=Paper=
{{Paper
|id=Vol-2239/article_17
|storemode=property
|title=Configuring Value Networks based on Subjective Business Values
|pdfUrl=https://ceur-ws.org/Vol-2239/article_17.pdf
|volume=Vol-2239
|authors=Jefferson Da Silva Reis,Patrcio de Alencar Silva,Faiza Allah Bukhsh,Angelica Felix de Castro
|dblpUrl=https://dblp.org/rec/conf/vmbo/ReisSBC18
}}
==Configuring Value Networks based on Subjective Business Values==
https://ceur-ws.org/Vol-2239/article_17.pdf
Configuring Value Networks based on Subjective
Business Values
Jefferson da Silva Reis1 , Patrı́cio de Alencar Silva1 , Faiza Allah Bukhsh2 , and
Angélica Félix de Castro1
1
Universidade Federal Rural do Semi-Árido, Rio Grande do Norte, Brazil
Programa de Pós-Graduação em Ciência da Computação
sreis.jefferson@gmail.com,
{patricio.alencar,angelica}@ufersa.edu.br
2
University of Twente, Department of Computer Science, The Netherlands 7500 AE
f.a.bukhsh@utwente.nl
Abstract. Monetary profitability is an objective value essential to the
sustainability of a value network. The analysis of this requirement con-
tinues to receive substantial attention by the e3 value research community
thus far. However, subjective values such as privacy, security and trust
might also play a key role on the configuration of a value network, es-
pecially when it is necessary to differentiate equivalent monetary value
propositions. This paper describes an ontological proposition for config-
uring value networks based on subjective values. The ontology is aimed
to be used as complement of the e3 value framework, blending concepts
of Multiple Agency Theory, Enterprise Ontology, Value Modeling and
Speech Acts Theory. We demonstrate our approach on a case scenario
based on the Directive 2009/72/EC, which defines common rules for the
liberalization of the European market of energy.
Keywords: Ontology, Subjective Values, Value Networks
1 Introduction
A value network has been initially referred to as a group of enterprises exchanging
objects of economic value to satisfy the needs of a market of consumers [13].
Aiming to define formally how business actors could collaborate within a value
network, Gordijn and Akkermans proposed the e3 value framework to support the
modeling and profitability analysis of value networks. The fundamental principle
grounding the e3 value ontology seems to be the one of ”economic reciprocity”,
which governs how business actors sacrifice objects of economic value to obtain
other ones (of equivalent value) in return. There, the difference between sacrifice
and benefit is measured, as objectively as possible, as monetary profit. It is
reasonable that reaching goals stated for this objective value is necessary to the
economic sustainability of a value network, but not sufficient for a consumer to
declare that his business need(s) will be fully satisfied with such a measure of
value [2, 18]. For this case, subjective values such as assurance, privacy and trust
might come into play for a consumer not only to differentiate between equivalent
monetary value propositions, but also acquire products and services the valuation
of which would depend on previous experience, such as products bought online
or innovative services (e.g. smart metering and medical nanotechnology).
Adopting a Design Science perspective on research [19], the research question
addressed in this paper is how a value network could be configured based on sub-
jective values. From an organizational perspective [6], this question is initially
threefold: What subjective values could be considered as important or even essen-
tial for consumers in a value network? How to measure these subjective values?
How these values might be related to the satisfaction of a certain consumers’
business need?
To start addressing these questions from an Information Systems perspective,
we propose an ontology for configuration of value networks based on subjective
values. The ontology was formalized in Web Ontology Language (OWL-DL),
supplemented by a set of rules defined in Semantic Web Rule Language (SWRL)
[11], and blends concepts of the e3 value ontology [10], Enterprise Ontology [7],
Speech Acts [15] and Value Monitoring Ontology [3].
The rest of this paper is organized as follows. In Section 2, we describe a case
scenario that motivated our conceptual analysis of the role of subjective values
on the configuration of value networks. In Section 3, we describe an ontology
for modeling and analysis of what can be named thus far as qualitative value
networks. In Section 4, we return to the motivating scenario to demonstrate the
modeling utility of the ontology on a case scenario depicted as a value network
of Smart Metering services. In Section 5, we provide theoretical conclusions,
threats to validity and future steps of this research.
