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Handbook of Knowledge Management for Sustainable Water Systems (KM4SWS)

Introduction and a Theoretical Framework for KM4SWS

Meir Russ-Final Draft-May 30, 2017

According to the World Health Organization (WHO), in 2009, about one fifth of the

world's population lived in countries that did not have enough water for their use. By 2025, 1.8

billion people will experience absolute water scarcity, and by 2030, almost half the world will

live under conditions of high water stress. Yet, only recently has the science of coupled human-

water system been initiated (Partelow, 2016; Sivapalan & Blösch, 2015) and transdisciplinarity

research utilized for societal sustainability problem solving (Polk, 2014). But, the understanding

of needs for data and knowledge transfer bridging organizational boundaries, and technological

aspects that challenge the praxis of policy making and planning are paradoxically increasing or

even worse, lacking (Cash et al., 2003; Hinkel, Bots, & Schlüter, 2014; Polk, 2014; Thomson,

El-Haram, Walton, Hardcastle, & Sutherland, 2007). For example, in a recent Water JPI (2014)

paper presenting eight major water topics for Europe (Horizon 2020), while identifying the gaps

and game changers, knowledge management was listed directly and indirectly in ALL of them.

These real life issues and academic research gaps are the motivators for this handbook.

Managing knowledge more effectively and efficiently might be a solution to many of the critical

water issues humans in the 21st century are, and will be, facing. Knowledge commons (e.g.

Brewer, 2014) and virtual, digital spaces of learning (Niccolini, in this book) provide some

unexpected and surprising rays of hope, of what might happen when knowledge is created and

managed well. The Israeli experience (Jacobsen, 2016; Siegel, 2015) is an illustration of a water

miracle (not phantoms) that can happen in the desert, and by extension, everywhere, where and

when people will set their minds to it.

As the new knowledge-driven economy continues to evolve, knowledge is being

recognized as a key asset and a crucial component of organizational, inter-organizational and

national strategy. The ability to manage knowledge, therefore, is quickly becoming vital for

securing and maintaining survival and success. As a result, organizations at all levels are

investing heavily in Information Systems (IS) and/or Knowledge-Based Systems (KBS)

technologies. Unfortunately, such investments frequently do not meet expected outcomes and/or

returns. For the purpose of this handbook we will recognize Knowledge Management (KM) as a

socio-technical phenomenon in which the basic social constituents such as person, team and

organization require interaction with IS/KBS applications to support a strategy and add value to

the organization (Russ, 2010) while improving the sustainability of a water system. Many

organizations and their executives recognize that the critical source of sustainable competitive

advantage is not only having the most ingenious product design, the most brilliant marketing

strategy, or the most state-of-the-art production technology, but also having the ability to attract,

retain, develop and manage its most valuable human assets (talent) and their knowledge and

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innovation. Furthermore, such an interaction of talent, processes and systems is what enables

organizations to develop and manage knowledge for success.

Sustainability has been defined as economic development that meets the needs of the

present generation without conceding the ability of future generations to meet their own needs

(e.g., Russ, 2014b.) With growing pressure from customers and regulators toward environmental

and social issues, organizations and governments at all levels are increasingly expected to

shoulder greater responsibility for making sustainable development a reality. Recent droughts

and water shortages worldwide and the advanced scientific understanding and documentation of

the impact of demographic and economic forces on water footprint and embedded water make

the need for sustainable development and management of water systems only more acute. This

requires policy makers, planners and executives to balance economic, business, social and

environmental concerns and outcomes. For that to happen, leaders need to quantify the

relationships of all those aspects across different time horizons and link their organizational

knowledge-base to strategy and outcomes so they can consider the tradeoffs of different

alternatives for their long-term success.

