The challenges of transitions towards a more sustainable business Introducing THRIVE framework

1. The challenges of transitions
towards a more sustainable business
Introducing THRIVE framework
Morris D Fedeli1,
*
1
University of Southern Queensland
*morris@fedeli.nu

2. 2
Abstract
Several obstacles face enterprises1 and their leaders during their transformation
journey towards sustainability. Shifting from a siloed and comfortable ‘Business as
Usual’ approach to creatively leveraging on new opportunities requires radical
innovation, worldwide integration, institutionalization of processes and the right
business strategy.
This study conceptually introduces The Holistic Regenerative Innovative Value
Enterprise (THRIVE) framework and develops THRIVE platform and Sustainability
Performance Scorecard (SPS) tool with visualizations in the form of pioneering
ciambella charts. Operationalized at the meso-level, aided by machine learning and
predictive analytics tools, THRIVE SPS tool transparently ranks the corporate
sustainability performance of enterprises alongside their strategy or business model.
Rooted in systems thinking, this study develops nascent design theory-knowledge
through a design science research (DSR) approach, building on emerging science-
based literature and illustrated by exemplary case studies. A holistic analysis of the
projected phenomenon informs the kernel theory, building on established science-
based first principles, together with reputable global data sources and purposely
selected impact case studies.
THRIVE SPS dashboard provides for the manipulation of engine weights and controls,
allowing assessment of performance and forecasted effects of changes in regulations
such as taxes or subsidies on carbon credits to be visualized. Contributing to a
healthier planet, THRIVE framework and SPS tool aids enterprises transitioning from
disclosure to exposure, thereby providing the necessary impetus for business leaders
to compete with one another and individual consumers to reward enterprises that
choose to do good to do well for the prosperity of all humankind.
Keywords
business strategy, business transformation, context-based sustainability, corporate
sustainability performance, sustainability performance scorecard.
1
Authors use a range of terms interchangeably when referring to the entity under discussion in their
studies. These include company, institution, organization, corporation, business, firm and enterprise.
Wherever possible, this study standardizes on the word 'enterprise(s)' to mean the entity itself.

3. 3
INTRODUCTION
Regarded as interdependent sides of the same coin (Braun et al. 2019), innovative
business strategies or business models (Casadesus-Masanell & Ricart 2010)
represent our best source for tackling ever increasing systemic planetary crisis
(Robèrt et al. 2013; Rockstrom 2009; Steffen et al. 2018). Yet following a
comprehensive review, no study nor public tools were found connecting business
models with strong sustainability (Neumayer 2010; Pelenc 2015) performance by
means of context-based measurements (Haffar & Searcy 2018) and science-based
targets (Adams et al. 2015; Bocken et al. 2014; Foss & Saebi 2016; Melkonyan et al.
2017).
Studies reveal that business leaders and innovators report not knowing where to
start in making the transition to sustainability (Breuer & Lüdeke-Freund 2017; Kurucz
et al. 2017; Schaltegger et al. 2016a). By applying a precautionary principled stance
(Costanza & Patten 1995), backcasting (Holmberg & Robèrt 2000; Voros 2003;
Willard et al. 2013) emerges as a particularly relevant approach in aiding transition.
With this technique, society envisions the future sought, and works backward to the
present whilst setting milestones along the way. By defining the framework and tools
to evaluate corporate sustainability performance of enterprises, guidance is provided
during their transition to becoming sustainable and prompts enterprises to embrace
a strive to thrive philosophy, requiring change at a scale, speed and scope unseen
before (Baue & Thurm 2018; SBTi 2018).
From an ecological economist risk minimization perspective (Broman et al. 2017) this
study conceptualizes The Holistic Regenerative Innovative Value Enterprise (THRIVE)
framework; providing the basis for formulating tools which openly rank corporate
sustainability performance of enterprises alongside their business strategy. A
business strategy is defined as the enactment of a set of business models (Wirtz et
al. 2016) across the three pillars of sustainability, being the economic, social and
environmental (Elkington 2004). Worthwhile noting is that an enterprise operates a
combination or hybridization of business models (BMs)(Laasch 2017). Many
attempts to date to report on the corporate sustainability performance of
enterprises have amounted to little more than disclosures (Lydenberg et al. 2010;
WBCSD 2017a), with several reports plagued by greenwashing (Najam et al. 2000).
THRIVE framework and SPS tool delivers the impetus for enterprises to move from
disclosure to exposure, ensuring sustainability risks are assessed as business risks
(WBCSD 2017a).