2 Observational Case Study
The case scenario presented in this section is a projection on future markets of
liberalized Energy services in Europe, normalized by the Directive 2009/72/EC
of the European Union [17], and described in earlier research by Silva et al. [2]. In
this scenario, householders will have the option to choose not only among energy
providers, but also smart metering companies that suit their needs the best. As
depicted in Fig. 1, the case scenario was shaped as an e3 value model. The final
consumer playing the Agency role of a principal is a householder represented
by a market segment of Balance Responsible Parties (BRP). EU reports have
revealed that one of the main issues on the adoption of smart metering solu-
tions by the European population concerns privacy, that is, energy consumption
information might be explored opportunistically [1]. Hence, householders might
consider peer assessment and evaluation of the privacy provided by such an in-
novative service before entering into an agreement with a metering operator.
This is therefore a special business case where it is not only the monetary price
of the technology that matters, but also the intangible value to be experienced
by the final consumer.
A BRP is motivated by the opportunity of balancing energy consumption, or
even selling unused energy through demand-response of the smart meters. Thus,
a BRP has the option to create value from smart metering assets provided as
core business objects by a market segment of Metering Operators. A householder
might obtain metering accounting or auditing reports from three value paths.
In the first option, the householder could possess a metering asset provided by
a Metering Operator, once granted with a Metering Responsible Party (MRP)
accreditation by the Transmission System Operator (TSO). In exchange, the
householder allows the TSO to have access to private verifiable information of
energy consumption through an Open (virtual) Monitoring Channel. This is
necessary for monitoring and control of the overall state of imbalance reduction
of the network by the TSO. In the second option, the householder could delegate
the energy metering activity to the peers from which the energy is bought, e.g.
aggregators or Distributed Energy Resources (e.g. wind turbine owners), both
allowed by law to operate with MRP accreditations. The analysis of qualitative
values relevant for the assessment of these options is demonstrated in Section
4.
Fig. 1. Case scenario of a smart metering value network extracted from Silva et al. [2]
3 Semantic Value Network Ontology
The ontology was formalized in OWL and complemented with SWRL rules to
allow semi-automatic configuration of qualitative value networks. However, the
scope of this paper is limited to the demonstration of the relevance of subjective
values on the configuration of a value network.
A business need is owned by a principal consumer (actor or market segment)
and is the starting point of configuration of a qualitative value network. A pol-
icy may assume five organizational arrangements (or patterns) that represent
different configurations whereby a business need could be satisfied. The concep-
tual foundation of these patterns is described in detail by Silva et al. [2] and
formalized in OWL in the ontology depicted parsimoniously in Fig. 2. Follow-
ing the organization of the value network model, the value indicators allow for
analysis of the subjective values assigned to products and services offered as
value propositions to the final consumer. The value indicators not only qualify
the economic effectiveness of a value proposition meeting a business need, but
also the economic efficiency of a policy arrangement. Yet, value indicators do
not replace the profitability analysis supported by the e3 value framework, but
adds an extra layer of information to refine the selection of value propositions
by the consumer.
Fig. 2. SVNO full viewpoint
3.1 Business need
In e3 value, the notion of a business need is reified as a value object desired by
the consumer. Here, this notion is extended by separating the identity of a core
business object (i.e. a product or service category) from its value, which can be
objective (e.g. quantity, quality, time and location) or subjective (e.g. privacy,
reliability or trust). In e3 value, a core business object meets a consumer’s need
when its investment is lower than its sacrifice, which is measured by quantifying
the monetary resources paid in exchange for the core product or service provided
by the network.
From our best knowledge, objective values such as quality, time and location
are not taken into consideration in an e3 value profitability analysis. Despite the
importance of these values, we take the e3 value quantitative approach as suffi-
cient for objective valuation of core business objects and move our discussion
into the role of subjective values on classifying value propositions of equiva-
lent objective values. Accordingly, a business need is demanded by a consumer
acting as an Agency principal of the network, and is satisfied not only by a
core business value object, but also by the value indicator assigned to this ob-
ject. There are two subclasses of value indicator: objective value and subjective
value. Different from objective values, subjective values are perceptual, and the
assessment of which involves at least two Agency parties and depends on indi-
vidual experience. The difference between the perception of two Agency parties
on the same value assigned to a value object is a measured value, which has three
value partitions: value surplus, value balance and value shortage [16]. The logic
underpinning subjective value measurement is detailed further in this section.
