Overview of the Balancing Pervasive Information Systems Project

and a Call for Research Collaborations

Sherry L. Xie

1,2,3

School of Information Resource Management, Renmin University of China, 59 Zhongguancun Ave., Beijing, China

Center for Digital Records Management Research, Beijing, China

Key Laboratory of Knowledge and Data Engineering, Ministry of Education of China, China

Keywords: Pervasive Information Systems, Ubiquitous Computing, Pervasive Computing, Digital Decision, Technology

Accountability, Accountability Information, Digital Records.

Abstract: The Research on Balancing Theories and Mechanisms to Pervasive Information Systems (B2P) is one of the

component studies of the Records-centered Digital Information Management Theory and Mechanisms

(DI{R}Mtm) Project, which aims pointedly at synchronizing records/information related research interests

with the evolution of information technologies and their joint impact on society. Due to the breakthroughs in

cloud computing, big data, the Internet of Things and artificial intelligence, ubiquitous/pervasive computing,

conceived in 1988, has now been rapidly advanced. As its materialization in organizations, pervasive

information systems (PISs) converges information technologies, data, digital operations and human actors

and functions as the main channel for digital decisions. Digital decisions, therefore, are autonomous,

algorithmic and penetrating, possessing influential abilities that are much greater than any single technology

in human history, including the Internet. Such abilities make it a strong force for societal advancement, yet,

at the same time, a potential hurdle – even harm – for decision recipients who are unaware of the decision-

making methods. The B2P study is conceived as a reaction to this phenomenon. This paper gives an overview

of the B2P study and calls for collaborations among researchers from the various fields relevant to the aims

of the study, i.e., archival science, records management, information science, pubic administration, law, social

sciences, and computer sciences.

1 INTRODUCTION

The Balancing Pervasive Information Systems

Project, fully entitled as Research on Balancing

Theories and Mechanisms to Pervasive Information

Systems (B2P), is one of the component studies of the

Records-centered Digital Information Management

Theory and Mechanisms (DI{R}Mtm) Project. The

DI{R}Mtm project, founded by the Fundamental

Research Funds for Central Universities and the

Research Funds of Renmin University of China

(15XNL032), is currently at its second phase (2018 –

2020), which, as suggested by the findings of the first

phase of the DI{R}Mtm project, aims more pointedly

at synchronizing research interests with the evolution

of information technology. This paper gives an

overview of the B2P study and calls for

collaborations among researchers from the various

fields relevant to the aims of the study, i.e., archival

science, records management, information science,

pubic administration, law, social sciences, and

computer sciences.

2 STUDY OBJECTS

The study objects of the B2P study include two major

topics: pervasive information systems and the issue

with digital decision accountability.

2.1 Pervasive Information Systems

Pervasive information systems (PISs) is an emerging

paradigm in the field of information systems (ISs),

which emphasizes pervasiveness. The idea of

pervasiveness is in concert with the visions of

ubiquitous computing (UbiCom) and (the later on)

pervasive computing (PerCom), all aiming at

“integrating computers seamlessly into the world”

(Weise, 1991). PISs, therefore, can be viewed as an

Xie, S.

Overview of the Balancing Pervasive Information Systems Project and a Call for Research Collaborations.

DOI: 10.5220/0007234304070416

In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR, pages 407-416

ISBN: 978-989-758-330-8

407

assembly of information technologies that extends

ISs from traditionally a PC network into a web of

heterogeneous cooperating/communicating objects,

both inside and outside the physical boundary of

organizations. At present, the assembly includes

technologies such as artificial intelligence (AI), big

data (BD), the Internet of Things (IoT), and cloud

computing (CloudCom), all of which – termed as

PISs component technologies in this application –

possess with each other interrelated relationships.

Relying on the entry relationships of the Library of

Congress Subject Heading (LCSH) (LC, 2018) and

the subject relationships of the databases of Academic

Search Complete and Business Source Complete

(ASC.BSC) (EBSCO, 2018), Figure 1 displays the

relationships among the PISs component

technologies (LCSH: year of entry creation; year of

revision and ASC.BSC: earliest publication year).

Figure 1: PISs and Its Component Technologies.