4. 4
Geissdoerfer et al. (2018) highlights how many business modes (BMs) fail and
Clauss's (2016) study indicates that a validated business model innovation (BMI)
measuring scale is not yet available. Operating at the meso scale (Thurm 2016),
THRIVE framework establishes a uniform, integrated and standardized platform
(http://www.strive2thrive.earth); which together with THRIVE sustainability
performance scorecard (SPS) tool, ranks the sustainability performance of
enterprises alongside their business model (BM). Thus THRIVE SPS illuminates the
pathway for enterprises to move from statutory to voluntary disclosure in their quest
towards sustainability through business model innovation (BMI) (Amit & Zott 2012).
The meso scale is situated at the interface between the micro and macro
environment where the greatest opportunity for positive change can occur (Baue &
Thurm 2018). This makes THRIVE framework the most useful way forward in
addressing Elkington (2018) revised triple bottom line approach. Furthermore, this
compares favourably with the Sustainability Assessment of Farming and the
Environment (SAFE) (Van Cauwenbergh et al. 2007) hierarchical framework,
composed of principles, criteria, indicators and reference values organized in a
structured way and used for assessing the sustainability of agro-ecosystems.
The utility of the proposed THRIVE framework and Sustainability Performance
Scorecard (SPS) tool encourages disruption (Bansal 2019) through radical innovation,
compatible with natural science; driving maximum creativity within natural socio-
environmental constraints (Gill & Hevner 2013; Pries-Heje & Baskerville 2014;
Upward & Jones 2016). Ecological footprint analysis (Wackernagel et al. 2005) shows
that humans are in ‘overshoot’ having used 1.7 Earths equivalent in providing the
resources needed and absorbing waste (Michael Borucke et al. 2012). Thus the role
of sustainability as a long-term corporate strategy and as a common practice
(Ioannou & Serafeim 2019) must become mainstream.
This paper firstly discusses the need to develop the THRIVE framework and SPS tool
to assist enterprises address the sustainability challenge. It then turns to the adopted
approach, emanating from the school of design science. Next it specifically outlines
each aspect of the contribution made, building on established literature and impact
studies. The conclusion encourages enterprises to use the THRIVE SPS tool to make
positive change for the benefit of all humankind.

5. 5
PURPOSE
As a transformative (Mertens 2007) conceptual study, this paper incorporates a
comprehensive thematic analysis of the prevailing literature (theory) (Braun & Clarke
2006; Lapadat 2010; Maguire & Delahunt 2017) informed by several case studies
(evidence) (Gomm et al. 2000; Johansson 2007; Perry 1998; Yin 2004). Secondary
numerator performance data are derived from publicly published integrated
corporate sustainability reports (e.g., Global Reporting Initiative Sustainability
Reports, Carbon Disclosure Project datasets) and corporate websites by
multinational enterprises worldwide (Szekely & Vom Brocke 2017). Denominator
threshold and allocation data is sourced from global footprint initiatives (e.g., Global
Footprint Network - GFN) and notable international agencies such as United Nations
(UN), World Health Organization (WHO), and World Resources Institute (WRI).
The unit of analysis is the business model (BM) (Teece 2017), although scholars do
not uniformly agree on its definition (Zott et al. 2011). Indicators are calculated from
material topics selected from the Global Reporting Initiative (GRI) standards (GRI
Material Topics) mapped to the United Nations Sustainable Development Goals (UN
SDGs) (Linking the SDGs and GRI). Although studies show that GRI-based
sustainability reports do not contain sufficient information needed for judging
corporate sustainability performance or how quickly they are approaching
sustainability (Barter 2015; Isaksson et al. 2009; McElroy 2017), the use of these
material topics alongside the formulae and methods developed in this study allows
for answering these questions.
Enterprises recognize that they can only manage what is measured (Bansal & Song
2017), and thus are often accused of not measuring what matters. A Haffar and
Searcy (2018) review of 463 environmental indicators found none to be context-
based with a prevalence for self-referential indicators without context in many of the
most commonly used reporting frameworks, such as the GRI and CDP. This study
develops THRIVE framework and SPS tool which allows enterprises to publicly,
transparently and accurately self-assess (without self-reference) their business
model (BM) and corporate sustainability performance (impact) whilst adopting a
context-based and science-based approach (SBTi Criteria and Recommendations
2018). This study thus supports enterprises by recognizing that innovations at the
business model level (BMI) (Amit & Zott 2012; Foss & Saebi 2016; Kiron et al. 2013)
are a precursor to business models for sustainability (Schaltegger et al. 2012;