This book is envisioned as a manuscript that will provide a robust scientific foundation

for an inter-disciplinary, multi-perspective theory and practice of knowledge management in the

context of, and for the advancement of, sustainable water systems. The book goes beyond the

current literature by providing a platform for a broad scope of discussion regarding KM4SWS,

and, more importantly, by encouraging an interdisciplinary/transdisciplinary fusion between

diverse disciplines. Specifically, the call for proposals for this book solicited chapter proposals

from a multidisciplinary array of scholars to discuss socio-hydrology sustainable systems within

the present political (legislative), economic and technological context from a number of

disciplines/perspectives, including: Economic Development, Financial, Systems-Networks,

IT/IS Data/Analytics, Behavioral, Social, Water Systems, Governance Systems and Related

Ecosystems. Multi-level and multi-discipline chapters that synthesize diverse bodies of

knowledge were strongly encouraged. When appropriate, plurality of empirical methods from

diverse disciplines that can enhance the building of a holistic theory of KM4SWS were also

encouraged.

While preparing for, and editing this book, a number of alternative theoretical

frameworks were considered (e.g, Elliot, 2011). The multi-level framework that was adopted

(described briefly below) is an amalgamation of a number of models reviewed (some are listed in

the bibliography below) with the addition of models I developed regarding knowledge

management over the last twenty years of teaching and studying the subject.

The first building block (see Figure 1.) is the model of co-evolution of the Human

Systems (political, economic, technological, social; see discussions and indicators in, for

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example: Partelow, 2016, Vogt, Epstein, Mincey, Fischer, & McCord, 2015; mostly based on

Ostrom, 2009) and the Sustainable (in our case) Natural and Engineered Water Systems (see for

example Sivapalan & Blöschl, 2015). Such co-evolution results, of course, from the impact

human activities have (mediated by technology) on the systems and the responses and outcomes

of the water systems to these activities. The co-evolutionary model (e.g. Sivapalan Blöschl,

2015) was modified and enhanced by adding on the human system side: the complexity of the

different potential units of analysis involved on the human systems side, starting with an

individual, teams, organizations and then going up in complexity to inter-organization, national,

regional and global units and scales. Each unit has its own learning complexity and more

complex units, issues, and boundary management aspects (see excellent discussions of the

importance of this complex management in Cash et al., 2003). On the sustainable water system

side, the different levels of the systems were added (e.g. household, city, river basins, etc.). Each

one of them is connected to the framework by models that are used and/or understood by the

human actors (see the interesting discussion in Sivapalan & Blöschl, 2015, about two models:

stylized and comprehensive). Finally, Knowledge Management (KM) was added at the heart of

the Human Systems’ section and the Co-evolution’s section (the two KMs are of course related

and intertwined).

________________

Figure 1. about here

________________

The second level of the model is the construct of Knowledge Management (see Figure

2.). Here, the model developed by Russ, Fineman, and Jones, (2010) was used, with focus on the

actors, (or talent), the process, or specifically the learning and decision making, and the systems,

or in this case the knowledge based systems. The majority of the chapters in this book touch on

all three factors and illustrate different aspects (e.g. content and process) of KM.

________________

Figure 2. about here

________________

In the third level of the model, each one of the three constructs used as building blocks

for KM (listed above) was broken down into its specific models (see Figure 3.a.-3.e.). For

example, learning, might focus on tacit knowledge using the Kolb active learning model (1976),

or on codified learning using the virtual Ba model illustrated by Niccolini et al. in this book, or

any mix of the two; or others as appropriate for the case; all, (potentially) using up to the three

feedback loops of learning (e.g., Argyris’ double-loop learning, 2002; or the review in Tosey,

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Visser, & Saunders, 2012 of triple loop learning). The human actors’, talent was modeled using

the HC praxis model (Russ, 2014a) and the Knowledge-Based-Systems (KBS) using the six life

cycle stages of KBS (e.g., Russ, Jones, & Jones, 2008), including the sustainability aspect of the

KBS as well as consideration (Elliot, 2011).

________________

Figure 3. about here

________________

The complexity of the reality of KM in SWS are overwhelming, as they are illustrated in

the chapters in this book. Such complexity is a result of the nature of knowledge management

which could cut across ALL levels of the model, as well as across the unit of analysis in the

water systems. Add to that the complexity of the diverse scientific areas and the diverse styles of

learning and decision making of the different actors, and you can see a complex networked

system at its best.

