The Impact Challenge

Reframing Sustainability for Businesses

Alessia Falsarone

• Publisher: CRC Press
• Year: 2022
• Print ISBN: 9781032079509
• eBook ISBN: 9781000562859
• Edition: 1
• Topic: Economics
• Subtopic: Finance
• Index: Economics

This book explores the role of businesses in delivering positive societal and financial outcomes as they seek to bridge the gap between short-term organizational behaviors and long-range sustainability commitments. By addressing the inevitable data challenges associated with the strategic integration of a sustainability mindset, it enables faster adoption of social, environmental and governance metrics that generate lasting enterprise value. Inspired by the experience of practitioners that have successfully influenced the learning behaviors of complex organizations, this book helps readers drive systemic innovations as they leverage sustainability initiatives in a programmatic and intentional manner.

Features:

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• Defines a toolkit to generate sustainable business value by focusing on the organizational design underpinning sustainability-oriented initiatives.

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• Provides a multidisciplinary lens on shaping the impact dialogue through applied frameworks.

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• Discusses the need to analytically identify an organizational learning curve before developing impact targets and framing sustainability commitments around them.

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• Combines theory and practice in a practical style by presenting a variety of real-life applications at a global level.

• The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
  
  

1 The Impact Enterprise

DOI: 10.1201/9781003212225-2

Over the past twenty years, sustainability efforts in the private sector have lacked the organizational structures they need to create long-lasting results. This has made it difficult to replicate or scale them outside of an individual market, product or team. A new field of study – that of data science for impact – promises to address this challenge and reshape the way organizations learn and act on impact-oriented business targets that combine the pursuit of positive societal outcomes with traditional financial metrics over a sustained period of time. What is data science for impact? I define it as a multidisciplinary set of research methods and analytical processes that can help an enterprise to achieve its impact objectives. Crucially, it must also help the enterprise define its own organizational learning path to sustain those objectives.

The challenges tackled by enterprises that commit to impact targets are multifaceted – from climate change and preservation of natural ecosystems to the need for inclusivity, equanimity and diversity. This means that in developing roadmaps and solutions, organizations that seek to deliver upon those targets must build a spectrum of collective efforts that foster authentic dialogue and constructive exchange of ideas between today’s scientists and industry practitioners and the future generation of scientists and industry practitioners they influence. For the purpose of this publication, I will use the terms outcomes, impact and purpose interchangeably.

The Evolution of Data Science for Impact

Historically, the definition of impact has been linked to the development of evaluation and assessment tools that are applied in environmental and ecological studies or in the area of socio-economic development. In the latter case, this was mostly led by international organizations and not-for-profit institutions as a way to value change and support the case for development funding – such tools were used to measure improvement in a variety of indicators of human wellbeing. The investigation of impact assessment tools dates back to the 1950s as an attempt by international NGOs and development agencies to forecast the likely environmental, social and economic consequences of carrying out a specific program. Environmental and social cost-benefit analyses (CBAs) were integrated into their decision-making. Generally, the effectiveness of CBAs was evaluated when a project or initiative was completed. This backward-looking view helped address a core resource allocation problem for their program portfolios (as a linear programming exercise) and identify whether the program was allocated enough people and funding to succeed.

Longitudinal approaches were introduced shortly thereafter to complement the simple resource allocation exercise of CBAs. In the evolution of the impact assessment toolkit, longitudinal studies added a component of forward-looking assessment. They helped track progress in target areas of impact at regular intervals, not just at the completion of a project. This moved efforts a step closer to leveraging direct evidence from pilot projects to maximize the target impact while keeping resource allocation constraints in mind. Identifying the set of descriptive variables that define the impact objective constitutes the core element of socio-economic studies that employ a longitudinal approach, while also making them comparable among a portfolio of potential impact projects an organization can choose from.

Let us take the example of a study that aims to establish how the built environment affects the wellbeing of a building’s occupants. A longitudinal framework will involve collecting an extensive set of observations, both qualitative and quantitative in nature. This would most likely start by recording field data across a spectrum of tenants in either a commercial or residential setting, including their preferences. The study would then map out basic evidence of behaviors associated with choices the tenants make around aspects of the environment they value – for example, comfort and temperature. A range of environmental quality considerations regarding building construction beyond traditional metrics of energy usage can be drawn from field data. These include behavioral measurements exemplifying thermal comfort of tenants, such as individual preferences for room temperature, which can be gauged from how often heating and cooling systems are used.

One of the limitations of longitudinal studies is their smaller scale. As pilot studies vary widely, it is difficult to draw generalized criteria of targeted impact – that is, to answer the question of when impact is “good enough.” Development practitioners think of this limitation as common when carrying out resource-constrained studies. Inherently, the impact outcomes reported will be only as good as the availability of resources, such as funding, the amount of time and energy devoted to each individual pilot program and the number of beneficiaries assessed. In other words, assessing impact outcomes through such studies is constrained by the limited resources put into them. The findings may also vary depending on the timescale and how long the study runs for. In the built environment example, a tenant preference for thermal comfort is likely to vary with the different seasons or the time of day. Nevertheless, these analyses can be easily replicated, which makes them appealing to academics and industry practitioners looking to tailor the scope of larger scale projects, as illustrated in Figure 1.1. In addition, the findings of longitudinal studies can be approached with a forward-looking orientation. By relying on a set of studies to draw adaptive behaviors from tenants and minimize the environmental impacts they may trigger, they can be used to inform policy decisions in similar settings.

