Benchmarking sustainability performance: the ... - Wiley Online Library

Journal of Public Affairs (2016) Published online in Wiley Online Library (www.wileyonlinelibrary.com) DOI: 10.1002/pa.1606

■ Academic Paper

Benchmarking sustainability performance: the next step in building sustainable business models Elliot Maltz1*, Henry H. Bi1 and Mark Bateman2 1 2

Atkinson Graduate School of Management, Willamtte University, Salem, Oregon, USA ENSOGO Analytics

Developing sustainable business models incorporating effects on people, profit, and planet is becoming an increasingly important strategic issue. Benchmarking with peer companies can assist a company in setting goals of improving its performance. As such, developing a methodology for effectively benchmarking sustainable business practices is an important step in the evolution of sustainability management. However, a company’s sustainability performance is composed of many elements that may involve difficult tradeoffs, and its performance may vary over time. In this paper, we propose a data-driven approach of innovatively adapting statistical process control charts, conventionally used in quality control, to simultaneously compare multiple performance measures and analyze variation in both trend and performance among companies in a given industry. We apply this approach to benchmarking the sustainability performance of companies in the US utility industry and demonstrate it is robust and reliable for benchmarking the performance of companies in virtually all industries. Copyright © 2016 John Wiley & Sons, Ltd.

The study and practice of sustainability is maturing. One could argue that it has reached a tipping point whereby incorporating sustainability into business practices is becoming a mainstream issue in strategy. Over the past two decades, scholars in a variety of business disciplines have focused largely on whether to engage in sustainable business practices (e.g., Griffin & Mahon, 1997; Margolis & Walsh, 2003; Orlitzky et al., 2003; Porter & Kramer, 2006). More recently, a number of scholars have focused more on how to engage in sustainable business practices (e.g., Epstein et al., 2010; Maltz & Schein, 2012). Evidence from practice shows a similar trend. Massachusetts Institute of Technology MIT Sloan Management Review and the Boston Consulting Group recently conducted their third annual sustainability survey of executives and

*Correspondence to: Elliot Maltz, Atkinson Graduate School of Management, Willamtte University, 900 State Street, Salem, Oregon, USA. E-mail: [email protected]

Copyright © 2016 John Wiley & Sons, Ltd.

managers worldwide. The results indicate that an increasing number of managers and companies are taking sustainable business practices seriously: • 70% of companies that have placed sustainability on their management agenda have done so in the past 6 years, and 20% have done so just in the past 2 years. • Two-thirds of respondents said that sustainability was critically important to being competitive in today’s marketplace, up from 55% in the 2010 survey (Kiron et al., 2012). However, even with this increased emphasis on sustainable practices, there is still widespread skepticism that companies have the tools to understand how to shape efforts effectively. According to a recent survey, only 38% of Chief Executive Officer believe they can accurately quantify the value of their sustainability efforts, and 37% see this as a major impediment to accelerating their efforts (UN Global Compact-Accenture 2013).

E. Maltz, H. H. Bi and M. Bateman A common way to develop successful practices in a given area is to benchmark firm performance relative to the best performing competition (Arthur, 2011; Breyfogle III, 2003; Brook, 2010; George et al., 2005; Kubiak & Benbow, 2009; Martin, 2007; Tague, 2005). Learning best practices from high-performing companies may not only help a specific company understand how to maintain high sustainability performance but also find standardized practices that can help entire industries improve. However, it is important to recognize that a number of critics see the growing business interest in sustainability as little more than a thinly veiled and cynical ploy designed to attract socially and environmentally conscious consumers (Jones et al., 2015). This makes it difficult to identify true sustainability leaders, as there may be wide variation between professed and actual commitment to corporate sustainability. In this paper, we propose an innovative, datadriven approach traditionally used in quality control. It simultaneously compares multiple performance measures to analyze variation and trends among companies in a given industry. This approach, statistical process monitoring, is flexible enough to assess sustainability performance at multiple levels. Thus, it can be used by managers to assess organizational sustainability and more granular levels of performance. In the next section, we highlight the challenges of benchmarking sustainability practices. We then describe the theory and methodology of statistical process modeling and apply this approach to benchmarking the sustainability performance of companies in the US utility industry. This approach meets the criteria for a strong benchmarking methodology that can be used in any industry: • It measures actual performance on multiple financial (profit), environmental (planet), and social (people) measures of sustainability. • It indicates tradeoffs between various financial, environmental, and social measures. • It measures the consistency and trajectory of performance both at the dimension level (people, profit, and planet) and at the overall firm level incorporating all three dimensions.

