s2fieldsofstudy: Business Economics

text:
Criteria-Based Approach to Select Relevant Environmental SDG Indicators for the Automobile Industry

: The United Nations Sustainable Development Goals (SDGs) cannot be met without the private sector. In order to contribute to the fulﬁllment of the SDGs, companies have to identify their inﬂuence and select relevant SDGs. However, so far no research has been conducted on the inﬂuence of companies or industries at the most concrete level in the SDG framework—the 247 SDG indicators. In this paper, a criteria-based approach to select relevant environmental SDG indicators for the automobile industry is developed. The three criteria—environmental impact, direct impact, and automobile impact—are deﬁned. By means of a qualitative analysis, 31 inﬂuenceable indicators are selected and substantiated by an empirical analysis of the automobile industry’s impact. These indicators belong to 12 SDGs and demonstrate the broad inﬂuence of the automobile industry. The outcome of this study is a structured procedure for selecting relevant environmental SDG indicators. This procedure can be applied by companies and can also be adapted to other economic sectors. Finally, it is possible to quantify the level of inﬂuence of the selected indicators and thus measure the contributions of companies or economic sectors to the fulﬁllment of the SDGs.

Introduction
For more than 30 years, mankind has been dealing with the questions of what sustainable development means in a globalized world and how it can be achieved. The United Nations (UN) plays a leading role in this task. Important milestones for sustainable development were: • the so-called "Brundtland Report" [1] of 1987, with a definition for sustainable development; • the Earth Summit in 1992, with the adoption of Agenda 21; • the Millennium Development Goals (MDGs) of 2000 for poverty reduction [2].
In 2015 the UN eventually adopted the Sustainable Development Goals (SDGs) with the resolution "Transforming our world: The 2030 Agenda for Sustainable Development" [2]. The SDGs are a response to the current major sustainability challenges-such as poverty, inequality, and environmental degradation [3]. In 17 goals, with 169 targets (or sub-goals) and 247 indicators, the SDGs define and 1.
Which criteria should be used to select relevant environmental SDG indicators? 2.
Which environmental SDG indicators are relevant for the automobile industry?
To answer these questions, the criteria for selecting relevant SDG indicators are defined in the chapter Materials and Methods. Using these criteria, relevant environmental SDG indicators for the automobile industry are subsequently selected from the latest SDG indicator set with 247 indicators [4]. For better understanding, additional examples of how this qualitative analysis was conducted are provided in this chapter, too. Additionally, empirical evidence for the impact of the automobile industry is examined for the selected indicators. The Results chapter contains the identified indicators and compares the results with other studies. The applied method, limitations, and results are discussed in the following chapter. In the final chapter of this paper, conclusions are shown and directions for future research are delineated. Additionally, an explanation of the complete indicator selection is provided in the Supplementary Materials.

Materials and Methods
The research questions of this paper are answered based on an analysis of the current global UN SDG indicators. The indicators examined are dated March 2020 and thus include the amendments of the 51st session of the UN Statistical Commission. This indicator set consists of 247 indicators in total. As some indicators are used multiple times to measure targets, 231 independent UN SDG indicators are listed [4]. Besides these global UN SDG indicators, there are also national (e.g., Germany [34]) and international (e.g., EU [35]) indicator sets derived from the UN member states or union of states [6] to contribute to the SDGs. What makes the global UN SDG indicators beneficial is the transnational availability of data, since all UN member states are obliged to report on them. In contrast, national or international indicators are linked to the 17 overarching goals, but may differ from the global UN SDG indicators (hereinafter termed just SDG indicators). Comparisons between countries are therefore not possible with these indicator sets. To select relevant environmental SDG indicators, criteria are first defined to characterize the term "relevant" in the sense of being influenceable [12,27]. For these criteria, it is then specified in which cases indicators are selected or not selected. Finally, the group of selected SDG indicators is sub-divided based on their ethical duties.

Criteria-Based Selection of Relevant Environmental Indicators
The basis for the selection of relevant environmental indicators is the available metadata, which contain the definition and calculation methodology of the SDG indicators [49]. At the time of this research (May 2020), provisional metadata were available for the latest indicator changes proposed to the 51st session of the UN Statistical Commission [50]. In addition, the methodology for some indicators is still under development. They are classified as "Tier III Indicators" [51]. For the three criteria, cases in which indicators are selected or not selected are defined. Definitions of the criteria and selection examples are presented in Table 1.
The selection is carried out in the form of a qualitative analysis in three steps. In each step, an evaluation is performed to determine whether the content of the indicators from the metadata meets the cases "selected" or "not selected" from Table 1. In the case of Tier III indicators, the evaluation is carried out on the basis of the designation. For an indicator to be selected, one example that meets the requirements is sufficient. In the first step, all 247 indicators are evaluated. In the following two steps, only those selected in the preliminary stage are evaluated.

Criteria-Based Selection of Relevant Environmental Indicators
The basis for the selection of relevant environmental indicators is the available metadata, which contain the definition and calculation methodology of the SDG indicators [49]. At the time of this research (May 2020), provisional metadata were available for the latest indicator changes proposed to the 51st session of the UN Statistical Commission [50]. In addition, the methodology for some indicators is still under development. They are classified as "Tier III Indicators" [51]. For the three criteria, cases in which indicators are selected or not selected are defined. Definitions of the criteria and selection examples are presented in Table 1. The selection is carried out in the form of a qualitative analysis in three steps. In each step, an evaluation is performed to determine whether the content of the indicators from the metadata meets the cases "selected" or "not selected" from Table 1. In the case of Tier III indicators, the evaluation is carried out on the basis of the designation. For an indicator to be selected, one example that meets the requirements is sufficient. In the first step, all 247 indicators are evaluated. In the following two steps, only those selected in the preliminary stage are evaluated.

Structuring of the Relevant Environmental SDG Indicators
In some studies, SDGs [11] or their targets [30] are distinguished on the basis of their ethical duties to either create positive value or to reduce harm. In the case of positive impacts, also called "doing good ", actions are not expected-i.e., they are perceived as additional. In contrast, for negative impacts, called "avoiding harm", it is expected that those causing problems will also solve them [52]. Hence, reducing harm can be seen as a stronger norm than creating positive value [30]. The two cases are illustrated by examples. If a company carries out activities to prevent its own emission of pollutants into the environment, it reduces negative impacts, i.e., avoiding harm. If, on the other hand, a company participates in water protection without polluting it, then it creates positive impacts, i.e., doing good. This distinction is used in this paper to structure indicators into positive and negative impacts. Therefore, the definitions of [30] are used: • doing good: generate "positive externalities"; • avoiding harm: "reduce negative externalities".
For each relevant environmental SDG indicator, it is examined whether it corresponds to the positive or negative side. In addition, the content of its target is also examined in order to identify the purpose of the indicator.