FIGURE 1.1 Five-step comparison of impact evaluation tools. Author's intuition. Reference: Barreto (2010).

The late 1990s saw the introduction of the Logical Framework Analysis (LFA), also known as LogFrame Matrix, as a tool to incorporate the context for which a specific program is evaluated. That included reviewing activities, inputs and objectives at each stage of its rollout vis-à-vis its operating environment, which enabled knowledge transfer within an organization. The LFA added the exploration of cost-and-effect relationships to an organization’s impact assessment toolkit, which means that potential deviations between expected and realized outcomes can be identified in a programmatic fashion.

The adoption of the LFA has opened the door for dynamic reassessment of pilot programs and made it possible to calibrate them against sudden changes in the operating environment. Its systematic analysis provides an actionable planning roadmap and builds on a common set of metrics to gauge project progress and achievements. However, the LFA’s strong focus on results and the need to meet budgetary constraints may limit an organization’s ability to define pivots and explore opportunities that had not emerged in the planning phase.

Stakeholder identification is an example of this – interest groups that may have not been considered at inception may not be discovered. It may also hinder innovation itself as a tool to create swift turnaround under volatile operating conditions.

The Stakeholder Lens on Methods and Tools

LogFrame analyses brought a vital development to the evaluation of the operating context – the introduction of participatory methods. Such methods ensure that impact beneficiaries (the target population assessed against standalone projects and/or broad organizational commitments) are elevated from being “subjects” to the evaluation and “objects” of the assessment to become “active participants” and providers of invaluable feedback.

Participatory learning and action methods (PLA) allow diverse opinions and perspectives to be introduced over the lifetime of the assessment, ultimately weighing in on the outcome.

For example, in the field of learning and education, these analyses have been deployed to incorporate the opinions of all relevant interest groups in the planning stage of a new program. This contribution helps to better address the operating environment and its day-to-day reality. In areas of community engagement, such as poverty or access to education, adopting a PLA mindset helps address issues by making it a priority to learn from the active participants. Interventions can be directly shaped to the living conditions by leveraging local community knowledge – regardless of age, ethnicity or literacy.

I refer to it as PLA mindset simply because maps and other visual representations of community life enable outsiders to see the relevance of shared resources and facilities through the eyes of community members. Although part of a qualitative research toolkit based on group analysis and learning, PLA is designed to provide insights and vital feedback to validate data with all stakeholders before an action plan is made. The transect walk presented in the Technical Note provides a PLA for land use evaluation. The mapping exercise is usually conducted by walking an area with members of the local community and creating a live representation of the direct observations. Transect walks have become quite useful in collecting input from native populations to address deforestation issues and community access to transport. (See Technical Note for the set-up of a transect walk.)

It is no surprise that stakeholder capitalism is increasingly regarded as the natural extension of over fifty years of impact theory and responsible practices conducted outside of the private sector. I refer to stakeholder capitalism as the outright and programmatic consideration of the interests of customers, employees, suppliers and communities – in addition to those of shareholders and other financial partners – in business decision-making. When we extend impact assessment tools to a wider range of use cases by private sector participants, we are confronted with open questions about accountability: Who is ultimately responsible for delivering impact at the organizational level across all projects and initiatives? How can we go about planning for impact as a portfolio of business decisions? How do we measure, forecast, manage and adapt longitudinal studies in a forward-looking way to inform future decision-making while also benefiting from participatory environments and cross-functional cooperation? In other words, how do complex organizations manage for impact? How do they measure the effectiveness of their efforts?

Intuition of what success may look like when an accountability mechanism is put in place is usually directly related to how transparent an organization is in the ordinary course of doing business. More often than not, impact-oriented organizations make no secret of their ambitions. Transparency becomes an internal compass to help them move across functions and engage their best players. Historically, publicly listed companies have released quarterly financial statements and annual reports. This means that accountability demands on leadership and organizational structures are directly connected to meeting legal reporting obligations as opposed to defining a stakeholder approach to public disclosures.

The need to meet regulatory requirements has defined which interests are prioritized (shareholders first) instead of ensuring that an organization reports progress with a long-term lens in mind. In other words, current reporting and disclosures in the private sector incentivize boilerplate financial metrics. This comes at the expense of the organizational learning and reframing that traditional Logical Framework Analyses may suggest, and has created accountability structures that over-emphasize short-term, quantitative, financially oriented goals and hierarchical management models. Goal attainment becomes a proxy for organizational effectiveness. The reporting of impact goals and aspirations will need to shift from interme...

Table of contents

• Cover
• Half Title Page
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• Acknowledgments
• About the Author
• Introduction
• Chapter 1 The Impact Enterprise
• Chapter 2 Organizational Learning for Impact
• Chapter 3 Are Novel Datasets the Solution?
• Chapter 4 Taking Impact a Step Forward
• Chapter 5 The Data-Enabled Sustainability Mindset
• Chapter 6 Culture and Climate Change
• Chapter 7 Building Inclusive Networks
• Chapter 8 The Informal Organization
• Chapter 9 The Impact of Data on Human Rights
• Chapter 10 The Next Decade: Innovation to Impact
• Conclusion
• Index