But the truth of the matter is that one aspect is still missing from this analysis and must be

added to the proposed framework, thus adding the fourth level, time (see Figure 4.). Again and

again, while teaching my KM classes, consulting with clients, researching and reading other

practitioner and academic research, I was perplex by the failures of all the constituencies to

understand the importance of time, on its complexities for strategic decision making, managing

knowledge or human capital, among many others aspects. Time is one of the hidden assumptions

(dimensions) we have and use continually without giving it a second thought.

________________

Figure 4. about here

________________

In practice, the misalignment of time horizons (present and future) and time frames of

events, makes an enormous difference, but is rarely explicitly identified as an issue and/or

studied (see a rare very recent exception in Myllykoski, 2017). In our context of KM4SWS, the

key players that take actions or make decisions, not only might have a diverse set of expertise,

understating and knowledge of the subject at hand, but they also operate in a different time

“space”. Politicians’ time horizons of the relevant future for their decision making is different

from that of the farmer, the hydrologist and the weather scientist. Their understanding of an

event (and it time frame) and the implication the event might have within a complex system,

including the impact of time-lags and complex feedback loops, could be startlingly different and

diverse (from others) within the realm of their intentions, goals and their time horizon. This

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factor (of time) can explain by itself why knowledge is not used effectively regarding important

aspects of the sustainability of water systems. Corralling all the different actors into a single

space of knowledge and coherent time for the purpose of advancing sustainable water systems

can happen rarely if at all. One case in which all of this might potentially happen is in a major

disaster. Recent history should tell us that even in the vast majority of the cases of disaster this is

not sufficient to create such a coherent space. To illustrate the praxis of such situations, an

updated model of the “garbage can model of decision making” (Cohen, March, & Olsen, 1972) is

proposed here, where time is a multi-dimensional construct having a synchronized (or not) time

horizon, time frame, and event-time, or what I would define as the “quantum model of

organizational time”. This model advances the model of time described by Myllykoski (2017)

(building on Hernes, 2014) in which she described the past and the future as a stream of events,

that get their “true” meaning at the present time, resulting from a stream of events, creating or

enabling a decision to be made, seeing time as agentic (p. 23). Events, or the bits of information

perceived by the observer that are remarked by the actor as events, are seen as collapsing at the

time of the decision. As such, it enables us to freeze an understanding of the past, from the

present perspective and the planning for the future, confirming a present rational for the decision

regarding the future (Tsoukas, 2016). Viewing time as agentic, and seeing the stream of bits of

information coming from the past and going toward the future, collapsing at a time of a decision

into one interpretation, brings Schrödinger’s cat from the quantum realm into individual decision

making, and results in what I would call, the “individual quantum model of decision making”.

Adding the complexity of multiple key actors with different time frames, etc. results in an

“organizational quantum model of decision making” which is illustrated in Figure 4. Finally, one

plausible explanation for the success of role playing (Sivapalan & Blöschl, 2015) and simulation-

based learning (Deegan et al., 2014) in a complex context as described here, is because it allows

for the time horizons of the participating individuals and the time frame of the event they engage

with to become coherent, enabling an improved process of decision making.

The chapters in this book not only illustrate the framework proposed above, and suggest

new venues for improving our water systems; more than that, they illuminate opportunities to

eliminate the risks of a thirsty world.

Acknowledgment

The call for chapters for this book stimulated the authors to synthesize multi-level and multi-

discipline diverse bodies of knowledge by encouraging an interdisciplinary fusion between

diverse disciplines. The authors were invited to contribute chapters to the book based on

proposals approved by the editor. Each complete chapter received external, blind review in

addition to the editor review. The editor thanks Kelly Anklam for her assistance in editing this

introduction and number of chapters. He wishes to thank also Dr. Vallari Chandna for editing

number of chapters, as well as the reviewers for their in depths reviews. Finally the editor want

to thank the Philip J. and Elizabeth Hendrickson Professorship in Business at UW-Green Bay for

partial financial support. As always, all mistakes are his.

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Figure 1. The coevolution of human and water systems

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Figure 2. Knowledge management in sustainable water systems

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Figure 3. The three elements of knowledge management in sustainable water systems (detailed)

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Figure 4. The time aspect of the quantum organizational decision making model