Empirical Evidence of the Impact and Comparison of the Results with other Studies
For the selected indicators, the empirical evidence of the impact of the automobile industry was additionally examined. For this purpose, the calculation methodology of the indicators in the metadata [49] was first identified. Subsequently, the factors that cause a change in the indicators (i.e., for emissions or resource use) were analyzed. Finally, sector data (for the automobile industry or the transport sector) were searched for these factors. The study was conducted for the European region. Where available, data from the EU28 were used. Alternatively, data from Europe in general or from individual EU28 countries were used. The most recent data were selected. For each selected indicator, at least one impact example was examined, but no comprehensive analysis of all impacts was carried out. If no dedicated data were available for an indicator, a reasoned estimate was made based on existing data. It was not possible to determine an impact for Tier III indicators, as no agreed methodology is yet available for these indicators. As a result, the proportion of the automobile industry or of the transport sector in the factor that causes an indicator change is given. For example, the proportion of the transport sector in certain emissions or of the automobile industry's share in the use of resources is analyzed.
Furthermore, the selection results of the present paper were compared with the results of other studies. This comparison of the selection decisions for the three criteria takes place on the overarching goal level, since other studies provide results on this level only (with the exception of environmental indicators defined by the United Nations Environment Programme (UNEP) [53]). One comparison each was made for the categorization of SDGs to the environment (step 1 and 2 in Table 1), and for the impact of the automobile or transport sector on the SDGs (step 3 in Table 1). The comparison of the categorization of SDGs to the environment was carried out with the work of UNEP [53], Beck and Buddemeier [11], and the Stockholm Resilience Centre (SRC) [36]. For the results of the present paper, an SDG was attributed to the environment if it contains at least one of the selected indicators with an environmental impact (124 in step 1; 45 in step 2). In the case of UNEP, the same procedure was applied: if an SDG contains at least one of the 93 environmental indicators, it was attributed to the environment. For Beck and Buddemeier, the SDGs are presented which are attributed to the environment, while for the SRC those which are attributed to the biosphere are presented. The comparison of the impact of the automobile or transport sector on the SDGs was carried out with the Sector SDG Target Impact Index (SSTII) presented by Betti et al. [32], Beck and Buddemeier [11], and the World Benchmarking Alliance (WBA) [31]. For the results of the present paper, an SDG was considered influenceable if it contains at least one of the selected indicators with an automobile impact (31 in step 3). For the SSTII, the seven SDGs with the highest impact index value for the transportation sector were chosen for comparison. For Beck and Buddemeier, SDGs were chosen for comparison which have a high impact on at least two out of three value chain steps for the automobile sector, while, for the WBA, SDGs with a high impact for the automobile and components industry were chosen.

Results
Environmental impact, direct impact, and automobile impact were identified as criteria for selecting relevant environmental SDG indicators. Applying these criteria results in a stepwise reduction in the SDG indicators. This is illustrated in Figure 2. Out of 247 indicators, 124 have an environmental impact. Of these, 45 indicators have a direct impact on the environment and 31 indicators-27 of which are independent-can be influenced by the automobile industry. Within this group of relevant environmental indicators, 28 deal with the negative (avoiding harm) side of ethical duty, while three indicators cover the positive (doing good) side. The selection is illustrated by three examples in Table 2.
The evaluation of all 247 indicators is attached in Table S1 in the Supplementary Materials. Each selection decision is also explained there. In the following, the environmental indicators relevant to the automobile industry are separately presented. selecting relevant environmental SDG indicators. Applying these criteria results in a stepwise reduction in the SDG indicators. This is illustrated in Figure 2. Out of 247 indicators, 124 have an environmental impact. Of these, 45 indicators have a direct impact on the environment and 31 indicators-27 of which are independent-can be influenced by the automobile industry. Within this group of relevant environmental indicators, 28 deal with the negative (avoiding harm) side of ethical duty, while three indicators cover the positive (doing good) side. The selection is illustrated by three examples in Table 2.

Avoiding Harm Indicators
The 28 avoiding harm indicators and empirical evidence of the impact of the automobile industry are shown in Table 3. The impacts are described in more detail in Table S2   in the Supplementary Materials. In addition, the calculation methodology for each indicator is also given there. It can be seen that these indicators are distributed over eleven SDGs. SDG 6 (clean water and sanitation) and SDG 15 (life on land), each with five indicators, contain the most, followed by SDG 12 (responsible consumption and production) with four indicators. In addition, three indicators occur multiple times. The indicators "Material footprint, material footprint per capita, and material footprint per GDP" and "Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDP" are used in both SDG 8 and SDG 12 to measure the targets. The indicator "Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population" is used in SDG 1, SDG 11, and SDG 13 to measure their targets. Thus, 24 independent, environmental avoiding harm indicators relevant to the automobile industry were identified. Wastewater from industry can cause a deterioration of the water-quality [74]. For the proportion of treated industrial wastewater, see 6.3.1.

6.4.1
Change in water-use efficiency over time.
Manufacturing industry accounts for 11.2% of the total water abstraction of fresh surface and groundwater [75]. Manufacture of transport equipment accounts for 2.5% of the total water used in the manufacturing industry [76].

6.4.2
Level of water stress: freshwater withdrawal as a proportion of available freshwater resources.
The indicator can be impacted by water withdrawal. For the proportion, see 6.4.1.
6.6.1 Change in the extent of water-related ecosystems over time.
For the 1. sub indicator (spatial extent of water-related ecosystems), transport accounts for 1.1% of the net land take of wetlands, as well as for 0.4% of the net land take of water bodies [78]. Transportation accounts for 28.5% of the total final energy consumption [79]. Meanwhile, the automobile industry accounts for over 7% of the EU GDP [80].