Challenges in benchmarking sustainability performance Before we can benchmark against high-performance companies, there is a basic question that needs to be answered: How do we identify the top performers? To answer this question requires a reliable methodology with appropriate metrics for identifying highperforming companies. However, developing such a Copyright © 2016 John Wiley & Sons, Ltd.

methodology for benchmarking a sustainability strategy faces a number of significant hurdles. Sustainability Strategies Must Satisfy Diverse Goals Proponents of sustainable practices argue that building responsible enterprises requires considering and measuring impacts on people, profits, and the planet (Elkington, 1998). Companies seeking to pursue sustainable strategies must find ways to integrate the tradeoffs between social, economic, and environmental impacts in decision-making (Epstein et al., 2015). In doing so, they face three challenges. Managers often face significant opposition from shareholder value advocates who characterize the triple bottom line as a zero-sum game in which creating value for society reduces value for the firm (Lazlo & Zhexembayeva, 2011). Managers arguing for investments in the social and environmental dimension of sustainability are tasked with demonstrating these investments, at minimum, do not hurt returns to shareholders. As such, any benchmarking methodology focused on improving the social and environmental dimensions of an organization’s business model must consider the relationship between sustainability initiatives and financial performance. Without this aspect of the benchmarking methodology, companies’ deployment of capital for sustainability initiatives may be blocked. Moreover, it may even be the case that achieving some ‘socially responsible’ goals may come at the expense of some other socially desirable outcome. For instance, investing in technology to reduce the use of toxic material in a manufacturing process may make the workplace safer for the employees. However, the introduction of the advanced technology can often generate more output utilizing fewer workers. Thus, a second requirement for any good benchmarking methodology assessing sustainability strategies is to be flexible enough to measure at multiple levels of performance. That is, it should be able to assess performance at the organization level, the individual dimension (people, profit, and planet) level, and even different key performance indicators (KPIs) of each dimension. A related issue is how to assess tradeoffs between goals (Epstein et al., 2015). It is not unusual for a strategy to have diverse goals. When all of the goals are financial metrics, it is relatively easy to assess comparative performance. Simply consider the cost savings and/or revenue enhancements associated with achieving the goals. However, assessing relative tradeoffs between financial outcomes and reducing carbon outputs or enhancing the welfare of the community are more complicated because performance metrics are typically measured on J. Public Affairs (2016) DOI: 10.1002/pa

Benchmarking sustainability performance different scales. As such, any benchmarking methodology measuring aggregate levels of sustainability must evaluate diverse performance outcomes utilizing a common scale. Sustainable Business Models Must be Evaluated from a Long-term Perspective The performance of any strategic initiative needs to be measured based on a longer term return on investment. However, measuring over a longer term is particularly important when assessing sustainability performance for at least two reasons. Sustainability is a long-term concept. Conceptually, a sustainable business model meets the needs of the present without diminishing the opportunities for future generations (Brundtland Commission 1987). Given the relative newness of the concept, it is difficult at this point to identify datasets that span generations on all of the performance criteria at the firm level. Nevertheless, it is important to develop a methodology that can measure consistency in firm performance over a longer term basis. High-performance organizations should have consistent and/or improving sustainability performance over multiple years. The relative emphasis on dimensions of sustainability performance can change because of macro influences. If one is to consider the broader effects on society, one of the problems managers face is that the perceived value of social initiatives shifts over time. If the economy is in recession, then outcomes that lead to higher employment may become more prominent. If an ecological disaster has recently occurred, then perceived societal value of environmental preservation often increases. This can lead to ad hoc, as opposed to strategic sustainable initiatives. Moreover, recent studies suggest firms facing severe economic constraints develop different stances in terms of their commitment to socially responsible investments (Bansal et al., 2015; Barnett et al., 2015). As such, it is important to understand which strategies lead to persistent sustainability performance. If one is benchmarking against high-performance companies, in part, to identify best practices, assessing the commitment to the strategy in good times and in bad is necessary. By measuring on a longitudinal basis at both the dimension and the organization level, one can assess the reliability of performance in different conditions. As such, a third requirement for any good benchmarking methodology to assess sustainability strategies is to measure consistency of sustainability performance. In short, benchmarking sustainability performance can assist a company in setting goals of improving its performance. A company’s performance is composed of many things and may vary over years. It is worth Copyright © 2016 John Wiley & Sons, Ltd.

