What is the decision making process for retail management

Performance measurement in retail

7 Empirically quantitative research

“Academically, knowing how perceptions of performance are formed should be useful in two senses. First, given that marketing scholars are interested in defining how marketing activities lead to marketing performance, it is important to know what performance managers are trying to maximize […]. Second, with increasing interest in developing better marketing performance measures, it is important to develop measures that are consonant with how managers actually judge performance. " (Clark 2000, 4).

The qualitative design is followed by two empirical quantitative studies: (1) A manager survey provides insights into the implementation of trading-relevant indicators in everyday trading. (2) A student survey gives insights into the benefits that are assigned to different sets of indicators.

Figure 48: Stages in the research process - quantitative design

With the help of this design, the underlying research questions will be examined from two further perspectives. The overarching research question that was formulated at the beginning of the thesis is specified in the course of this chapter by the following research questions (Table 51). ← 237 | 238 →

Table 51: Research questions (empirical quantitative design)

Main research question:
How should performance measurement be designed to support operational decisions by retail managers at the store level?
Research questions (quantitative design):
Which key figures in the sense of a performance measurement play a role in retail?

How do the contingency factors of strategy, structure and size of retail companies influence the design of performance measurement in retail?

Which sets of indicators are attractive to retail managers and to what extent? Which set is rated the most useful?

Can target groups be defined that differ from one another in the assessment of the benefits of the key figure sets?

Homburg (2007, 39-44) shows in his article "Business administration as empirical science - inventory and recommendations" the importance of the following points, which are also indicative for the studies at hand:

• Requirement 1: The relationship between the construct under consideration and its indicators in the sense of formative and reflective relationships should be explicitly operationalized. This requirement is met in both studies through theoretical derivation and subsequent visualization in the form of conceptual models.

• Requirement 2:Content validity should be represented by completeness of the indicators. An approach to this requirement is ensured through the inclusion of expert knowledge at different points. Furthermore, already established scales - if available - are used and disclosed.

• Requirement 3:Multidimensionality the constructs should be observed and complexity should be reflected. Here the challenge is met to make the research design as compact as possible and still reflect the central constructs through multi-items (Diamantopoulos et al. 2012).

• Requirement 4: Validity of the answers from key informants (Key informants)that provide information about the characteristics of the organization should be systematically ensured. The goal is to Key informant bias, i.e. avoiding systematic distortion of responses through functional and / or hierarchical positions in the company (Homburg et al. 2012b, 594). For this reason ← 238 | 239 → different management positions are used as the basis for the analysis.

• Requirement 5: If the relationship between independent and dependent variable is not due to the fact that it actually exists, but to the method used, one speaks of Common Method Bias. This error needs to be reduced. The multi-perspective approach helps to critically compare the results obtained from the studies and to check them with regard to this bias.

Finally, the quality criteria of market research with regard to implementation in the project are reflected upon again (Figure 49).

Figure 49: Quality criteria for market research (Bruhn 2012, 94; Diekmann 2012, 248–260; Kuß 2013a, 150)

The structure and results of each study are discussed separately below. The starting point is a survey of managers. ← 239 | 240 →

7.1 Management survey

The present study addresses operational trade management and extends the results of Grafton et al. (2010), who analyze the decision-making and decision-influencing functions of performance measurement combined, and Mintz / Currim (2013), who examine the influences of contingency theory on the design of sets of indicators. The aim of the present study is to show which sets of indicators are used at the operational level, which role trade managers assign to performance measurement and where there is still a need for action in terms of a consistent strategic alignment of the company. The theoretical findings from Chapter 5.4 show that there are differences in implementation and use, depending on which strategic orientation is chosen. Therefore, this construct is integrated into the model and based on Mintz / Currim (2013, 34) and Olson et al. (2005, 63). In line with the contingency theory approach, differences in structure and size are also taken into account in the analysis (Figure 50).

Figure 50: Management survey: basic model

7.1.1 Management survey: hypotheses and methodological profile

Derived from the theoretical knowledge of the respective chapters and the underlying model, the following hypotheses are formulated:

The first hypothesis (H1Mgmt) examines the strategic fit between performance measurement and the objectives of retail companies and shows whether the performance indicators used are adapted to their strategy and objectives (Chapter 3).

Structural components influence the design of performance measurement (H2Mgmt): Not only the size of the company but also the hierarchies within the company and the resulting coordination ← 240 | 241 → national difficulties are seen as declared challenges for performance measurement in the retail landscape (Chapter 4).

The connection of performance measurement with a remuneration component should lead to increased usage behavior (H3Mgmt). The decision-influencing components of performance measurement are derived from Chapter 3.3.

The main research question of the present work (Chapter 1) addresses the support of managers through performance indicators in operational decisions. For this reason, individual factors that managers bring with them and that affect the beneficial effect of performance measurement are also of interest (H4Mgmt). Similar to Mintz / Currim (2013), numerical understanding and professional experience are used to identify differences. Furthermore, these constructs should also be used to investigate improved management decisions, expressed by increased company performance as a dependent variable (H5Mgmt) (Chapter 1.2).

Table 52: Management survey: catalog of hypotheses

No.hypothesis
H1Mgmt:There is a connection between the strategic orientation of retail companies and the use of performance indicators.
H2Mgmt:There is a connection between the structural characteristics of the company (industry, size, hierarchy, area of ​​responsibility) and the use of performance indicators.
H3Mgmt:The more important performance components are assessed in the variable remuneration components, the more these components are used by the company and the more useful they are rated.
H4Mgmt:There is a connection between the individual manager's characteristics (understanding of numbers, professional experience) and the use of performance indicators.
H5Mgmt:There is a connection between the perceived usefulness in everyday life or company performance and the use of different sets of indicators.