Doing Good Indicators
The three environmental doing good indicators and empirical evidence of the impact of the automobile industry are listed in Table 4. More detailed descriptions of the impacts as well as the calculation methodology for each indicator are given in

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper. Table 5. Overview of the SDGs [2] that are attributed to the environment. The two upper rows contain the source and information on the evaluation. The following rows show the relationship between the SDGs and the environment. Colored: SDG is attributed to the environment; white: SDG is not attributed to the environment. The share of renewable energy in the gross final energy consumption in the transport sector is 8% [100]. Road transport accounts for 72.7% of the total final energy consumption in the transport sector [101]. 9 9.1.2 Passenger and freight volumes, by mode of transport.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Indicator Level
Step 1 *

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Table 6. Overview of the SDGs [2] influenced by the automobile industry and the transportation sector, respectively. The two upper rows contain the source and information on the evaluation. The following rows show the relationship between the SDGs and the automobile industry or the transportation sector. Colored: influence exists; white: no influence exists. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Table 6. Overview of the SDGs [2] influenced by the automobile industry and the transportation sector, respectively. The two upper rows contain the source and information on the evaluation. The following rows show the relationship between the SDGs and the automobile industry or the transportation sector. Colored: influence exists; white: no influence exists. 1 No Poverty  Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Table 6. Overview of the SDGs [2] influenced by the automobile industry and the transportation sector, respectively. The two upper rows contain the source and information on the evaluation. The following rows show the relationship between the SDGs and the automobile industry or the transportation sector. Colored: influence exists; white: no influence exists. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Table 6. Overview of the SDGs [2] influenced by the automobile industry and the transportation sector, respectively. The two upper rows contain the source and information on the evaluation. The following rows show the relationship between the SDGs and the automobile industry or the transportation sector. Colored: influence exists; white: no influence exists.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper.

Comparison of the Results with other Studies
A total of 31 environmental SDG indicators relevant to the automobile industry are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover more than two thirds of the 17 SDGs. The results of the present paper are compared with other studies. This comparison of the selection decisions of the three criteria takes place on the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. First, the categorization of SDGs to the environment is shown in Table 5, including the interim results of the first two steps from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Table 6. Overview of the SDGs [2] influenced by the automobile industry and the transportation sector, respectively. The two upper rows contain the source and information on the evaluation. The following rows show the relationship between the SDGs and the automobile industry or the transportation sector. Colored: influence exists; white: no influence exists. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Table 6. Overview of the SDGs [2] influenced by the automobile industry and the transportation sector, respectively. The two upper rows contain the source and information on the evaluation. The following rows show the relationship between the SDGs and the automobile industry or the transportation sector. Colored: influence exists; white: no influence exists. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Table 6. Overview of the SDGs [2] influenced by the automobile industry and the transportation sector, respectively. The two upper rows contain the source and information on the evaluation. The following rows show the relationship between the SDGs and the automobile industry or the transportation sector. Colored: influence exists; white: no influence exists. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Table 6. Overview of the SDGs [2] influenced by the automobile industry and the transportation sector, respectively. The two upper rows contain the source and information on the evaluation. The following rows show the relationship between the SDGs and the automobile industry or the transportation sector. Colored: influence exists; white: no influence exists. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Table 6. Overview of the SDGs [2] influenced by the automobile industry and the transportation sector, respectively. The two upper rows contain the source and information on the evaluation. The following rows show the relationship between the SDGs and the automobile industry or the transportation sector. Colored: influence exists; white: no influence exists. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper. Partnerships for the goals Note: * SDGs containing at least one of the 124 indicators selected in methods step 1 (environmental impact); ** SDGs containing at least one of the 45 indicators selected in methods step 2 (direct environmental impact); *** SDGs containing at least one of the 93 indicators attributed to the environment; **** SDGs that are attributed to the environment; ***** SDGs that are attributed to the biosphere. Table 6 shows an overview of the SDGs influenced by the automobile industry and transport sector, respectively, including the results of the third step from the methods in the present paper.

Discussion
The developed method is used to select relevant environmental SDG indicators for the automobile industry. The method is not limited to the present design, but can also be broadened to other economic sectors and sustainability elements (society and economy). Companies can thereby select influenceable SDG indicators and, thus, contribute to the fulfillment of the SDGs. Therefore, the present paper goes beyond the existing studies [12,27,30] by selecting the influenced SDGs on the indicator level and by reducing the set of indicators by identifying impacts. Additionally, impacts of the automobile or transport sector on the selected indicators could be empirically proven in the present paper, as shown in Tables 3 and 4. Impacts were found for 29 of the 31 selected indicators, some of them in the double-digit percentage range. On indicator 3.9.3 (unintentional poisoning), no impact could be detected, since causes other than the automobile industry are responsible for a change in this indicator. Although the automobile industry could theoretically influence CO emissions (and CO poisoning is considered in the indicator), no causal relationship was identified, because unintentional poisoning by CO occurs in enclosed spaces and usually improper use or poor condition of indoor combustion devices and structural fires are the cause [71]. Additionally, no impact could be determined for indicator 12.5.1 (recycling rate), as no agreed calculation methodology is yet available for this indicator (Tier III). In the following, the results of this paper will be interpreted and compared with other studies. Further, the methods, possible changes, and limitations are discussed. Finally, the future research potential is presented.