close study to understand how high-performance companies achieve and sustain their performance over years. Yet the unique challenges in assessing sustainability tradeoffs over the longer term make this a challenging task. In the next section, we describe an approach suitable to meeting these challenges—statistical process monitoring. Statistical process monitoring theory Statistical process monitoring originated in the 1930s as a way to identify process changes due to ‘assignable causes’ in manufacturing (Shewhart, 1931). It has become an established way to improve the reliability of manufacturing processes (ASTM, 1962; Grant & Leavenworth, 1996). More recently, the methodology has moved beyond manufacturing to monitoring of financial data (Frisen, 2008) and health care and public health surveillance (Woodall & Montgomery, 2014). From a statistical process monitoring perspective, variations in performance are due to a number of causes. Chance causes are the causes of variations that may be inconsequential and cannot be identified. When only chance causes exist, the averages, the standard deviations, or any other functions of random samples will exhibit statistical stability. Assignable causes generate greater variations in data than results from chance. When assignable causes exist, they can be detected by the use of control charts. An examination of these charts provides a way to detect lack of statistical stability in a KPI that indicates one or more assignable causes. Types of control charts Control charts relevant to our purpose include average charts, standard deviation charts, and individual charts (Kubiak & Benbow, 2009; Montgomery, 2012). Average charts plot the average of the set of companies on some KPI. Standard deviation charts plot the standard deviations of the set of companies on a KPI. An individual chart plots a time series of observations for a single company within the set on a KPI. Control limits Each control chart consists of a center line, an upper control limit (UCL), and a lower control limit (LCL). The center line indicates the expected or average value of the sample. The control limits, conventionally defined as three sigma control limits, are placed three standard deviations above and below the center line, respectively. (See technical appendix for the calculations of the center line and three sigma control limits of the various charts.) J. Public Affairs (2016) DOI: 10.1002/pa

E. Maltz, H. H. Bi and M. Bateman Control chart analysis The out-of-control points (above or below control limits) on control charts are said to be attributed to assignable cause variation. Such points, regardless of whether they constitute ‘good’ or ‘bad’ occurrences, constitute exceptional variation that is not attributable to chance but instead is due to assignable causes. In short, through the use of control charts, a manager can identify the presence of exceptional variation within the data and therefore indicate that a search for assignable causes may be appropriate. In other words, a company does not have exceptional performance by chance, or at least its exceptional performance cannot be attributed to chance. If a company performs significantly better or worse than its peers, then its performance can be thought of as due to certain assignable causes. Such causes may include, but are not limited to, exceptional management practices, training, and investment. Thus, it is viable to use control charts to display whether a company has exceptional performance in comparison with its peers. Because a control chart is divided into three parts by its UCL and LCL, each control chart provides an intuitive instrument for performance comparison on a particular KPI. However, the unique value of the methodology is to look at the three control charts in combination. Take, for example, a typical KPI that might be used in measuring performance on the planet dimension— waste. If the average amount of waste is above the UCL, that would indicate low performance due to assignable causes. Similarly, those below the LCL would be considered high performers. However, for each KPI, a company may have high, normal, or low performance, as indicated by the average chart; and high, normal, or low variability of performance, as indicated by the standard deviation chart, relative to other companies in the sample. If a company is above the UCL on the standard deviation chart, this

Table 1 Average chart (x chart) Standard deviation chart (s chart) Individual chart (X chart)

indicates high variability in performance and hence, less reliability in this KPI. That is, companies scoring below the LCL on the average chart (indicating good performance on waste) may have done so through one good year. If, on the other hand, the company was also below the LCL on the standard deviation chart, this would indicate relative stability and hence consistency in delivering low waste relative to its peers in the sample (Table 1). The individual control chart looks at one company in terms of the trend of performance. A company that has the majority of its later data points below its own mean is considered to have an improving trend in performance (remember, lower is better in terms of waste). A company with a majority of its later data points above the mean is considered to have declining trend in performance. Otherwise, no trend is indicated (Table 1). In short, an individual chart can be used to demonstrate the direction and amount of changing performance. Rational subgrouping A topic commonly discussed in the context of statistical process control is rational subgrouping (JonesFarmer et al., 2014). Rational subgrouping concerns what to measure and how to select samples. Its implementation relies on process knowledge and some common sense. Shewhart (1931, pp. 288–299) emphasizes the importance of rational subgroups in terms of identifying out-of-control events and finding the assignable causes of the events.

Benchmarking sustainability using statistical process control theory Our goal in utilizing statistical process control theory is to identify companies that can be identified as having positive exceptional variation. These companies can then be used as benchmarks for managers. They can

Waste generated per dollar of revenue

Above UCLx Between UCLx and LCLx inclusively Below LCLx Above UCLs Between UCLs and LCLs inclusively Below LCLs At least 50% of all data points in a row above the center line x toward the end of the time series At least 50% of all data points in a row below the center line x toward the end of the time series Otherwise


Low performance Normal performance High performance High variability Normal variability Low variability Decreased performance Improved performance No trend