6. 6
Schaltegger et al. 2016b) i.e., sustainable business models (SBM) (Lüdeke-Freund et
al. 2018a).
Whilst some argue that harmonizing measures globally may go against enterprises
promoting their own narratives or value creation stories (2013), THRIVE SPS tool
provides a sophisticated and standardized, long sought after means of scientifically
comparing corporate sustainability performances. The extended version of this study
incorporates a discussion on the theory of change as it relates to business models
(Andriopoulus & Lewis 2009; Upward 2013) to ensure long-term systemic sustainable
success by considering business enablers and moderators, such as leadership,
culture, globalization, collaboration, risk, and regulation.
APPROACH
The study adopts a Systematic Design Science Research (DSR) approach (Aken 2004;
Gregor & Hevner 2013; Hevner 2007; Winter & Aier 2016), integrating a pluralistic
interdisciplinary method (Redclift 1998; Shrivastava & Guimarães-Costa 2016; von
Bertalanffy 1968), acknowledging the role of societal and natural factors in
accounting for sustainability issues (Persson et al. 2018), and the wicked nature of
the projected phenomenon under investigation (Kurucz et al. 2017; Lüdeke-Freund
& Dembek 2017). Initially, a kernel theory based on first principles is advanced
(Gregor & Hevner 2013; Gregor & Jones 2007), together with illustrative case studies
and other public data, to explain and provide justification for the underlying
approach (March & Vogus 2010; Winter & Aier 2016). Adopting a high-level systems
thinking perspective (Ackoff 1971; Gharajedaghi 2006; Williams et al. 2017), the
study investigates corporate business models (BM) at the meso level, illuminating
how novel business model design affects performance (Martin 2009; Zott & Amit
2007) and in support of the understanding that business strategy needs design and
is about invention and thus requires innovation, as unguided novelty does not
necessarily create value (Liedtka 2018).
The flexibility of the holistic DSR approach aids in developing solutions to wicked
problems (Foss & Saebi 2018) and best serves the corporate responsibility field
(Visser 2012). Credibility is achieved by progressive focusing (Stake 1995) through
prolonged engagement (Lincoln & Guba 1985), evaluation of multiple instances
(Krefting 1991) and convergence of multiple secondary data sources (triangulation)
(Knafl & Breitmayer 1989; Krefting 1991; Morse 1991), explicit documentation of
choices made for included truths during discovery and evaluation (Guba 1981),

7. 7
maintenance of chain of evidence (Lincoln & Guba 1985) and narratives/reflexive
analysis of records (Ruby 1980) thus improving trustworthiness. Furthermore, this
experienced researcher, acting as the instrument, is careful to avoid confirmation
bias by ensuring objectivity (Good & Brophy 1985), whilst dictating the framework
(Agar 1986), showing expertise in the subject matter (Miles et al. 2014),
demonstrating good investigative skills (Miles et al. 2014), strong interest in
conceptual/theoretical knowledge (Miles et al. 2014), structural coherence (Guba
1981) and a multi-disciplinary approach (Miles et al. 2014). If possible, and time
permitting the final stages involve the peer review by subject domain experts from
around the world (Delphi Study) (Lincoln & Guba 1985).
Transferability is improved through thick descriptions of cases (Geertz 1973), using
selective maximum variation of nominated samples (Patton 2002), and longitudinal
triangulation of data sources (Krefting 1991; Morse 1991) with explicit general
research dimensions as indicated in Table 1, to aid in understanding the researcher’s
worldview and approach (Krefting 1991); and includes an index of case studies
reviewed (Krefting 1991).
As a transdisciplinary study, theoretical triangulation of perspectives is explicit (Knafl
& Breitmayer 1989; Krefting 1991; Morse 1991). Together with audits, this ensures
comparable conclusions, including a detailed step by step description of the thematic
analysis (TA) (Braun & Clarke 2006; Fereday & Muir-Cochrane 2006) and case study
(CS) (Baxter & Jack 2008; Perry 1998; Yin 2012) methods employed, thus ensuring
dependability and confirmability (Kielhofner 1982). By using multiple illustrative
DesignResearchDimensions
Worldview
Ontology Epistemology Paradigm
Atheist / Realist Rationalist Critical Realist
Approach
Design/Method Lens/Framework/Type
Systematic Design Science /
Thematic Analysis
(Literature/Case Studies)
Transformative / Exploratory /
Observational
General
Logic Outcome Ethics
Reductive
Cross-Sectional
Basic Applied
Humanist
Table 1. Design Research Dimensions used by the Author in this study.