Interpretation of the Results
To select relevant environmental SDG targets for companies, the number of targets in [30] is reduced in several steps. First, this was done via exclusion criteria-SDG targets with letters and those that are mainly aimed at governmental action. Then, multiple and similar targets were summarized. Summarizing and thus reformulating similar indicators is deceptive, however, because it reduces the number but does not unselect any topics-the affected targets in the SDG framework remain the same. In addition, summarizing targets creates the problem that contributions can no longer be exactly matched with the SDGs. Compared to this, the present paper sets a narrower focus for the selection of relevant indicators. On the one hand, with the environment, one element of sustainability is considered, which is further limited by direct impacts. On the other hand, impacts for one particular industry are examined. This leads to the fact that, although the number of indicators is higher than the number of targets, the topics are selected more effectively. The indicators are reduced without being summarized or reformulated. This ensures an exact attribution to the SDG structure and allows determining the contributions of companies to the SDGs.
Comparisons of the selection decisions of the three criteria can only be carried out at the overarching goal level, since, with the exception of environmental indicators defined by the UNEP [53], other studies provide results on this level only. Table 5 shows an overview of the categorization of SDGs attributed to the environment. In [11] and [36] SDGs are categorized by ecology, society, or economy on the goal level, whereas in [53] and in the present paper the categorization is conducted on the indicator level. It is noticeable that, in the evaluation at the indicator level, more SDGs were categorized as environmental than at the goal level. In the case of SDGs 1, 2, 3, 8, and 9, individual indicators with direct environmental impact are identified, whereby the majority of the indicators of these goals have no (direct) environmental impact. Analyses at the goal level would filter out these indicators and their topics and thus not include them in the results, whereas analyses at the indicator level also consider SDGs that have another focus (such as social or economic) if at least one indicator also has an environmental impact. In this way, topics and impacts are not excluded. The results of the analyses at the indicator level are therefore similar. The main difference between the results of the first step in this paper (environmental impact, 124 indicators, 17 SDGs) and UNEP [53] (93 indicators,16 SDGs) relates to the definition of environment. In the present paper, human health is considered as part of the environment, while basic services and financing issues (including for environmental protection) are excluded. In [53], it is done vice versa. In addition, in the present paper the current SDG indicators [4] with the latest changes are examined, while the other study uses a previous indicator set. The second step in this paper (direct environmental impact) reduces the number of selected indicators to 45, belonging to 12 SDGs.
The impact of the automobile industry (third step) on the SDGs is compared with three studies -see Table 6. The comparability with the studies mentioned above decreases if it is taken into account that, in the present paper, before assessing the impact of the automobile industry numerous indicators were already excluded in the first two steps. In this paper, the impact of the automobile industry on indicators with a direct environmental impact is evaluated. All the more interesting is the result that, in this paper, indicators which are influenced by the automobile industry are also identified for the mentioned SDGs of the three studies-except SDGs 5 and 17. It can be concluded from this that the automobile industry has a broad impact on the environment, and that this impact is apparent even in analyses without an ecological focus.
Definitions from van Zanten and van Tulder [30] were used to classify the ethical duties. The evaluation of the indicators in the present paper, for which the respective targets were also included, is therefore consistent with the evaluation of the targets from [30]. As shown in Tables 3 and 4, 28  have positive impacts. This strong imbalance for avoiding harm indicators can be explained by the focus on environmental protection. Environmental protection is mainly aimed at reducing damage. This relationship is also shown in [11]: for SDGs with environmental impacts, the reduction in negative impacts is in the foreground, whereas for social and economic SDGs the creation of positive impacts is predominant. Besides this, by definition, doing good is intended to create benefits that are perceived as additional. Since, in the present work, indicators have been evaluated that are influenced by automobiles, it is expected that the automobile industry will also take care on the negative impacts of these indicators. For the automobile industry, however, the selection of largely avoiding harm indicators may provide opportunities. According to [30], companies that are committed to the SDGs tend to select avoiding harm indicators, as the responsibility is more evident in these indicators and companies can thus contribute to the SDGs without being dependent on others. Nevertheless, it is also noted that companies will have to change their practices and also work on doing good indicators in order to achieve the SDGs by 2030.

Discussion of the Methods
The three criteria of environmental impact, direct impact, and automobile impact are used to identify the environmental indicators relevant for the automobile industry, in the sense of being influenceable [12,27]. The first two criteria are independent of industry and are used to select the SDG indicators with a direct environmental impact. The third criterion is used to evaluate the influence of an industry-in this case, the automobile industry-on indicators.
The first criterion, environmental impact, was determined on the basis of the sustainability hierarchy and the need for more environmental protection. The number of indicators selected in this stage demonstrates that environmental impact is a useful criterion for focusing the analysis on relevant indicators. On the one hand, this criterion is effective in filtering out about half of the 247 SDG indicators. On the other hand, due to the comprehensive definition of the environment, the group of 124 indicators selected is not too specific, but ensures the coverage of a wide range of topics. In this step, mainly indicators with social and economic content were filtered out. All the indicators with a financial content were assessed as economic, even if they collect financial resources for environmental protection (e.g., 1.a.2, 6.a.1 and 15.a.1). Indicators that promote sustainable development were evaluated as having an environmental impact, as this is part of sustainable development (e.g., 12.1.1 and 17.16.1). Basic services were assessed as social even if they also have potential environmental impacts (e.g., 7.1.1). Health issues were not counted as basic services, as they are part of the environmental definition in this paper and are therefore further investigated in the next steps. If an indicator includes both basic services and environmental topics, it has been evaluated as having an environmental impact (e.g., indicator 6.3.1).
In order to select influenceable indicators from the group of indicators with an environmental impact, direct impact was used as the second criterion. In this step, indicators are selected whose environmental impact does not depend on other activities. Thus, contributions to these indicators also directly contribute to environmental protection. In this way, the group selected in the first step was reduced to 45 indicators. Environmental conditions are considered as indicators with direct impact (e.g., 2.5.2 and 6.3.2) and are selected. All the preparatory measures such as plans, strategies, or political frameworks are filtered out. Such structures are necessary conditions for environmental protection. However, they are a preparatory stage, and actual protection must be performed afterwards. (e.g., 1.5.3, 6.5.1, and 14.2.1). In addition, indicators that include general health and are not caused by ecosystems or resources are filtered out in this step (e.g., 2.2.2., 3.3.3, and 4.2.1.). A possible improvement of the methods would therefore be to filter out general health indicators in the first step, since it is a basic service anyway. This would reduce the number of selected indicators in the first step, but would lead to the same results from the second step onwards.
The automobile is used to select indicators that are influenced by the automobile industry. In this way, 31 relevant environmental indicators are identified, 27 of which are independent. These indicators belong to 12 SDGs and thus cover a wide range of environmental issues and demonstrate the diverse influence of the automobile on the SDGs. The topics not influenced by the product include nature conservation zones (e.g., 14.5.1 and 15.4.1) and the installed renewable energy-generating capacity in developing countries (e.g., 7.b.1 and 12.a.1). By using the main product of the investigated industry as an impact, the main activity is covered and the influence can be defined well by inputs and outputs. For industries with different products, the totals of inputs and outputs can also be used as an impact. The definition of impact of an industry could also be broadened-e.g., the pure possibility to exert influence, or additional engagement independent of the own value chain. This would more realistically represent the actual impact and also increase the sphere of influence. In large companies, for example, additional activities can also be carried out, such as providing food for employees, supplying energy and water to the region, or other extraordinary commitments, especially in crisis periods. However, an extension of the impact definition would also bring with it the challenge of defining limits to what and how much of the potentially influenceable issues can ultimately be considered as "impact". Assuming the impact of the automobile industry through the product also falls short when considering automobile companies as mobility service providers. Moreover, such a definition primarily applies to automobile manufacturers. For other participants in the automobile industry, such as suppliers, it would have to be investigated which sub-areas they influence. Ultimately, the binary classification of whether an influence exists bears the risk that influences of different magnitudes are treated equally, since influences are considered independently of relative contribution to an environmental problem. Therefore, the level of influence would have to be calculated additionally.
The analysis of the empirical impact demonstrates that, by applying the present method, indicators can be selected on which the automobile industry has an impact in practice. It should be noted that the percentages given in Tables 5 and 6 do not necessarily reflect the share of the automobile industry in an indicator. On the one hand, because no complete analysis of all possible impacts was carried out, one example was already sufficient to demonstrate an impact. On the other hand, the evaluation frames are not consistent for every indicator. For example, the transport sector contains other parts besides road transport. Road transport itself can be differentiated into further parts-e.g., passenger cars, light commercial vehicles, and trucks. However, influences from the automotive industry and the transport sector were treated equally in the present paper. For this reason, the percentages between the indicators are not comparable. Furthermore, for some indicators, reasoned estimates had to be made if no dedicated data were available. In the case of indicator 3.9.3 (unintentional poisoning), no impact could be identified based on this. For indicator 8.4.2 (domestic material consumption (DMC)), the proportion of indicator 8.4.1 (material footprint) was used, since DMC by definition cannot be disaggregated to economic sectors. For indicator 15.4.2 (mountain green cover index), it is possible that part of the area is also used for the automotive industry-e.g., for cork oaks. However, since this could neither be substantiated with data nor disproved, the total economic share of wood used for automobiles (without differentiation for mountains) was assumed to be proportional for this indicator.
Besides, it does not necessarily mean that an indicator is controllable by the automobile industry if it is influenceable-e.g., for the indicators 1.5.1 (number of deaths, missing persons, and directly affected persons attributed to disasters per 100,000 population), 3.9.1 (mortality rate attributed to household and ambient air pollution), and 15.5.1 (Red List Index), the automobile industry causes inputs or outputs that influence the indicator. For the three examples mentioned, CO 2 emissions lead to an increase in the average temperature, particulate emissions lead to an increase in particle concentration, and land use and land change lead to habitat destruction. However, the impact (indicator change) is not linear and not exclusively attributable to the activities of the automobile industry. The impact depends on the actual situation and other stakeholders. This does not mean that the automobile industry should not work on indicators that can be influenced, as it still can control inputs and outputs of the indicators selected in the present paper. However, it should be considered that the automobile industry cannot achieve these targets and goals on its own. Partnership from SDG 17 becomes the focus of attention at this point.
A further limitation is that, since one example per stage is sufficient for the selection decision, Table S1 in the Supplementary Materials does not contain a complete analysis of the impacts per indicator. If a topic has multiple impacts-for example, climate change impacts human health as well as ecosystems-usually only one impact is used for the explanation of the decision. For this reason, no analysis of the most frequent environmental impacts can be made. In addition, no metadata are available for some indicators-marked "Tier III" in Table S1. As such indicators are assessed by name, there is uncertainty as to whether the assessment will remain valid once the methodology is developed and metadata are published. With 12.5.1, one of the environmental indicators relevant for the automobile industry belongs to this group with uncertainty.