J. Public Affairs (2016) DOI: 10.1002/pa

Benchmarking sustainability performance also be used to identify assignable causes and ultimately best practices. In this section, we identify choices that need to be made in the development of control charts to determine top performers in terms of sustainability practices. There are several noteworthy issues in discovering the top performers: First, no single measure guarantees organizational success (AMA, 2007; Kaplan & Norton, 1992); instead, ‘high performance is a composite of many things’ [p. 6] (AMA, 2007). A number of studies have considered various financial and operational measures (AMA, 2007; Jain, 1998; Joyce et al., 2004; Kaplan & Norton, 1992; Kotter & Heskett, 1992; Muldrow et al., 2002; Peters & Waterman, 1982; Zook & Allen, 2001). Most of those studies use more than one metric (KPI) in measuring performance (e.g., revenues, net profits, total shareholder returns, return on investment, etc.). However, as noted earlier, to identify KPIs in the sustainability context requires going beyond financial metrics. As such, identifying KPIs to measure people, profit, and planet dimensions of performance in a way that can be set to a common scale is required.

Identifying Appropriate Key Performance Indicators The KPIs chosen should be consistent with the overall sustainable business strategy. A number of authors have developed frameworks for identifying the metrics of sustainability on which to focus (e.g., Porter and Kramer, 2005; Maltz & Schein, 2012; Senge et al., 2008). Generally, these frameworks suggest firms should identify where they have the capacity to make a significant difference (i.e., have a competence in the area) and where sustainability enhances the longterm viability of the firm in some way (i.e., confers longer term competitive advantage).

Choosing a Time Frame The long-term viability aspect directs us to the second managerial decision. The firm needs to decide the span of time used to measure the KPIs. Maintaining high performance is a major challenge for any organization, and many companies are unable to sustain high performance. Hence, it is more meaningful to identify the high performers with staying power over multiple years rather than those with fleeting success [p. 5] (AMA, 2007). Different spans of time have been used in assessing the performance of companies. Some have used as few as 4 years (Jain, 1998), while others extend the analysis to decades. Two considerations are relevant in identifying the appropriate span of time to evaluate sustainability initiatives. First, how long does the firm believe it Copyright © 2016 John Wiley & Sons, Ltd.

will take to see expected returns from sustainability investments? For instance, if the firm includes carbon reduction and accompanying economic returns due to improved brand positioning as a KPI, it will need to measure results over a longer term period. If, on the other hand, the firm is focused on waste reduction and accompanying cost savings, the firm can consider a shorter period. The second aspect relates more to the shifting public sentiments toward different dimensions of sustainability. As noted previously, in periods where economic growth is vibrant, stakeholder concerns lean more toward the people and planet dimensions of sustainability than in recessionary periods. This can lead to shifting commitments to overall sustainability efforts. In order to account for this effect, the benchmarking period should include periods of economic strength and economic weakness. Choosing a Rational Subgroup The third management issue is which companies to benchmark against (i.e., what is the rational subgroup). The scope and number of organizations selected for close analysis can affect the findings. The types of ‘people’ and ‘planet’ choices that firms can make and still be viable will vary widely by industry. The environmental impacts and the assignable causes associated with those impacts are very different for Nike and Coca Cola because of the inputs to and processes inherent in their manufacturing processes. In sustainability benchmarking efforts, the rational subgroup should be constrained to firms in the same industry. To sum up, identified high-performance organizations should exceed in more than one measure and should have consistent performance and/or sustained improvement over multiple years, including during periods of high and low economic performance of the industry. Metrics that are chosen should be consistent with the strategic sustainability goals of the firm, and the sample should be limited to firms in the same industry. With this in mind, we now turn to a case study to illustrate the statistical process control methodology.

BENCHMARKING THE SUSTAINABILITY PERFORMANCE OF COMPANIES IN THE US UTILITY INDUSTRY In this section, we apply the control chart methodology to benchmarking the sustainability performance of the companies in the US utility industry. As noted J. Public Affairs (2016) DOI: 10.1002/pa

E. Maltz, H. H. Bi and M. Bateman earlier, our methodology can assess performance at the individual dimension level and at the overall organization level incorporating people, profit, and planet dimensions. Dimensions are measured with multiple KPIs. To understand how to assess dimensions, we first discuss KPI level assessment.