Moreover, the principal has also a business rationale, e.g. a business weakness,
threat or opportunity (the only rationale treated in this paper). A business need
is represented as a defined class in the ontology, as depicted in Fig. 3.
Fig. 3. Description logic representation of the class business need
3.2 Policy
A policy is defined here as an organizational pattern of actors, activities and
objects connected by speech acts and inspired by the NIST metamodel of Role-
Based Access Control (RBAC) [8]. Preventive monitoring is intrinsic to Multiple
Agency, and the ontology formalizes five Agency monitoring patterns for value
networks, described in detail in Silva et al. [2]. The patterns are named as single,
double-check, chokepoint, committee and gossip, and comprise different strategies
whereby the principal might evaluate products and services offered by the net-
work. The pattern described in detail here is the committee pattern, due to its
completeness on covering the case scenario. The ontology defines four actor roles
(i.e. principal, third-party, regulator and agent), four value activity roles (i.e.
front-end, back-end, resource and regulatory) and four value object roles (i.e.
core business, proof-of-performance, certification or accreditation and counter-
object). Actor roles are connected to activity roles by relationships of authority,
competence and responsibility, inspired by the Enterprise Ontology [7]. Activities
roles are connected to object roles by speech acts, (some of them adapted by En-
terprise Ontology as production acts) such as produce, consume, grant, transfer,
bundle and distribute. The formalization of the committee pattern is depicted in
Fig. 4.
Fig. 4. Description logic definition of the committee monitoring pattern
3.3 Value indicators
Subjective values are normally used by consumers when evaluating a product
or service before acquisition. Without experience on the use of the product or
service, it is also common that consumers consider other peers’ evaluation on sub-
jective values of the desired commodity. Such a practice is not recent, and there-
fore should not be exclusively associated with the current trend of e-Commerce
solutions. However, subjective values are shaped not only by individual and pri-
vate productive acts of experience, but also by social communication, which is
closely related to the reputation of a commodity and its respective provider.
From a Speech Acts perspective, it is possible to argue that subjective values
might assume different roles, depending on who is making a (subjective) value
proposition to whom.
Taking this perspective into a value network organized with Multiple Agency
roles, the final consumer acting as a principal receives value propositions of core
business objects produced by back-end activities of competence of third-parties,
and transformed (i.e. bundled, distributed, granted or transferred) into value-
added commodities by agents and regulatory parties. Considering that suppliers
acting as third-parties are prone to communicate biased evaluations on their own
products or services, it is assumed here that their evaluation on corresponding
products or services shall not be fully taken into consideration by a rational
consumer. Thence, the social construction of a business value in this case will
involve the consumer acting as a principal, agents and regulators.
For the principal, what is relevant is the expectation of value to be created us-
ing the product or service produced by third-parties. In this sense, the principal
initially predicts his expected value for the commodity to be acquired. Nonethe-
less, for the agent and regulators, whose evaluation will be considered by the
principal on acquiring the commodity, and who somehow experienced or sensed
the value of the commodity, the subjective value will have the role of perceived
value. While a regulator testifies his perceived value of the commodity by veri-
fication or witnessing, an agent reports own perception on the same commodity
through business transformation. The description logics definition for subjective
value and corresponding value scales (or partitions) are depicted in Fig. 5-a
and Fig. 5-b, respectively. The SWRL rules for assignment of expected and
perceived values are summarized in Table 1.