Future Research Potential
The results of the present paper serve as a basis for further work. The qualitatively determined, relevant environmental SDG indicators for the automobile industry indicate which indicators are influenceable. The impact was additionally empirically proven. Quantitative analyses are needed to determine the level of influence consistently for all indicators. Therefore, a calculation model can be developed that quantifies the impacts (see Table 5, Table 6, Table S1, Table S2, and Table S3 for impact examples) of the automobile industry within defined system boundaries. The impacts can then be calculated for indicators as proportions for a country or region. In this way, the contributions of an industry or a company to the SDGs can be determined, thus solving the problem of the lack of methods for evaluating contributions. The procedure described may be necessary and logically plausible, but obstacles still have to be overcome in future research. For example, in addition to indicators where the proportions are obvious (e.g., 13.2.2 "Total greenhouse gas emissions per year"), there are also indicators where further work still needs to be undertaken. In order to be able to quantify the impact on indicator 1.5.1 "Red List Index", for example, it is first necessary to determine which aspects of the automobile industry influence this indicator. Subsequently, data must also be collected for the determined influencing variables until they can be calculated to a proportion. Another promising research area is the prioritization of these relevant indicators through a multi-criteria analysis. In addition to the quantified influence, other criteria, such as those defined by Allen et al.
[105]-that is, level of urgency, systemic impact, and policy gap-can be used for this purpose.

Conclusions and Outlook
To support the private sector in meeting the SDGs, a method for selecting relevant environmental SDG indicators for the automobile industry was developed in this paper. Therefore, the three criteria-environmental impact, direct impact, and automobile impact-were defined, by which 31 relevant out of the current 247 SDG indicators were selected. These indicators belong to 12 SDGs, so that more than two thirds of the SDGs are influenced by the automobile industry. SDG 6 (clean water and sanitation) and SDG 15 (life on land), each with five indicators, contain the most, followed by SDG 12 (responsible consumption and production) with four indicators. For the selected indicators, empirical evidence of the impact of the automobile industry was additionally examined and found for 29 of the 31 indicators. This analysis demonstrates that, by applying the present method, indicators can be identified on which the automobile industry has an impact in practice. In order to make its ecological contribution to the SDGs, the automobile industry should work on the indicators selected and optimize its influences along the value chain accordingly. The vast majority of the indicators selected here measure negative impacts and are concerned with avoiding harm. This offers the advantage that the responsibility is more recognizable and individual companies can contribute to environmental protection.
The reason for the relatively high number of influenced SDGs is the analysis at the indicator level. In this way, it is possible to select influenceable indicators from one SDG even if this SDG has a different focus, as is the case for environmental impacts in SDGs 1, 8, and 9. Analyses at goal level would filter out these indicators and their topics and thus not include them in the results. The impact analysis at the indicator level is also necessary to quantify contributions to the fulfillment of the SDGs, since indicators substantialize and operationalize targets. For the attribution of contributions to the SDGs, it is particularly important that the original SDG framework is not modified by reformulation or summarization.
The results of this paper serve as a basis for further work. Thus, in the next step, the level of influence for the relevant environmental indicators can be quantified, as further described in the discussion chapter. In addition, other criteria can be examined and then compared with the presented results. The present method can be broadened to include the other elements of sustainability (society and economy), as well as other types of impact (e.g., indirect). Finally, it is also interesting to use this criteria-based approach to determine the relevant indicators of companies, other industries, or economic sectors. This makes it possible to determine who can contribute to which indicators. Additionally, such comparisons clarify for which indicators there are few contributions. Such indicators, which are influenced to a small extent by the private sector, must then be addressed by, or with, other stakeholders in order to meet the SDGs by 2030.