Assessing company performance on a single metric: key performance indicator level analysis in utilities industry In the case of the utilities industry, we identified 10 performance metrics reflecting people, profit, and planet criteria. The list of metrics is summarized in Table 2. Consistent with the primary concerns of most companies we have spoken to and the utilities industry in particular, we have used more planet and profit metrics than people metrics. Because utilities are, in general, such a high-fixed cost industry, we used asset-based profit measures extensively. Consistent with concerns of the industry, we focused heavily on emissions and waste in the planet measures. On the basis of the control limits described earlier, three charts can be developed for each metric to illustrate how the companies performed at the KPI level. Figures 1–2 illustrate the average and standard deviation charts for the performance metric ‘Planet2 (lbs. Table 2

List of utility industry key performance indicators

Performance No. measure 1

Planet1a

2 3 4

Planet2b Planet3b Planet4b

5

People1c

6

People2c

7 8

Profit1 Profit2

9

Profit3

10

Profit4

Description Total Toxics Release Inventory emissions Total amount of generated waste Number of reported spills Total dollar value of environmental fines and penalties levied against the company Aggregate initial penalty for the U.S. Occupational Safety & Health Administration violation Whether a company is fined by the U.S. Equal Employment Opportunity Commission during a year Asset turnover = revenue/assets Financial leverage = total debt/total equity Gross margin % = (revenue – cost of goods sold)/revenue Operating margin = operating income/ net sales

a

Source: Environmental Protection Agency (http://www.epa.gov/). Source: Environmental Protection Agency (http://www.epa.gov/). Source: http://gcmd.nasa.gov/records/GCMD_EPA0147.html.

b c

of waste)/Revenue (million US$)’. (Note all of the non-profit metrics were normalized based on total revenue generated.) The period analyzed was 2004– 2010 (7 years) to account for potential changes in performance based on the economic stress caused by the financial crash of 2008. The variance in average performance was quite large across the 80 companies analyzed (Figure 1). The triangles in red indicate companies generating waste higher than the control limits over the period, indicating poor performance. The diamonds indicate companies within the control limits, and circles indicate companies with waste levels less than the control limits, indicating significantly superior performance. The data points above UCLx or below LCLx in Figure 1 indicate the presence of assignable causes of variation in the averages of the KPI ‘Planet2 (lbs.) of waste/Revenue (million US$)’. The range of standard deviations was also quite large across the 80 companies analyzed (Figure 2). Moreover, an examination of the two figures indicates that the patterns of average and variance are quite different. Figure 2 indicates the variability of some companies’ performance on this KPI is not statistically stable (their standard deviations are above the UCLs). To identify consistent high performers, we examine Figures 1 and 2 in combination. The arrows in the figures indicate Firm #16, which is below the LCL on both average and standard deviation of its performance over the period, indicating consistent low generation of waste.


Assessing trends at the individual company level using control charts For each company and each performance metric, we can use the formulas in the technical appendix to calculate the values of the center lines and control limits to assess trends in performance at the individual company level. Based on these calculations, we have provided three individual charts illustrating different patterns of performance (Figure 3). According to the rules in Table 1 (four or more data points above or below the center line at the end of 7 years), Firm #1 has an improved performance in the measure of ‘Profit3 Gross Margin % = (revenue – cost of goods sold)/revenue because it has the most recent four points above the center line x . Firm #4 does not have an obvious pattern of improved or decreased performance. Firm #19 has a decreased performance. As can be seen from the analysis previously, organizations can vary in their performance based J. Public Affairs (2016) DOI: 10.1002/pa

Benchmarking sustainability performance

Figure 1 Average control chart for waste generation per dollar of revenue

Figure 2 Standard deviation control chart for waste generation per dollar of revenue

Figure 3 Individual trend control chart for waste

on the raw performance relative to the rest of the industry, variability of performance relative to the industry, and the trend in performance. From a benchmarking perspective, for firms looking to Copyright © 2016 John Wiley & Sons, Ltd.

improve on an individual metric, it will be most instructive to look at firms with high performance, low variability in performance, and an improving trend in performance. J. Public Affairs (2016) DOI: 10.1002/pa

E. Maltz, H. H. Bi and M. Bateman

CATEGORIZING SUSTAINABILITY OF ORGANIZATIONS AT THE DIMENSION LEVEL (PEOPLE, PROFIT, AND PLANET) As illustrated previously, we can benchmark the companies’ performance level, variability, and trend by using control charts systematically to evaluate KPIs. Then, based on the control charts, we follow Table 1 to find high, normal, and low-performance companies on each KPI based on their performance variability and trend. However, that still leaves us the problem of dealing with tradeoffs. Companies can be adopting policies to maximize performance on a single KPI at the expense of other aspects of the dimension. To assess dimension level sustainability performance, we use Table 3 to categorize the companies’ performance level, variability, and trend within a particular dimension (i.e., people, profit, or planet) and overall across dimensions. Based on these three characteristics, companies in the industry can be characterized as stars, climbers, tumblers, and losers.