The application of PISs can be characterized as

personal, domestic, corporate, and public

(Kourouthanassis et al., 2010). The proposed research

limits its inquires to “corporate” and refines it as

organizations represented by government institutions

and business corporations. This is because first, the

setting of organization is the indigenous context to the

origination of ISs (Davis, 1974) and second, these

representative organizations typically have a more

influential stance than other types of organizations,

which signals research priority. Figure 2 depicts the

sitting of a PISs organization in the PISs environment,

where it

operates with both an administrative

boundary established by functions and activities and

an organizational data boundary made up by diverse

types of data threads supplied by the PISs component

technologies. By the very nature of PISs, the

organizational data boundary goes beyond the

administrative boundary and it interacts dynamically

with data flows from both within and outside. As

such, an organizational PISs data repository pulls or

receives data from other PISs organizations and at the

same time, contributes data threads to the PISs data

universe.

Figure 2: A PISs Organization in a PISs Environment PISs

and Its Component Technologies.

Deploying PISs in organizations is now believed

imperative or inevitable because of the comparative

advantages (or surviving capabilities) it offers.

Empowered by PISs defining features such as being

embedded, distributed, context-aware, portable and

real-time, organizations are becoming “smart” or

“intelligent”: their manners of handling both their

internal and external affairs are becoming more

prompt, precise and proactive. Underneath this

smartness/intelligence lies one foundation –

autonomous decision making, which outputs

decisions without human intervention. Two main

types characterize such decisions:

machine/algorithm-made decisions, which are done

by digital agent alone, and machine/algorithm-aided

decisions, which are made jointly by human and

digital agents. Digital agents here include any data

processing devices or models, large or small,

independent or as one part of a complex whole. As

Figure 3 displays, a PISs enabled smart organization

utilizes both human and digital agents in all its

decision-making processes, be they strategic or

transactional, and many of the decision-making

processes are invisible to humans, be they human

agents or human decision recipients. Indeed, being

invisible to human is the ultimate goal of PISs

deployment as the UbiCom idea indicates that “[t]he

most profound technologies are those that disappear”

(Weiser, 1991).

FR-HT 2018 - Special Session on Managing Digital Data, Information and Records: Firm Responses to Hard Technologies

408

Figure 3: Characteristics of PISs Decision-Making.

As such, entities in decision making processes

exhibit relationships that are dynamic and interactive,

so do the made decisions in terms of the time of their

existence and the way they are being delivered.

Decision maker and decision recipient are two typical

types of entities in decision making processes; in

PISs, however, they function in a much more

complicated manner. As exemplified in Figure 4,

where a PISs deploying organization (dotted line) is

situated in a backdrop made up by the other types of

PISs organizations, decision maker has three types

and decision recipient has as many as seven. For

decision makers, there are PISs human agent (PISs H-

Agent), PISs digital agent (PISs D-Agent), and PISs

organization agent (PISs O-Agent), with the former

two being considered as individual or independent

unit and the later a collective whole. For decision

recipients, there are:

 Potential employees of the PISs (i.e., job

applicants);

 Employees of the PISs, who are also the PISs

H-Agent;

 PISs users, who voluntarily make use of the

services and/or products provided by the PISs

deploying organization for work purposes;

 PISs consumers, who consume the services

and/or products provided by the PISs deploying

organization for personal purposes; and

 All the three types of decision makers.

Outside this particular PISs deploying organization

are other PISs deploying organizations, either of the

type of government institution or business

corporation, and PISs technology organizations. The

type of PISs deploying organizations together with

the type of PISs technology organizations constitutes

PISs controlling organization in the sense that, for the

former, they control the specific deployment of PISs

in their respective organizations, and for the latter,

they control the underlying PISs technologies of all

PISs deployments.

Figure 4: Entity Relationships in PISs Decision Making

Environment.

These decision entities are listed as 3 types below.

Type 1: Decision Maker:

 PISs Organization

 PISs H-Agent (individual)

 PISs D-Agent (independent unit)

 PISs O-Agent (the collective whole of H-Agent

and D-Agent)

 Other PISs organization

Type 2: Decision Recipient:

 For one PISs organization:

o PISs H-Agent

o PISs D-Agent

o PISs O-Agent

o PISs Consumers

 Other PISs organization

 None-PISs Organization

 PISs Potential Employee

 PISs Employee

Overview of the Balancing Pervasive Information Systems Project and a Call for Research Collaborations

409

Type 3: PISs Decision:

 Algorithm-Aided

 Work Decision: Transactional

 Work Decision: Strategic and Transactional

 Living Decision: Transactional

 Work Decision: Recruitment

 Work Decision: Performance

 Work Decision: Strategical

 Work Decision: Purchase

2.2 The Issue with Digital Decision

Accountability

Emerging in this PISs environment is the concern

about digital decision accountability, i.e., how PISs

enabled decision-making processes can be explained

and justified when legitimate inquiries arise.