THE MEASUREMENT PROCESS IN MICRO AND SMALL SOFTWARE MAINTENANCE COMPANIES: EMPIRICAL STUDY

With the dissemination of quality models and the need for measurement-based management for software organizations, a variety of approaches and models are emerging to support organizations on this journey. According to a Systematic Review of Literature that selected 30 of 692 papers returned from search sources, it was noticed the need for guidelines and guides regarding the measurement process in a specific sector: micro and small companies that work with software maintenance. In this work, an empirical study was carried out to understand how the measurement process aligned with quality models can be implemented in these organizations. The field study followed the implementation of an improvement program based on CMMI® level 2 in 7 Brazilian companies from Maringá, Paraná. Finally, it was found that the process for the definition of a measurement guide can be common to a specific group of companies, taking steps and guidelines for its execution, always aligned with the organization’s strategy, giving top management an objective view work and process, supporting decision making.

INTRODUCTION
Software Process Improvement (SPI) has received considerable attention in recent years, both in academia and industry. A reflection of this is the popularization of maturity models that have been disseminated and applied in software organizations due to factors related to competitiveness, product quality, economy and efficiency ( FERNÁNDEZ-SÁEZ; CHAUDRON; GENERO, 2018). Historically, software development has been far more representative than maintenance in most organizations. However, this scenario is changing, given the organizations' effort to make the most of their software development investments, keeping the product operating as long as possible (EDBERG; IVANOVA, 2011) It is perceived that maintenance is an essential part of the software life cycle and has been gaining increasing attention among industry and researchers, according to the Brazilian Association of Software Companies (ABES) (ABES, 2017), 55.4% of the investments, in millions of dollars, for the Brazilian software and services market, adds the stage of software maintenance and evolution. Other specific characteristics of the maintenance context are: continuous and repetitive nature, focus on individual response to change requests, and uncertainty of new scope changes (APRIL; ABRAN, 2008, PORT;TABER, 2018). Thus, the improvement of the process based on specific reference models for the development of a software project is not totally adequate to the specifics of the software maintenance and http://www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 evolution context (KAJKO-MATTSSO, 2001;KAJKO-MATTSSON;SNYGG;HAMMARGREN, 2012;NIESSINK;VLIET, 2000;PINO et al., 2012) and has caused damages to organizations. The problem may become even worse when observed in the micro and small Brazilian companies sector. According to the Brazilian Software Association (ABES), in a survey conducted in 24 Brazilian states, of the software companies, 86% of them are classified as micro and small companies (SUKARIE-NETO, 2018). This represents a significant portion of the sector.

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Considering this scenario, the main challenge faced by these organizations is the limitation of resources: people, time and budget. These limitations lead to insufficient processes, methodologies, guidelines, tools and documentation required for software maintenance. Also in the context of some micro-software organizations, a single product may be the basis of the organization's business model, and new product development activity may be rare or nonexistent.
The Knowledge Engineering Body of Knowledge (SWEBOK) (EDBERG; IVANOVA, 2011) presents some of the issues related to software maintenance, grouped into four categories: i) Technical issues; ii) management issues; iii) Estimate of Costs; and iv) measurement, the entities that can be subjected to measurement are process, resource and product. If applied correctly, the measurement has great potential to contribute to a better understanding of the maintenance and evolution of software and to motivate necessary changes in processes and methodologies (ROMBACH; ULERY, 1989).
However, measuring quality, productivity, and other aspects of software maintenance within an organization is a difficult task (DESHARNAIS; APRIL, 2010, OKIKE, 2018), mainly because the nature of the work differs from software development by design (ALORAN; EID; AL-SARAYREH, 2015), especially by the characterization of its work continuous and on demand.
Therefore, the objective of this work is to observe how the implementation of a measurement process within a Software Process Improvement (SPI) program based on quality models in micro and small companies, which also work with software maintenance, takes place in practice.

LITERATURE REVIEW
According to DeMarco (DEMARCO, 1986), one cannot control what cannot be measured, which demonstrates the need for measurement with respect to the Software http://www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 Engineering context. Good management based on indicators supposes the possibility of predicting the future behavior of software products and processes, from the moment we have reliable and appropriate information. In this way the measurement becomes an important practice since one cannot predict what cannot be measured (KITCHENHAM; PFLEEGER; FENTON, 1997). With this, several quality models and guidelines for improvement were created based on the measurement process.

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SPI can be implemented ad hoc, but process improvement implemented systematically, based on models, standards or approaches, has been shown to be more efficient and with better results for organizations (MATA-LIMA et al., 2016;MUTAFELIJA;STROMBERG, 2003;SALVIANO 2006).
Among the existing quality models, the most widely disseminated in the context of micro and small Brazilian companies that also work with the software maintenance stage are the CMMI® model will be used to validate the field study due to its international recognition.
In CMMI®, the process area that represents and contains measurement practices is called Measurement and Analysis.

The CMMI® Measurement Process
The goal of the Measurement and Analysis process area is to develop and sustain the measurement capability that is used to support information management needs (CMMI, 2010).
This general purpose is divided into two specific objectives: (1) Align measurement objectives and activities with identified information needs and objectives; (2) Provide measurement results that address the needs and objectives of identified information. This process area, as well as the CMMI® model in all its definitions, presents only what is charged, what the model expects from the organization to consider the process area implemented, ie "what to do" and not "how to make".

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http: //www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 The Measurement and Analysis process area, as well as the others, is composed of generic objectives (SG) and specific objectives (SP): In addition to the practices of the Measurement and Analysis process areas, at the maturity level 2, we have the generic objective (GP2.8), which emphasizes that the processes must be monitored and controlled, an activity that also involves measurement at this level.
Because it is a generic practice, it focuses on all process areas of maturity level 2.