Fig. 5. a) Description logic definition of subjective value; b) subjective value partition
Table 1. SWRL rules for assignment of expected value and perceived value
Agency-role value
SWRL rule for value assignment
viewpoint
svn:Principal(?p)ˆsvn:hasSubjectiveValue(?p,?s)ˆ
Value expected by svn:SubjectiveValue(?s)ˆsvn:predicts(?p,?svp)ˆ
the principal svn:SubjectiveValuePartition(?svp)
→ svn:hasExpectedValue(?s,?svp)
svn:Agent(?p)ˆsvn:hasSubjectiveValue(?p,?s)ˆ
svn:SubjectiveValue(?s)ˆ
Value perceived by
svn:testifies/reports(?p,?svp)ˆ
the agent
svn:SubjectiveValuePartition(?svp)
→ svn:hasPerceivedValue(?s, ?svp)
svn:Regulator(?p)ˆsvn:hasSubjectiveValue(?p,?s) ˆ
svn:SubjectiveValue(?s)ˆ
Value perceived by
svn:testifies/reports(?p, ?svp)ˆ
the Regulator
svn:SubjectiveValuePartition(?svp)
→ svn:hasPerceivedValue(?s, ?svp)
Subjective value is a class defined as an enumerated set of partitions adapted
from the SERVQUAL model to express measures for expected value or perceived
value, as depicted in Fig. 5-b. Accordingly, the value partitions comprise: ideal
value, forecasted value, equitable value, deserved value and minimum tolerable
value [14]. The difference between expected value (predicted by the principal)
and perceived value (testified by at least one regulator and reported by at least
one agent) is assessed qualitatively as measured value. The logic underpinning
the qualitative assessment is formalized in SWRL rules summarized in Table 2.
Table 2. SWRL rules for assignment of measured value
Partition of
SWRL rule for measured value assignment
measured value
svn:Principal(?p)ˆsvn:demands(?p, ?bn)ˆ
svn:BusinessNeed(?bn)ˆ
svn:hasSubjectiveValue(?p, ?sv)ˆ
Value surplus
svn:hasExpectedValue(?sv, svn:EquitableValue)ˆ
svn:hasPerceivedValue(?sv, svn:IdealValue)
→ svn:hasMeasuredValue(?bn, svn:Surplus)
svn:Principal(?p)ˆsvn:demands(?p, ?bn)ˆ
svn:BusinessNeed(?bn)ˆ
svn:hasSubjectiveValue(?p, ?sv)ˆ
Value balance
svn:hasExpectedValue(?sv, svn:EquitableValue)ˆ
svn:hasPerceivedValue(?sv, svn:EquitableValue)
→ svn:hasMeasuredValue(?bn, svn:balance)
svn:Principal(?p)ˆsvn:demands(?p, ?bn)ˆ
svn:BusinessNeed(?bn)ˆ
svn:hasSubjectiveValue(?p, ?sv)ˆ
Value shortage
svn:hasExpectedValue(?sv, svn:EquitableValue)ˆ
svn:hasPerceivedValue(?sv, svn:DeservedValue)
→ svn:hasMeasuredValue(?bn, svn:shortage)
4 Demonstration: A Case Scenario in Smart Metering
We now return to the case scenario introduced in Section 2. The problem of this
case is how a householder could analyze value propositions of smart metering
services based on qualitative values that this technology might return. Earlier
research conducted by the European Commission has uncovered privacy as a
key value expected by the European population to be offered by smart metering
operators. As smart metering assets are becoming more intelligent and innova-
tive, the acceptance of this technology by European householders shall depend,
among many other factors not covered in this paper, on progressive peer evalua-
tion of subjective values such as privacy to be created using this technology. This
evaluation can be supported by e-Government channels providing transparent
accounting of infrastructure services to the population [4].
Hence, to decide which metering operator to choose, a householder might take
into consideration some evaluation provided by agents that used the technology.
After declaring a business need of a smart metering service and predicting a
subjective value to be created by its use, a householder has the option to select
one among many policies whereby the desired commodity could be acquired.
The Agency monitoring patterns proposed by Silva et al. [2] can be used for
this purpose. For brevity, our demonstration will be resumed to the committee
pattern.
Once the policy pattern is selected, the next step is to evaluate its internal
subjective value propositions for the smart metering asset as a core business
object. In this case scenario, the BRP’s business need could be satisfied by the
metering asset prospected to offer the best level of privacy, as a subjective value
of relevance. This prospection has been referred in this paper as measured value,
which is defined by the difference between the principal’s expected value of the
core business object and the agents perceived value of the same object, based on
previous experience or use. Let it be supposed that:
(1) the market segment of Metering Operators acting as third-parties has three
individuals for analysis;
(2) the BRP predicts an expected value for the metering asset as equitable;
(3) the MRP-Aggregator reports a perceived value as forecasted ; and
(4) some MRP-DER reports a perceived value as equitable;
Then, the rules for definition of the measured value taking the BRP’s perspec-
tive as dominant apply as summarized in Table 3. Accordingly, it is possible to
notice that the metering asset provided by the Metering Operator 1 is prospected
to generate value surplus as a measured value on the BRP’s side.