Climbers, tumblers, and losers From a strategic perspective, it is also worth looking at both the climbers and the tumblers. Benchmarking against climbers may help identify emerging trends in the rapidly shifting ‘planet’ landscape. By ensuring that all the metrics are at least at a normal level and not declining, we reduce the likelihood that an improvement in one area is at the expense of another. The decline in performance of the tumblers may indicate a company in distress that is reducing their commitment to sustainability. The losers may be looked at to indicate suboptimal tactics as intentionally or unintentionally they are not performing well. Clearly, these firms do not have a dimension level sustainability strategy. Tables 4, 5, and 6 categorize the companies in the utilities industry as stars, climbers, tumbles, or losers for the planet, people, and profit dimensions of sustainability.

Assessing assignable causes for outperformance Stars Our methodology identifies the ideal benchmarking candidates. By imposing a high bar of all metrics within a dimension having a high or normal level of performance, we reduce the likelihood that firms are trading off one aspect of a dimension for another. By insuring variability of performance is at least normal, we increase the likelihood that firms are maintaining their commitment to sustainability relative to the rest of the industry. Firms meeting both these requirements may be thought of as ‘stars’. They are likely viewing the particular dimension of sustainability as a strategic asset and can be looked to for best practices at the dimension level.

Table 3

Star Climber Tumbler Loser

Using Tables 4–6 provides a lot of information for companies looking for sustainability opportunities. First, they can identify stars on individual dimensions of sustainability. For companies looking to improve performance on a particular dimension, they can look to these companies with confidence that the tactics and strategies they employ do not involve trading off performance on one metric at the expense of another important aspect at the dimension level. They can examine climbers in a particular dimension to identify tactics that may be the future for improving in a dimension. They can also examine tumblers and losers to identify tactics to eliminate from their sustainability strategies at the dimension level.

Dimension level performance

Level of performance

Variability/trend of performance

Each of the KPIs within a dimension has a ‘high’ or a ‘normal’ level with at least one ‘high’. Each of the KPIs within a dimension is at the ‘high’ or ‘normal’ level. No KPIs within a dimension are at the ‘high’ level.

Each of the KPIs within a dimension has ‘low’ or ‘normal’ variability. There is no decreasing trend. Each of the KPIs within a dimension has at least one improving trend, and there is no decreasing trend. Each of the KPIs within a dimension has at least one decreasing trend, and there is no improving trend. Each of the KPIs within a dimension has no improving trend.

Each of the KPIs within a dimension has a ‘low’ or a ‘normal’ level with at least one ‘low’.

KPI, key performance indicator.



Benchmarking sustainability performance Table 4


Planet level categorization


E. Maltz, H. H. Bi and M. Bateman Table 5


People level categorization


Benchmarking sustainability performance Table 6

Profit level categorization

The last column of Table 6 provides an overall rating of sustainability strategies across dimensions. Companies were categorized based on the description in Table 7. Superstars have a consistently high performance in at least one dimension without Copyright © 2016 John Wiley & Sons, Ltd.

impairing their performance relative to the industry on other dimensions. For companies looking for a comprehensive sustainability strategy, this is a good place to start. A second place to look is at the climbers. It is worth noting that there is only one J. Public Affairs (2016) DOI: 10.1002/pa

E. Maltz, H. H. Bi and M. Bateman Table 7

Firm Level Performance Trend of performance

Superstar

A star in either people, profit, or planet and no tumblers or losers Star in the Making A climber in either people, profit, or planet and no tumblers or losers Tumbler No climbers or stars and at least one tumbler in people, profit, or planet Loser No climbers or stars and at least one loser in people, profit, or planet

star (firm 63) on the financial dimension. However, there are a number of climbers on the financial dimension that are rated as Stars in the Making. This means they are improving their financial performance at the same time they are excelling in at least one of the other dimensions (people and or planet). At a minimum in these companies, it seems efforts on environmental and social issues are not making financial performance suffer and actually may be helping financial performance improve. For example, Figure 4 shows the relative financial performance in terms of stock price of one superstar, public services energy group (PEG), and one loser PPL. As you can see from the graph since 2013, their performance mirrored each other until 2015. PEG is

New Jersey’s largest electric utility providing energy to more than 1.8 million people. They have made significant investments in solar resources starting in 2009. By 2012, they were ranked third in the nation in solar capacity (PEG Sustainability Report 2013). This is likely to improve its overall planet performance. An examination of Table 6 considering all three dimensions indicates PEG is a superstar. Note that in 2015, PEG started significantly outperforming PPL. With the recent precipitous drop in solar costs (http://www.bloomberg.com/ news/articles/2015-04-16/big-oil-is-about-to-losecontrol-of-the-auto-industry), it is likely that PEG is reaping the benefit of their investments through lower costs of production and additional opportunities to serve the market of customers who are also likely try to take advantage of the drop in solar costs. In other words, an assignable cause is investment in solar assets. In addition, it is worth noting that it took sustained investment of several years to see the improvement in financial performance. This gives a company additional insight into how long it takes to recoup investments. While one cannot completely attribute the improved financial performance to the investments in solar resources, additional research may be able to more strongly assess the correlation of these performance indicators.