RELATED WORK
The related works were identified from a Systematic Review of Literature (SRL), which is a means of identifying, evaluating and interpreting all available research relevant to a particular research question, topic area or phenomenon of interest (KITCHENHAM, 2007). The work analyzed shows, as of great relevance to the organization, the implementation of a measurement process to evaluate the work performance and evolution of the process

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http: //www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 improvement, always highlighting its organizational benefits focused on improving the quality of product delivery and / or service. Several authors (AKINGBEHIN, 2008 It is also agreed between the authors of the works, the need for some quality model to evaluate the proposed indicators, with CMMI® being the most cited model among them. Still to aggregate the results, an analysis was performed on the of the organizations observed in the studies, with only 3% referring to micro-enterprises (given that this proves the need for research in this sector), and more than 50% of the studies at least mentioned the size of the organization, generalizing the indicators, the measurement process has to adhere to the size of the

RESEARCH METHOD
After SRL performed by the authors, a field study was planned and conducted. A field study or study is a case study of the real world without necessarily having direct intervention by the researcher / observer responsible (KITCHENHAM et al. 2002). http://www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 When conducting a field study it is important to keep in mind that this type of specific study seeks to deepen a given reality, it is basically carried out by means of direct observation of the activities of the group studied and, if necessary, interviews with the purpose of capturing explanations and interpretations about the studied context. This type of study differs from the case study, since the case study can be defined as an exhaustive, deep and extensive study of one or a few units, empirically verifiable, in a way that allows its comprehensive and detailed knowledge (YIN, 2015).

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Field research has the purpose of observing facts and phenomena of the way they occur in reality and also collecting data on the elements observed and then analyzing and interpreting them, based on a solid and well-founded theoretical foundation, in order to understand and explain the problem that is the object of study of the research. The field study does not provide specific techniques for data collection, and they may vary according to the needs of the work developed.
The field study performed in this work is characterized as an exploratory and descriptive study where the researcher analyzes, observes, records and correlates aspects, phenomena or variables that involve facts, without manipulating them. The investigation takes place without the interference of the researcher, where he only "seeks to discover, with the possible precision, how phenomena occur, their relation and connection with others, and also their nature and their characteristics" (BERVIAN; CERVO;SILVA, 2002). Being also exploratory, it aims to provide greater familiarity with the object of study. It is necessary to define a research process that identifies the nature of the phenomenon and points out the essential characteristics that one wants to study (KÖCHE, 1997).
Characteristics of the descriptive research are spontaneity (the researcher only observes the phenomenon), naturality (the facts are studied in their natural habitat) and a high degree of generalization, which dispenses with great concern about the representativeness of the selected sample. In addition, it is a qualitative study, we sought to verify a phenomenon through observation and study (KIRK; MILLER; MILLER,1986).
The field research conducted for the execution of this work followed the steps that were adapted from the method proposed by Neto (NETO, 2002): (1) definition of the research objective; (2) choosing the context that the study would run; (3) selection of companies to be observed;

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http: //www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 observation of the context that was defined for the execution of the research; (5) data collection through interviews and analysis of available documents; and (6) analysis and conclusion of the aspects observed in field research.

Contextualization
This field research was conducted in the city of Maringá-PR, which is a regional hub in software development, and has the presence of a Local Productive Arrangement of Software The improvement project was carried out jointly with SWQuality Consultoria e Sistemas, a Brazilian company recognized nationally and internationally for its successful work with SPI in the most diverse models and business sectors, also accredited by the CMMI Institute as an official evaluation institution of the model.
The purpose of this observation is to understand how the measurement process aligned with quality models can be implemented in micro or small companies that work with software maintenance.
The sample for convenience refers to the acquisition of answers by companies that have availability and are willing to collaborate (KITCHENHAM; PFLEEGER, 2008).
The companies were selected using criteria defined according to the study objectives, where there is no knowledge of the probability that a given population element will be selected.
To select the samples, companies were chosen that, at the time of the research, accepted to be observed and were available for observation of the researcher.
To reduce the chance of choosing samples that did not contribute to the study objective, the following criteria were taken into account: • micro or small businesses; • perform maintenance of your software products; • be implementing the software process improvement aligned to some quality model that focuses on measurement; • be implementing the SPI with the support of some specialized external consulting; and • have the objective of undergoing a formal and official evaluation of the quality model in question.
Seven micro and small Brazilian companies from the Maringá region of Paraná were selected. All observed organizations had as main activity the maintenance and evolution of their software products. Some of them used agile practices, and two of them followed the cascade model of software development, they had 1 to 3 teams, which were composed of 4 to 8 professionals who worked throughout the maintenance cycle.
For all organizations observed, the main product was an Integrated Enterprise Management System (EMS), which varied the market niche that the company operated, ranging from stalls to medical laboratories, also some of them had government software.

Study Execution
The implementation of the process improvement and definition of the measurement guide in the organizations was given by biweekly visits where the consulting company trained with formal orientations or from mentoring the employees of the companies and left activities for them to execute. These activities would be reviewed on the next visit and the cycle would be repeated until the end of the project. http://www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 This project was divided in phases as it is possible to see below, the visits of the consultancy in the companies were accompanied by the researcher / observer so that it could carry out its analysis.

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• Phase 1 -Diagnosis: At this stage, the consulting team carried out a diagnosis of the companies with the objective of identifying to what degree the organization adhered to the processes required by CMMI®-DEV level 2, to understand how the work was carried out in the day to day of the company, identification of problems and alignment of expectations with those involved about the improvement program. Based on the information collected, the consultancy carried out high-level planning considering the goals established for the project.
• This phase took place through separate job interviews: directors, managers, architects, requirements analysts and developers were heard.
• Phase 2 -Kick-off and Training. The improvement program was presented to all employees of the company with the goal of leveling expectations and informing the kick-off of the project. In this presentation, doubts were clarified and guidance was given regarding the structuring of the environment conducive to the accomplishment of the works.
• Training was also conducted in order to level the employees of the companies directly involved in the CMMI®-DEV implementation project, in this training the model was presented, as well as its process areas, and the general objective of each of them, as well as also a high-level schedule of the implementation of these processes, with milestones to review the progress and possible planning of the other activities to be developed.
• Phase 3 -Reformulation of the Software Development Process: After mapping, the current software development and maintenance processes were reformulated according to the improvement program, good practices and new procedures were added using the SCRUM agile method (SCHWABER, 1997) and model guidelines as a reference.
• A close follow-up of the consulting team and the team of developers with training and mentoring were carried out to better transpose the work method with gradual changes.
Adaptations in the SCRUM method were performed according to the need and limitation of each organization (sprint size, distribution of roles and responsibilities, etc.). Parallel to this phase, Phase 4 has already begun.
• Phase 4 -Deployment of Management Process Areas: At this stage, emphasis was placed on the process areas related to the management of the requirements, work and http://www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 the monitoring and follow-up of the day to day. Processes were defined to make these activities as fluid as possible. In the monitoring and control stage, indicators proposed by SCRUM were initially used, such as Team speed. The number of hours began to be followed to serve as a basis in the calculation of the other indicators to be defined.

