also the impact will be known to the top management of the company well in advance so that they can make the proper strategy. This will be very useful in current situation when measuring business outcome and make strategy well in advance is of any company's utmost priority.Originality/valueFocusing on these identified factors companies can improve its sales performance. The authors contribute in creating a statistical model and computing a sales performance score, based on the final factor loading values, would be unique and unprecedented to measure the current industry performance by quantifying its standard or benchmark value for better strategic support toward the achievement of targets.
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