Figure 4 Example of superstar longer term performance



Benchmarking sustainability performance

DISCUSSION Developing sustainable business models incorporating effects on people, profit, and planet is becoming an increasingly important strategic issue. Benchmarking with peer companies can assist a company in setting goals for improving performance. As such, developing a methodology for effectively benchmarking sustainable business practices is an important step in the evolution of sustainability management. The current study describes how statistical process-monitoring theory can help to manage the tradeoffs and long-term performance issues inherent in identifying peer companies to benchmark against given a particular set of KPI’s material to a company’s strategic sustainability goals. As such, it offers a number of implications for theory and practice as the field of sustainability management matures.

Managerial implications The study offers important lessons for both sustainability managers within companies and the rating agencies attempting to identify companies that are truly committed to sustainable strategies. Sustainable management practices are on the cusp of going mainstream. However, to take the next step, managers will have to understand how to effectively implement sustainable management practices that take into account the long-term value to people profits and plant. In this study, we have described and provided an example of a benchmarking methodology that takes into account trends and tradeoffs among the three dimensions of sustainability performance. By explicitly modeling these tradeoffs and trends, the benchmarking methodology proposed provides a sophisticated assessment of leaders and laggards within a single industry and a basis for identifying sustainability performance drivers. Moreover, managers in this area are constantly making decisions around the notions of materiality and what constitutes success. That is, what are the boundaries of our strategy? Where do we have competitive advantage? Where are the areas that we should focus that have long-term implications for the firm and broader system in which the firm operates? The proposed methodology asks them to consciously think more explicitly about tradeoffs and the correct time frame to assess success. One often sees some arbitrary date for achieving significant sustainability goals. For example, achieving 20% reduction in carbon emissions by 2020, or 50% waste reduction by 2050. By looking at the high Copyright © 2016 John Wiley & Sons, Ltd.

performers and seeing what they have actually been able to achieve, managers can develop a better sense of what is achievable and what is a stretch goal requiring significant innovation beyond the state of the art. For rating agencies, this methodology specifically overcomes some typical weaknesses in benchmarking sustainability and as such offers a path forward for improving approaches to sustainability benchmarking by managers, investors, and other groups assessing corporate sustainability efforts. For instance, while most ratings systems generally have some element of comparative ratings as the end step (e.g., Calvert; www.calvert.com/sri-integrated-analysis.html, FTSE4Good http://www.ftse.com/products/indices/FTSE4Good), our study employs a more rigorous statistical method for drawing significant bright lines of leaders and laggards. In this way, it brings a quantitative approach to the comparative rating, rather than a somewhat arbitrary determination of what constitutes a leader or a laggard in an industry. A second common theme among existing rating systems is that they rely on transparency as a basis for ratings. That is, they assess how much information an organization is disclosing and use disclosure as a primary criteria for rating performance (e.g., CRO100; http://thecro.com/content/methodologybehind-100-best-corporate-citizens-list). Our methodology, on the other hand, rates organizations based on relative actual performance. Thus, at minimum, our methodology could be used as a cross-check to rank companies.

Theoretical Contributions and Directions for Future Research In terms of theory, the study contributes to the ongoing debate on whether ‘sustainable’ strategies incorporating multiple stakeholders can actually be implemented without impairing returns to shareholders. Early studies in this area proposed strategic benefits of sustainable practices (e.g., Burke & Logsdon, 1996). Subsequently, others have tried to provide empirical evidence that sustainable practices actually can enhance shareholder value (e.g., López et al., 2007). Nevertheless, skepticism remains in both the management community and the academic community as to whether sustainable practices are consistent with long-term return to shareholders (Lazlo & Zhexembayeva, 2011). This may be, in part, because the actual measurement of success is limited to the financial dimension of sustainability, while other dimensions are inferred from other rating systems. Looking at actual KPIs J. Public Affairs (2016) DOI: 10.1002/pa