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• Phase 5 -Support Process Area Deployment: At this stage, the areas of Process Management Configuration, Product Quality Assurance and Process and Measurement and Analysis were worked directly. For these process areas, the work was structured and adapted to the reality of each of the organizations. Strategies to meet the requirements of the model were defined without hurting the agility of work in companies. These processes were implemented, monitored and adapted whenever necessary.
• Phase 6 -Institutionalization of Processes: At this stage the processes were institutionalized in all the companies' projects. This stage began next to step 3 and followed gradually until the completion of the implementation of the processes adhering to the proposed quality model.

Discussion of the Field Study
During the execution of the field survey, during each of the phases, notes were made to record the observations that made analogy to the process of process improvement with the measurement process. Whenever necessary, non-systematic questions were asked both for the organization's employees and for the members of the consulting firm. http://www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 Also during observation of the improvement project phases, each goal of the CMMI® Level 2 Measurement and Analysis process area was related to the steps to check if the work performed meets the needs of the models. This relationship can be seen in Table 1. Phase 1 did not correspond directly to any of the practices, but was necessary for the start of the improvement project. In the diagnostic carried out were collected some strategic objectives of the managers and some information needs that later would serve as input for the definition of the indicators, since, according to the existing approaches to definition of measurement process, they should be aligned.

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Phases 2, 3, and 4 are more focused on process definitions and redefinitions, where the objectives of measurement, measurement, data collection and storage, and analysis criteria have been defined, validated and consolidated in Phase 5. In these phases the process was also executed, but its focus was on the alignment of the Measurement and Analysis activities.
Phase 6 had as main objective the maturation of the execution of the previously made definitions, such as obtaining, analyzing and storing the data and its communication to the involved ones, being the focus in providing the measurement results. Finally, Phase 7 encompasses all specific practices and achieves specific objectives.
The Measurement and Analysis process, as well as the other CMMI®-DEV Level 2 processes, were briefly addressed in employee leveling training, ensuring their commitment and engagement in building new processes in organizations.
With the management of the teams following the SCRUM method, a time-box was defined that, together with the teams' timekeeping, allowed a periodicity for the collection of data that would generate the indicators, initially guided by method metrics such as team speed, based on the ability of story points with the technique of estimative Planning Poker (GRENNING, 2002). By dividing management into short and fixed time cycles, SCRUM assisted in standardizing the work.

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http: //www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 In the implementation of the management processes, the development management was carried out following a previous planning, with organization and prioritization of the demands, where the objectives and parameters and goals of monitoring were defined by the responsible one. From these parameters, it was possible to observe deviations in the defined goal, making it possible to take corrective actions whenever necessary.
The Measurement and Analysis process was worked with greater emphasis in the implementation of the support process areas, having all its objectives worked directly, seeking strategies to satisfy all the expected results of the reference model. Based on the measurement objectives and needs already identified and the metrics created, a Measurement Guide was established where this information was recorded and related to the indicators and steps for the execution of the process were described.
The Measurement Guide also defined the collection and storage procedures to analyze this information and how the entire process should be followed, with responsible and welldefined steps. The strategy for the execution of the process was also established, which includes collecting and analyzing the data, storing it and communicating the results to those involved and interested. It should be noted that this guide and its indicators should be reviewed periodically, since the strategic needs of companies may vary over time.
In addition to a document name, company name and document maintainer header, this guide has the following sessions: 1. Objectives and Need for Information: A relationship between each of the defined indicators, which strategic objective it helped measure or respond to, and why that information was necessary for the organization.  http://www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 f. Unit of measure: which unit of measurement was used. i. Analysis Criterion: This field was based on the goal, and deviation ranges were defined and classified as "OK", "Alert", "Critical" and "Attention". For each of the classifications, an action was defined and should be taken for the normalization of the indicator. j. Storage: where the indicator will be stored after collected for reuse and history.

FINAL CONSIDERATIONS
The field study presented in this study provided the interaction with the industry in several stages of its development, which increases the quality and relevance of the work (GONÇALVES; SILVA, 2017; RODRÍGUEZ; KUVAJA; OIVO, 2014), approaching companies of the university whose objectives are always converging with respect research and production of new solutions.
Like the other process areas, Measurement and Analysis was observed in practice in the organization. All expected results have been reviewed and, if necessary, improved in the last phase. The adherence of the Measurement and Analysis process was evaluated. All expected results were checked to identify possible weaknesses and opportunities for improvement. http://www.ijmp.jor.br v. 11, n. 2, March-April 2020ISSN: 2236-269X DOI: 10.14807/ijmp.v11i2.1028 Finally, it was possible to observe how the Measurement process was implemented in micro and small software companies that also work with maintenance. The definition of the guide was a joint work between each company and the consultancy, and the indicators could vary between organizations.

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Because CMMI® level 2 does not require the process to be defined in detail, measurement was followed in the guidelines found in the guide book section.
One of the most interesting points observed was that the indicators to be measured were constructed in a "personalized" way for each company, varying according to the business, development process and strategic objectives.
At the end of the implementation, a set of indicators was generated to support the managers of the organizations and the 7 analyzed companies were submitted to official evaluation by SCAMPI A (SCAMPI, 2011), which is the official method indicated by CMMI Institute (current owner of CMMI®) to evaluate the model regarding the maturity and capacity of organizations. The method consists of rules and guidelines that guide the evaluation of the model, obtaining the level 2 certification of maturity.
As a future work to this study, we intend to find common points between the deployments in the companies and their adaptations so that it is possible to propose a standard base measurement guide for this type of organization (micro and small company that operates with software maintenance). From this standard guide, the company can evolve it according to its strategic objectives and will be independent in the implementation of the process of Measurement and Analysis.