Accountability is a concept that offers many utilities

(Jabbra and Dwivedi, 1989; Mulgan, 2000;

Lindberg, 2009; Bovens, 2010) and is entangled

with many other concepts such as responsibility,

liability, transparency, etc. (Fox, 2007; Hood, 2010;

Thompson, 2014; Castiglione, 2018). Its core,

however, can always be distilled into the notion of

accounting for actions or decisions. In this

application, “accounting for” is furthered as two

distinctive concepts, i.e., explanations and

justifications. Explanations here refer to the

information that answers the question how decisions

were made, and justifications here refer to the

information that answers the question why the

decisions were so made. Together, these two types

of information form what the proposed research

labels as (digital decision or PISs) accountability

information, which is one of the fundamental

constituents of the (digital decision or PISs)

accountability regime. Accountability information

can be described by two aspects: the way by which

it comes into existence and the way by which it

participates in decision-making processes. Tightly

associated with accountability information are the

concepts of availability and answerability.

Availability here has two forms: one from the stance

of the accountor and the other from that of the

accountee (both terms from Bovens, 2007).

Availability for an accountor means the provision of

accountability information, which is aided by the

actions of keeping, identifying and delivering

accountability information, and availability for

accountee means the obtaining of accountability

information, which requires the actions of initiating

accountability inquiry and receiving accountability

information. Answerability here means that the

provided/received information must be able to

answer the inquiry, i.e., it needs to be relevant (to

the decision being questioned), sufficient (for

covering the entire decision-making process),

understandable (by the accountee’s criteria) and

credible (by commonly accepted criteria).

3 CURRENT RESEARCH

Research dedicated to accountability in PISs,

including UbiCom and PerCom, does not currently

exist (dedicated here refers to restricting

“accountab*” in Title in databases of Web of Science

and Academic Search Complete). For its components,

a small number of dedicated articles were found: 25

in English (1 on AI, 3 on IoT, 6 on BD, and 15 on

CloudCom) and 4 in Chinese (1 on AI and 3 on BD),

and an extended search for “right to know”, a concept

close to accountability, returned no hits. As Figure 5

shows, PISs technology and accountability are both

topics that the academia is keen about and despite the

small numbers of articles, the relationship between

the development of technology and accountability

tends to be positive, i.e., the number of articles

regarding the intersection of accountability and a

particular technology increases along with the

increase of the number of articles regarding the

technology. This demonstrates that there is increased

attention paid to technology accountability.

Figure 5: Development of PISs Technologies and Their

Relationships with Accountability.

From reviewing those dedicated articles and the

relevant ones cited by them, the following

observations can be generated:

 Discussions on accountability in relation to

advanced technology started with AI in 2001

7939

6486

2294

4892

9160

11123

0 4000 8000 12000

Accountability

AI+Accountability

PISs+Accountability

IoT+Accountability

BD+Accountability

CloudCom+Accountability

FR-HT 2018 - Special Session on Managing Digital Data, Information and Records: Firm Responses to Hard Technologies

410

and kept accelerating with the other

components in particular cloud computing;

 Most of the articles take the viewpoint of IT

(i.e., authored by IT professionals), and they

focus mainly on data privacy (Xhafa et al.,

2015; Grunwell and Sahama, 2015) and user

accountability, i.e., how users or employees

can be accountable for their actions (Boos, et

al., 2013; Gao and Iwane, 2015; Khan et al.,

2016) (labelled in this application as individual

accountability);

 Others are mainly authored by sociology and

legal experts, which focus on both the concept

itself and its application to technologies (6,

2010; Vedder and Naudts, 2017) (labelled in

this application as organizational

accountability);

 Information is not the focus of study, albeit it

appeared inevitably (e.g., Weber, 2011; Boos,

et al., 2013);

 There are three main consensuses:

o The complexity of the issue (e.g., Ko et al.,

2011; Gao and Iwane, 2015);

o The need for accountability to be

addressed at the early stage of technology

development (e.g., Boos, et al., 2013;

Arnaboldi et al., 2017); and

o The need for further research as many of

the technologies are new or being renewed

(e.g., Arnaboldi et al. 2017).