The questionnaire was drawn up together with a controller. The team translated those key figures into German that resulted from the literature analysis and the qualitative interviews (Appendix A). Subsequently, these were divided into the categories "Strategic orientation"← 241 | 242 → and "Operational orientation" assigned. All key figures that were assigned to the operational area were in turn divided into the categories "financially" and "Non-financial" as well as in the categories "Assortment", "Customers" and "Employees" subdivided. A clear list of 25 different key figures was created that was used for the survey (Appendix B). Comparability of the results is ensured by similar classifications in studies such as Klein (2010) or Reineke / Reibstein (2002).

Two events in Vienna, at which trade managers from all over Austria could be found, made it possible to carry out the study. The starting point was a trading conference of WKO at the Vienna University of Economics and Business in early May 2014. As part of the REGAL industry get-together in 2014 Another survey point was used. A follow-up survey in which store managers in Vienna and Lower Austria were interviewed resulted in a total of 168 questionnaires. In the course of the exploratory data analysis of the completed questionnaires, questionnaires from outside the industry and inadequately completed questionnaires were excluded. Thus, a total of 134 evaluable questionnaires are included in the further investigation.

Table 53: Management survey: methodological profile

Characterization featureDesign-specific characteristics
research objectUse and perception of key figures in everyday trading
Research instrumentOral, personal survey (quantitative questionnaire)
Pretestn = 8 trading and / or controlling experts from science and practice (April 28, 2014 - May 5, 2014)
Data analysis softwareEXCEL 2007; PASW Statistics 18
Survey regionAustria
Survey periodWave 1: May 7, 2014 (WKO); Wave 2: 14.06.2014 (REGAL industry meeting); Wave 3 (July-August 2014)
PopulationPeople who are active in the Austrian retail sector
Sampling procedureExpert sample
Sample sizen = 134 ← 242 | 243 →

7.1.2 Management survey: sample description

According to the proportions in the Austrian industry average, the examined sub-sample is also in the range Fast moving consumer goods most strongly represented: 41.8% of those surveyed work in Food retail, 3.7% in DFH. The fashion sector also flows through the with a total of 26.2% represented EH with clothing or Retail with sporting goods and clothing relatively heavily in the analysis (Table 54). If one compares the sample with the market shares of the Austrian retail landscape (Chapter 4.2), it is noticeable that the proportions are not distributed in a representative manner. Nevertheless, (chain stores) retailers of fast-moving groups of goods are the target group with the greatest relevance for the present research problem. Following this argument, the sample is seen as relevant for answering the research question; however, the claim of generalizability cannot be guaranteed.

Table 54: Management survey: industry distribution

BranchfrequencyPercentages
LEH5641,8 %
EH with clothing2518,7 %
Retail with sporting goods and clothing107,5 %
DFH53,7 %
EH with shoes and leather goods53,7 %
EH with electrical appliances and consumer electronics32,2 %
EH with books and magazines21,5 %
EH with furniture21,5 %
Food eCommerce21,5 %
EH with building and home improvement needs10,7 %
EH with clothing and food retail10,7 %
EH with watches and jewelry10,7 %
Trade (no clear allocation possible)2115,7 %
total134100,0 %

The company's success was queried through the self-assessment of the managers with regard to the sales development compared to the industry average. A similar approach was used by Homburg et al. (2002) and Grafton et al. (2010) elected. On a scale of "1 = Much worse " to "5 = Much better "← 243 | 244 → the respondents see a synchronous to slightly above average performance development in their company (n = 130; M = 3.55, SD = 0.86).

An open question shows the functional area of ​​the people questioned in the retail company. In the course of the analysis, the statements were divided into the categories "Store Management ", „Middle management" and "Top Management " assigned. If no clear position was given, the category "Rest" chosen (Table 55).

Table 55: Management survey: functional area

The above-average share of top management positions at 37.3% results from a survey of self-employed entrepreneurs in smaller retail formats. In the sample examined, 56.0% of those questioned state that fewer than 100 people work in their company. A quarter of the respondents are employed in large companies with over 1000 employees (Table 56).

Table 56: Management survey: number of employees - total companies

Question: How many people does the entire company in which you work (full-time) employ?frequencypercent
<1007556,0 %
100–4991712,7 %
500–99975,2 %
>10003425,4 %
not specified10,7 %
total134100,0 % ← 244 | 245 →

Performance measurement has different functionalities. Among other things, coordinating, motivating or controlling tasks are assigned to this, depending on the area of ​​responsibility and size. On the one hand, the analysis here also reflects the small-scale structure of retail companies. At the same time, it can be seen that store managers from large retail companies are responsible for smaller teams (Table 57).

Table 57: Management survey: number of employees - area of ​​responsibility

Question: How many employees are you responsible for (full-time)?frequencypercent
0 people2921,6 %
1–10 people5641,8 %
11–50 people3929,1 %
51-100 people75,2 %
more than 100 people32,2 %
total134100,0 %

The decision-making effect of performance indicators also manifests itself in different forms of remuneration. As Table 58 shows, around a quarter of those questioned receive an entrepreneur's wage. One third each receives a fixed basic salary or variable components in addition to the basic salary.

Table 58: Management survey: remuneration

compensationfrequencypercent
I receive an entrepreneur's salary.3022,4 %
I receive a fixed basic salary.4936,6 %
In addition to a basic salary, I also receive variable components (premium / bonus payment, commission, ...)4634,3 %
I receive an entrepreneur's salary as well as a fixed salary.53,7 %
not specified43,0 %
total134100,0 % ← 245 | 246 →

On average, the respondents have worked in retail for around 18 years (M = 17.75, SD = 13.74). This indicates above-average professional experience and competence for the underlying question. The sample consists of approx. 60% male and 40% female subjects.