Table 3. Qualitative value prediction of Metering Operator’s service based on evalu-
ation provided by delegated agents
Third-
Party Agent Agent
(Metering (MRP-DER) (MRP-Aggregator)
Operator)
svn:hasSubjectiveValue svn:hasSubjectiveValue
(svn:BRP, svn:Privacy)ˆ (svn:BRP, svn:Privacy)ˆ
svn:hasExpectedValue svn:hasExpectedValue
(svn:Privacy, (svn:Privacy,
Metering svn:EquitableValue)ˆ svn:EquitableValue)ˆ
Operator svn:hasPerceivedValue svn:hasPerceivedValue
1 (svn:Privacy, (svn:Privacy,
svn:ForecastedValue) svn:IdealValue)
→ svn:hasMeasuredValue → svn:hasMeasuredValue
(svn:SmartMetering, (svn:SmartMetering,
svn:Surplus) svn:Surplus)
svn:hasSubjectiveValue svn:hasSubjectiveValue
(svn:BRP, svn:Privacy)ˆ (svn:BRP, svn:Privacy)ˆ
svn:hasExpectedValue svn:hasExpectedValue
(svn:Privacy, (svn:Privacy,
Metering svn:EquitableValue)ˆ svn:EquitableValue)ˆ
Operator svn:hasPerceivedValue svn:hasPerceivedValue
2 (svn:Privacy, (svn:Privacy,
svn:EquitableValue) svn:DeservedValue)
→ svn:hasMeasuredValue → svn:hasMeasuredValue
(svn:SmartMetering, (svn:SmartMetering,
svn:Balance) svn:Shortage)
svn:hasSubjectiveValue svn:hasSubjectiveValue
(svn:BRP, svn:Privacy)ˆ (svn:BRP, svn:Privacy)ˆ
svn:hasExpectedValue svn:hasExpectedValue
(svn:Privacy, (svn:Privacy,
Metering svn:EquitableValue)ˆ svn:EquitableValue)ˆ
Operator svn:hasPerceivedValue svn:hasPerceivedValue
3 (svn:Privacy, (svn:Privacy,
svn:DeservedValue) svn:DeservedValue)
→ svn:hasMeasuredValue → svn:hasMeasuredValue
(svn:SmartMetering, (svn:SmartMetering,
svn:Shortage) svn:Shortage)
5 Conclusions and Future Research
In this paper, we have addressed the research question of how a value network
could be configured based on subjective values. We recognize that meeting a
consumers’ need with value objects exchanged by a reciprocate monetary price
is necessary, but insufficient to state that the need will be fulfilled. The ontology
proposed here defines a business need as a composition of a desired value object
(i.e. a product or service category) and its value components (i.e. objective or
subjective values). For objective value assessment, we recommend the use of
the e3 value mechanism of profitability analysis to verify the objective values of
quantity and quality (e.g. monetary price) assigned to value propositions. For
subjective value assessment, we assume that values such as privacy, security and
trust are perceptual and dependent on social communication. Accordingly, the
ontology includes five Agency monitoring patterns of Silva et al. [2] to indicate
the provenance of the value propositions that will possibly satisfy a consumer’s
business need. For simplicity, only one pattern is demonstrated in this paper.
Moreover, the ontology is complemented by a set of SWRL rules to support
semi-automated classification and selection of value propositions based on the
need of value surplus on the consumer’s side.
Gómez-Pérez [9] proposes a framework for ontology evaluation, which com-
prises three phases: (1) verification of correctness, consistency and completeness;
(2) validation via theoretical demonstration, prototyping or case study applica-
tion; and (3) evaluation of community acceptance, modeling utility and usability.
The correctness and consistency of the ontology has been checked by using OWL2
and a SRWL plugin for Protégé [12]. Completeness has been verified according
to an Ontology Requirements Specification Document (ORSD) not described
in this paper. For validation, we have been using observational case studies in
digital music clearance [10], energy imbalance reduction [3], and customs control
[5] for research problem exploration and technology evaluation. Thus far, the
ontology has not yet been applied in interventional case studies or submitted to
users’ evaluation and surveying, which comprise the current threats to validity
of this research.