For the four Chinese articles, only two qualify as

research articles. Neither of them identified the

meaning of accountability within their respective

research frames and both promoted technological

means for accountability insurance. Differences lie

mainly on the level of technological details, with the

one on online media suggesting not just people but

also technologies are needed to be held accountable

in a general manner (Jiang and Yang, 2016) and the

other on privacy protection outlining an

accountability strategy (Zhu et al., 2016). The core of

the strategy, however, targets only the action of

malicious leaking of user information on the part of

service providers, which does not correspond to the

proposed research in that:

 The concept of PISs Controlling Organization

has a much broader coverage than service

providers;

 For a particular PISs Controlling Organization,

the entirety of its decision-making activities is

under investigation – not just the action of

malicious leaking of user information. To

protect user information from breach or leaking

is readily distinguishable from the unjustifiable

use of personal information in normal business

operations.

There are research institutions internationally

concerning PISs technologies in relation to

transparency and accountability. For example:

 United States: Brookings Institution

(technology, accountability and international

law); Data and Society (social and cultural

issues arising from data-centric and automated

technologies);

 United Kingdom: Open Data Institute (data

identification of economic evidence and

business cases combined with social and

environmental impacts);

 United Kingdom and Germany: Information

Innovation Lab (development of fundamental

rights of communities everywhere around the

world);

 Canada: The Citizen Lab (“Lifting the lid off

the Internet”; intersection of information and

communication technologies, accountability

mechanisms, and global security)

 Australia: Smart Cities Research Institute (the

grand challenges facing large, fast-growing

cities in Australia and around the world; new

methods of accountability and feedback);

 The Information Accountability Foundation

with Americas Interest Group, Asia Discussion

Group, EU GDPR Group (data protection law

and practice through accountability-based

information governance).

The EU General Data Protection Regulation

(GDPR) (Regulation (EU) 2016/679) has included

accountability principles in its personal data

protection framework. All these efforts, however,

focus typically on one aspect albeit with much depth.

As the same as what is displayed in Figure 5, the

interplay between accountability and PISs as a whole

Overview of the Balancing Pervasive Information Systems Project and a Call for Research Collaborations

411

remains currently a gap. The produced balancing

theories and mechanisms therefore aim at

 The realization of a healthy, fulfilling society,

one that cares about everyone with equality,

promotes economic prosperity with fairness,

and materializes democracy in real life events;

 The optimization of corporations’ practice of

social and ethic responsibility;

 The advancement of disciplinary,

interdisciplinary (i.e., integrating knowledge

from different disciplines) and

transdisciplinary (i.e., creating a unity of

intellectual frameworks beyond the

disciplinary perspectives) knowledge, as well

as the joint process of knowledge production;

and

 The preparation of the next generation that is

PISs accountability ready.

4 RESEARCH QUESTIONS

The following research questions are identified as key

to the identified technology accountability issue, and

they are grouped as theory-related and mechanism-

related. For the theory-related group, two sub-groups

are further developed as theory deductive type and

theory inductive type.

Theory- related: Deductive:

 To what extent can the existent accountability

theories in the field of public accountability be

instructive to the technology accountability

issue? e.g., principal-agent theory (Gailmard,

2007)? Contingency theory (Mansbridge,

2014)?.

 Given the established relationship between

information and accountability (Stewart,

1984; Parkinson, 1993; GC, 2007; Obama,

2009; United Kingdom, 2009; NAA, 2017),

what kind of application of the current

development of computational archival

science and digital records management (CAS

and DRM) are suitable for building

technology accountability solutions?

Theory- related: Inductive:

 In the context of equal social and economic

development, what the considerations or

criteria should be for the properness of balance

between accountability and PISs be

determined?

 Among the different types of balancing forces

(i.e., PISs H-Agent type of decision recipients,

in forms of individual and aggregations), what

are the relationships between them? And what

kind of relationships can facilitate a positive

impact on developing PISs balancing power?

 What should the responsibility relationships

be among all the entities in Figure 4?

 What will the PISs synergetic effect be when

compared to the impact of individual PISs

technologies on accountability?

 Will the relationship between PISs

deployment and PISs accountability remain

the same across industries or it will change? If

it will change, then what the most influential

factors for the change are?

Mechanism - related:

 How can accountability information be

identified for a particular decision? How to

decide its ownership? When should the

identification take place? At the time of

deploying PISs? At the time of receiving

accountability inquiries? Or automatically

when a decision is made?

 How can accountability information for a

particular decision be delivered? When should

the delivery take place? At the time of

receiving accountability inquiries? Or

automatically when accountability

information is identified?

 How can the answerability of accountable

information be ensured?

 How can the discharge of responsibilities be

evaluated?

 Can PISs technologies be used to automate the

responding process to accountability inquires?