8. 8
example cases (Eisenhardt & Graebner 2007), latent interpreted themes (Ruby 1980)
showing coherence (credibility) (Maguire & Delahunt 2017) and consistency
(dependability) between the claims and the data (Javadi & Zarea 2016) further aid
reflexivity (Hyett et al. 2014) through sense-making and quantification of the
thematic analysis (TA) during the creation of the set of patterns (Lapadat 2010).
Thus, rigor and trustworthiness are achieved by way of addressing the above issues
of credibility, transferability, dependability, and confirmability (Krefting 1991). Case
study construct validity is achieved through the evaluation of multiple data sources
(Baxter & Jack 2008; Creswell et al. 2011; Luck et al. 2005) coupled with the
maintenance of the chain of evidence (Tranfield et al. 2003); and reliability by
development of the thematic analysis (TA) database thereby further enhancing rigor
(Healy & Perry 2000; Huberman & Miles 2002). Both theory and method
triangulation is sought (Tobi & Kampen 2018). Multiple secondary data sources are
included in a codebook for consistency and transparency (Rowley 2002), thus
providing triangulation and verification (Hyett et al. 2014), and thereby improving
the trustworthiness and credibility of the study. This study contributes to theory,
methodology as well as empirically (Gregor & Hevner 2013).
CONTRIBUTION
This study takes a strong sustainability (Bjørn & Røpke 2018; Landrum 2017;
Neumayer 2010; Pelenc 2015), deep ecology (Naess 1973), multi-capital (Howitt &
Thurm 2018; McElroy & Thomas 2015), values-based (Breuer & Lüdeke-Freund 2017)
and science-based stance (SBTi 2018) with material topics evaluated within context
(Faber & Hadders 2015; McElroy 2013, 2017; McElroy et al. 2008; UN Environmental
Program 2015) i.e., based on inner and outer limits and allocations (Raworth 2012;
Reporting 3.0 2018). The Holistic Regenerative Innovative Value Enterprise (THRIVE)
framework and associated Sustainability Performance Scorecard (SPS) tool (Table 2)
resulting from this study, categorically identifies successful sustainable business
models (Bocken et al. 2014; Fellmann et al. 2018; Lüdeke-Freund et al. 2018b;
Remane et al. 2017) thereby encouraging enterprises to adopt sustainable practices
(Evans et al. 2017; Pansera & Randles 2013). A comprehensive literature review
reveals this is the first study to envision showcasing a quantitative sustainability
performance score alongside categorical business model pattern identifications.
THRIVE framework provides a methodological contribution to a strongly sustainable
future (Heikkurinen 2019; Stal 2018) as well as forming the basis of practical

9. 9
practitioner’s tools such as the THRIVE SPS. The THRIVE SPS tool assesses the
corporate sustainability performance of enterprises at the business model level
(Evans et al. 2017) and identifies which category of business models or strategies are
more successful than others. THRIVE tools are licensed under creative commons,
published in journals and made available to academia, business leaders and the
public (Creative Commons Website).
The THRIVE SPS echoes initiatives by the newly formed World Benchmarking Alliance
(WBA) (Oppenheim et al. 2017) and their most recent development of the Seafood
Stewardship Index (WBA 2018). Their sustainability ranked ‘league’ tables aim to
closely align performance with the SDGs. There are other initiatives or case studies
that report on corporate sustainability performance (Morioka et al. 2016), although
not categorizing based on BM. These include Climate Counts (Climate Counts 2013),
The Wikirate Project (Wikirate 2018), S&P Global Trucost (Aldhous 2019); the
individual ExxonMobil Integrated Report (Eccles & Krzus 2018); and GIIN: Impact
Investing in SEA (Global Impact Investing Network 2018) which focuses on a whole
country or sector. A plethora of individual self-assessment tools exist, however most
notable are the strongly sustainable F2B2 (Kendall & Willard 2015) and the VMDBee
tools (Man 2019). In a number of other efforts that rank the sustainability
performance of enterprises, there is a lack of methodological transparency or in
THRIVE Rank Enterprise
Name
Industry
Classification
BM
pattern
SP
scorecard
Sustainability
Performance
Scorecard
1 Enterprise A Pharmaceuticals,
Biotechnology and Life
Sciences
P6.1 0.503
2 Enterprise B Technology Hardware
and Equipment
P11.1 0.625
3 Enterprise C Consumer Durables,
Household and Personal
Products
P8.4 0.768
4 Enterprise D Food and Beverage
Processing
P7.4 0.891
5 Enterprise E Electric Utilities and
Independent Power
Producer
P4.3 1.106
6 Enterprise F Banks, Diverse Financials,
Insurance
P7.1 1.282
** For illustration purposes only ** SPS: 0 <= score <= 1 means strongly sustainable enterprise, score > 1 means
NOT a strongly sustainable enterprise. Context-based Sustainability Performance Scorecard (SPS) values are
calculated based on figures from public sources covering a range of material topics. Business model (BM) patterns
from Lüdeke-Freund et al. (2018). Industry classification is derived from GRI Business Activity Groups (BAG). Italicized
values are masked and anonymized.
Table 2. Sustainability Performance Scorecard (SPS). Sample of an instantiation.