For future work, this research will follow three directions. First, a more precise
characterization of subjective business values demands philosophical grounding.
Business values should not be misinterpreted as soft goals (as defined in Re-
quirements Engineering) or Non-Functional Requirements (NFR) analysis. It is
necessary to investigate the role of subjective value analysis for the overall sus-
tainability of value networks. Second, it is necessary to design a mechanism for
integrity check of transactions that compose a value network, also taking sub-
jective values into consideration. For a while, our analysis is driven by a Service
Dominant Logic, focused on a single consumer’s business need. However, it is
relevant to resolve two or more business needs covered by a same value network.
Third, the logic of qualitative assessment proposed here will be revisited based
on related research in gossip algorithms and evaluation of products and services
provided by e-Commerce platforms.
References
1. European commission: Country fiches for electricity smart metering (accompany-
ing the document), report from the commission, benchmarking smart metering
deployment in the eu-27 with a focus on elec-tricity. swd (2014) 188 final, brussels,
17 june (2014)
2. de Alencar Silva, P., Bukhsh, F.A., da Silva Reis, J., de Castro, A.F.: Agency mon-
itoring patterns for value networks. In: International Conference on the Economics
of Grids, Clouds, Systems, and Services. pp. 81–93. Springer (2017)
3. de Alencar Silva, P., Weigand, H.: Enterprise monitoring ontology. Conceptual
Modeling–ER 2011 pp. 132–146 (2011)
4. Bertot, J., Estevez, E., Janowski, T.: Universal and contextualized public services:
Digital public service innovation framework (2016)
5. Bukhsh, F.A., Weigand, H.: Smart auditing–innovating compliance checking in
customs control. In: Business Informatics (CBI), 2013 IEEE 15th Conference on.
pp. 131–138. IEEE (2013)
6. Cameron, K.: Critical questions in assessing organizational effectiveness. Organi-
zational dynamics 9(2), 66–80 (1980)
7. Dietz, J.L.: Enterprise ontology: theory and methodology. Springer Science & Busi-
ness Media (2006)
8. Ferraiolo, D.F., Sandhu, R., Gavrila, S., Kuhn, D.R., Chandramouli, R.: Proposed
nist standard for role-based access control. ACM Transactions on Information and
System Security (TISSEC) 4(3), 224–274 (2001)
9. Gómez-Pérez, A.: Ontology evaluation. In: Handbook on ontologies, pp. 251–273.
Springer (2004)
10. Gordijn, J., Akkermans, J.: Value-based requirements engineering: exploring inno-
vative e-commerce ideas. Requirements engineering 8(2), 114–134 (2003)
11. Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.,
et al.: Swrl: A semantic web rule language combining owl and ruleml. W3C Member
submission 21, 79 (2004)
12. Musen, M.A.: The protégé project: a look back and a look forward. AI matters
1(4), 4–12 (2015)
13. Normann, R., Ramirez, R.: From value chain to value constellation: Designing
interactive strategy. Harvard business review 71(4), 65–77 (1993)
14. Parasuraman, A., Zeithaml, V.A., Berry, L.L.: Servqual: A multiple-item scale for
measuring consumer perc. Journal of retailing 64(1), 12 (1988)
15. Searle, J.R., Vanderveken, D.: Foundations of illocutionary logic. CUP Archive
(1985)
16. Steedman, I.: Positive profits with negative surplus value. The Economic Journal
85(337), 114–123 (1975)
17. Union, E.: Directive 2009/72/ec of the european parliament and of the council of
13 july 2009 concerning common rules for the internal market in electricity and
repealing directive 2003/54/ec. Off. J. Eur. Union L 211, 55–93 (2009)
18. Weigand, H., Johannesson, P., Andersson, B., Bergholtz, M., Edirisuriya, A.,
Ilayperuma, T.: Strategic analysis using value modeling–the c3-value approach. In:
System Sciences, 2007. HICSS 2007. 40th Annual Hawaii International Conference
on. pp. 175c–175c. IEEE (2007)
19. Wieringa, R.J.: Design Science Methodology for Information Systems and Software
Engineering. Springer, Heidelberg (2014)