E. Maltz, H. H. Bi and M. Bateman across all dimensions using a rigorous statistical methodology could provide a more systematic assessment of whether financial and broader system sustainability can coexist. Scholars universally agree that to understand this relationship requires looking at long-term value to the firm as many of the proposed benefits to profitability are based on reputational effects, increased productivity of the workforce, and/or proactive responses to expected regulatory responses. However, event studies trying to empirically model this relationship invariably focus on a single dimension at a single point in a time. No study, to our knowledge, takes into account both the longitudinal element and the tradeoffs among multiple sources of value to the different dimensions of sustainability. Given the multidimensional nature of the sustainability construct, effective measurement requires explicitly considering tradeoffs over time. Our methodology has the potential to do just that at the industry level. The study also has the potential to increase the use of statistical process-monitoring theory beyond traditional operational settings. Recently, the theory has moved beyond manufacturing to monitoring of financial data (Frisen, 2008) and health care and public health surveillance (Woodall & Montgomery, 2014). Our study points to broader uses at a more strategic level. The limitations of the study suggest avenues for further exploration. First, we have presented one example in a single industry in a single country (utility industry in the USA). Given the novelty of the theory in this context, we felt the relatively narrow sampling frame was appropriate. It is possible that looking at a broader group of countries beyond the USA would provide different benchmarking outcomes. As such, scholars looking to validate or invalidate this methodology should look to expand to other industries and develop more global samples. In doing so, scholars can start to identify KPIs that can be utilized across industries and those that are industry specific. The potential for developing more robust benchmarking in the sustainability context provides a justification for studying these issues further.

BIOGRAPHICAL NOTES Elliot Maltz received his MBA from the University of California at Davis and his Ph.D in Marketing from the University of Texas at Austin. Prior to coming to the Atkinson School he taught for 6 years at the Marshall School of Business at the University of Southern California. His teaching interests Copyright © 2016 John Wiley & Sons, Ltd.

include marketing management, marketing strategy, new product planning, sustainability management, and supply chain planning. His research has been highlighted in Harvard Business Review, Journal of Marketing, Journal of Marketing Research, Journal of the Academy of Marketing Science, Journal of Business Research, Journal of Product Innovation Management, Journal of Public Affairs, Journal of Business Logistics, Long Range Planning and Sloan Management Review. He has consulted and conducted workshops for a variety of concerns including: Texas Instruments, Hewlett-Packard, Johnson and Johnson, 3M, The Samsung Corporation, The Monitor Group, The Society for Competitive Intelligence Professionals, The Sony Corporation, Weyerhaeuser, and The Center for Telecommunications Management. Henry Bi joined Willamette University in Fall 2009. Prior to Willamette, he has been an Assistant Professor of Supply Chain and Information Systems at the Pennsylvania State University’s Smeal College of Business for five years. He also has work experience with Xerox Corporation in Rochester, NY and with an import/export company in China. Henry has taught courses in the areas of information systems management and operations management at the Pennsylvania State University as well as at the University of Arizona when he was a doctoral student. His research focuses on information management and process management. Henry is a member of the Association for Information Systems (AIS) and the American Society for Quality (ASQ). Mark Bateman Founder and CEO at ENSOGO Analytics. Prior to founding ENSOGO he founded IW Financial’s Research Department. In that capacity he built research methods and research collection/storage/ manipulation technology and infrastructure and training protocols for the new research team. He provided coverage on over 4,000 companies on dozens of environmental, social, and governance issues.

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E. Maltz, H. H. Bi and M. Bateman

APPENDIX The center line and the three sigma control limits of the x -s charts and XmR charts are calculated as follows [(pp. 226–229) (Breyfogle, 2003); (pp. 366–367) (Kubiak & Benbow, 2009); (pp. 116, 236, 261–262, 268) (Montgomery, 2012)]: (1) Control limits of x-s charts for m samples of the same size n:

Average of m sample standard deviations: m (6) ∑ si i¼1 s¼ m The values of factors A3, B3, and B4 are standard and provided in [p. 720] Montgomery (2012). (2) Control limits of XmR charts for a sample of size n: where Type of control chart

Type of control chart Average chart (x chart) Standard deviation chart (s chart)

Control limits

Control limits UCLx ¼ x þ A3 s center line = x LCLx = x A3 s UCLs = B4 S center line = s LCLs = B3 s

Individual chart (X chart)

UCLx ¼ x þ 3 MR d2 center line = x LCLx ¼ x 3 MR d2

(7)

Moving range chart (MR chart)

UCLMR ¼ D4 MR center line = MR LCLMR ¼ D3 MR

(8)

(1) (2)

n

∑ xi

n

∑ xij where j¼1 Average of the ith sample of size n: x ¼ n ; plot points (3) m ∑ xi (4) Grand average of m samples: x ¼ i¼1m Standard deviation of the ith sample of size n: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi un ; plot points (5) u ∑ x x 2 u ij i tj¼1 si ¼ n1


Sample average: x ¼ i¼1n

(9)

Moving range: MRi = |xi xi 1| for i = 2,3,4,…,n

(10)

n

∑ MRi

Average moving range: MR ¼ i¼2n1

(11)

The values of factors d2 = 1.128, D3 = 0, and D4 = 3.267 when n = 2 [p. 720] (Montgomery, 2012).