10. 10
some cases the source data set may be proprietary (thus subject to bias), as in the
case of RobecoSAM (RobecoSAM List of Companies), the ‘big 4’ accounting and
consulting firms, and Corporate Knights (Corporate Knights 2018 Global 100 Issue).
Some, like TruePrice (TruePrice Website) evaluate the specific ‘trucost’ of raw
materials in set regions only, such as coffee, palm oil, milk, and bananas. And finally,
in some cases, sustainability reports simply reflect the level of disclosure (e.g., DJSI)
(López et al. 2007) without adequately addressing each material topic (UN
Environmental Program 2015; WBCSD 2017a) nor attempting to systematically
measure the actual sustainability performance of the enterprises concerned
(Lydenberg et al. 2010).
THRIVE SPS assists enterprises and consumers at large with a better understanding
of their impact based on its actual footprint (Rees 2000) across all three pillars of
sustainability: the economic, social and environmental (Elkington 1997). This is
achieved by summation of each quotient based on the enterprise’s impact on each
material topic, proportional to its allocation (Daly 1992). Each quotient is calculated
as the actual impact (numerator) divided by the allotted impact (denominator)
multiplied by its weight (Figure 1). It is argued by many as to what these weights
(Bjørn & Røpke 2018) and normalizations (Huang et al. 2015) ought to be across
sectors, industries, and pillars; with some arguing these should be simply omitted
and others that they ought to be industry or sector-specific (Eccles 2012; Sironen et
al. 2014). THRIVE SPS model supports any and all such variations in its calculation
SP score =
1
𝑛
× ෍ 𝑤𝑖 × |
xi
ei
|
n
i = 1
where 0 ≤
xi
ei
≤ 1 for strong sustainability, and
𝑤𝑖 is the weight applied to each specific material topic i
(i.e., usually 1; e.g., set to over 1 to model legislative
changes affecting topic)
xi actual impact by the Enterprise on material topic i
(i.e., measured based on true performance)
ei allocated footprint for material topic i
(e.g., based on Global Footprint Network datasets)
i material topic under evaluation
(e.g., Diversity and Equal Opportunity)
n total number of material topics
(e.g., 36 for GRI Standard or 17 for SDGs)
Figure 1. Formula for the calculation of the sustainability performance score as detailed in this study.

11. 11
engine, enabling the use of material topic weights as triggers for transition. For
example, weights may be employed to model the introduction of a government tax
on certain goods or services, or be used as a catalyst to encourage enterprises to
switch to more renewable resources by attributing higher weights (Ioannou &
Serafeim 2019) to certain material topics. Whilst THRIVE SPS engine supports
multiple weights and thus algorithms, the key to its usefulness is consistency of
method across material topics in order to effect fair comparisons (IIRC 2013).
Numerator values are based on actual performance relative to the inner (threshold)
and outer (ceiling) measurement, as espoused by GTAC (Reporting 3.0 2018) and
similar groups, being aware of congruency in units of measure and the related issue
of normalization across all material topics. In some cases where global data is
missing, allocations may be based on gross domestic product (GDP), even though this
is not the best method (Faria & Labutong 2015), and subject to refinement based on
a more meaningful measure. Denominator data is sourced from the open data and
method platform provider Global Footprint Network’s (GFN) (Global Footprint
Network Homepage 2019) ‘measure what you treasure’ service, or from other
notable and reputable sources: United Nations (UN), World Health Organization
(WHO), Organisation for Economic Co-operation and Development (OECD), Carbon
Disclosure Project (CDP), World Resources Institute (WRI), Eurostat and others.
The support for granularity within THRIVE framework means that the SPS dashboard
may be used to see performance on an international, national or sector by sector
basis or even year-on-year within an enterprise or cumulative within a sector.
Together with the custom produced ‘Ciambella Chart’ (Figure 2), it provides a quick
and easy direct visualization of an enterprise’s sustainable impact across GRI topics
or SDGs goals (Linking the SDGs and GRI). Thus, unlike the sustainable value add (SVA)
method (Figge & Hahn 2005) based exclusively on GDP which fails to measure the
actual sustainability performance of an organization within context (Huang et al.
2015) - much less from a strong sustainability perspective - the THRIVE SPS method
is similar to the approach advocated by the multi-capital scorecard (MCS) (McElroy
& Thomas 2015) and sustainability quotient for social footprints (McElroy et al. 2008)
where a clear absolute binary identification of ‘is sustainable’ (green) or ‘not
sustainable’ (red) is achieved.
Whilst this study advocates the use of the GRI material topics (GRI Material Topics),
Bailey and Eccles (2018) argue for using the sustainability factors as identified by the
Sustainability Accounting Standards Board (SASB) mapped to the objectives of the

12. 12
United Nations’ Sustainable Development Goals (UN SDGs). Moreover, whilst there
are no set metrics specified, the suggested framework agnostic Global Impact
Investing Network (GIIN) IRIS+ (IRIS. 2019) metrics provide data clarity and
comparability, across the 169 targets, and 230 measurable indicators of the SDGs
(2019, 2019). Whilst the specifics of the IRIS+ metrics are outside the scope of this
paper, business leaders may use these internally consistent IRIS+ metrics to manage
what is measured (Prusak 2019), and moreover ensure they are measuring what
matters, within context, using science-based targets (Assessing Corporate Emissions
Performance through the Lens of Climate Science 2013; SBTi Criteria and
Recommendations 2018; UN Environmental Program 2015) and uniform units of
measure (IIRC 2013) thus ensuring fair comparisons.
CONCLUSION
The strong theoretical basis employed by the framework together with public
domain science-based datasets ensures a broadly useful THRIVE platform and SPS
tool that can be used by several actors: consumers, business analysts, researchers,
government and business leaders. For example, consumers may use the system to
decide which enterprise they should transact with; business analysts to guide
enterprises in engaging in certain activities or strategies; researchers to analyze
trends and effectiveness of business models for sustainability; governments to
forecast the effects of legislative actions; and by business leaders pursuing a
competitive advantage.
Figure 2. Sample Ciambella Chart developed by the Author.

13. 13
Designed to provoke transformative change, THRIVE SPS ranking tool provides a
transparent public platform to encourage enterprises to embrace the circular
economy (Geissdoerfer et al. 2017; WBCSD 2017b; Webster 2015) through
contributing their fair share. It achieves this by arming business leaders with the
knowledge to actively compete and excel among their peers through alignment with
the United Nations Sustainable Development Goals (UN Global Compact 2018) in
order to do good to do well. It also empowers individuals with the ability to actively
stimulate competition among enterprises for greater global shared value creation
and collaborative peaceful partnerships for people, planet, profit with purpose and
prosperity (Kolk et al. 2017).
Future
As tri-impact integrated reporting (<IR>) becomes the norm featuring
comprehensive and complex levels of analysis and automation (Lai et al. 2016;
Lydenberg et al. 2010), newer technologies like Artificial Intelligence (AI) , Big Data
and Analytics (BDA) (Eccles & Krzus 2018) brings us a step closer to the next leap
towards dynamic Integrated Report Generator Tools (IRTG). These technologies are
ultimately only as good as the underlying frameworks that dictate how data is to be
compiled, reports constructed and as accurate and reliable as the source datasets
are. This study serves to bridge this gap by contributing to the framework and
toolsets necessary to inform these comparisons. Thus, business transformation for
sustainability benefits from the more sophisticated THRIVE SPS engine, featuring
granular and accurate denominator data and integrated reporting at the global
macro level and interactive real-time querying of an enterprise’s sustainability
performance, truly closing the loop on sustainability and the circular economy.

14. 14
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