FR-HT 2018 - Special Session on Managing Digital Data, Information and Records: Firm Responses to Hard Technologies

412

5 RESEARCH DESIGN

For investigating the above research questions, two

research methodologies were selected: design science

research (DSR) and grounded theory methodology

(GTM). Both methods have been promoted to be

employed in ISs (Gregor and Hevner, 2013; Wiesche

et al., 2017) and their investigating courses are

illustrated in Figures 6 and 7.

Figure 6: Design Science Research Methodology Process

Model (Peffers, et al., 2008).

Figure 7: Grounded Theory Methodology Process Model.

For the proposed research, DRS is responsible

for answering the research questions of the theory

deductive type and of the mechanism-related, and

GTM is responsible for answering the research

questions of theory inductive type. Data for both

methods include the types of published research,

which, as displayed in Figure 5, is sizable by now and

is constantly growing, and empirically collected data,

which will be done by the tools of questionnaires and

interviews. By integrating these two methods,

balancing theories and mechanism to PISs are

expected to be produced. Figure 8 displays the overall

process of the research:

Figure 8: PISs Balancing Theories and Mechanisms

Research Process.

6 A CALL FOR RESEARCH

COLLABORATIONS

The proposed research is considered leading

internationally in the following three aspects:

• Focusing on the impact of integrated PISs

instead of just that of PISs component

technologies (Figure 2);

• Findings to be instructive to the development

of standards by ISO, who is currently at the

initiating stage of developing relevant

standards (Figure 9);

• Enhancing the understanding and application

of PISs to the fields of social science and

humanities (Figure 10).

Figure 9: ISO Standards Re PISs Component Technologies

Currently Under Development.

Total Under Development Withdrawn

Overview of the Balancing Pervasive Information Systems Project and a Call for Research Collaborations

413

Figure 10: Current Situation of PISs Applications to fields

of Social Science and Humanities.

The proposal to timely investigate PISs

accountability takes into also the consideration that

many governments are investing massively in PISs

technologies. It is an international consensus that

these technologies are critical to the enhancement of

all-around national competitiveness; consequently,

the past couple of years see increasingly the

establishment of notional policies that strongly

encourage the racing for advancements of PISs

technologies. Enthusiasm, however, cannot be the

only condition for success. Comprehensive planning

including careful examination of side affects (or

trade-offs) must be part of this technology

development movement.

Accountability is a societal necessity because it

represents the force that aims to counter the

controlling power of decision-making authorities. As

such, it is widely acknowledged in both the

organizational types of government institutions and

business corporations. For government institutions,

accountability requires the provision of information

regarding the policies they make (Peters, 2014) and

for business corporations, the promotion of self-

discipline and code of conduct (Pava, 2008; Thorne,

2008). These accountabilities, however, do not

represent sufficiently digital decision accountability,

or in general, technology accountability. In a PISs

environment, digital decisions are being made

constantly and they are made by technologies that are

ubiquitous and invisible. These features and their

combined effects make it extremely difficult for

accountability information to be available and/or with

answerability. As a consequence, human decision

recipients, also the typical accountability force, will

find it extremely difficult to make inquiries about the

decision-making processes, let alone to understand

them. For example, in-memory processing offers

instant analytical results, however, re-examination of

the results in a later time will have to take much more

time because the participating data threads are moved

to permanent storage media after the in-time

processing and delivery. Re-examination may not

even be at all possible if the participating data threads

are not considered worthy being retained in permeant

storage media. The promise of context-aware

computing is to tailor information products and

services to user needs as much as possible, and with

the help of IoT and BD analytic thinking, such

promise is speedily becoming a reality. To

reconstruct the tailoring process, however, can be

time consuming or entirely impossible, depending on

the degree of customization, i.e., the combination of

the number of data threads used as input, the formats

in which these data threads existed, and the

complexity of the analytical tools utilized. It must be

pointed out that these examples describe only

challenges caused by individual use of technologies,

not the pervasiveness of computing as framed in PISs.

As stated above, in true PISs, all decisions, big or

small, are made based on ubiquitous technologies that

are invisible to the consumers of information,

meaning that even the decision makers do not know

where the information prompted in front of them

come from and how it is produced, let alone the

recipient of the decisions. These difficulties

accumulate with the increased number of digital

decisions and the level of integration between PISs H-

Agent and PISs D-Agent and are compounded with

the fact that accountability inquires typically take

place at times (much) later than that of decision

making.

If decision-making processes cannot be

sufficiently understood by decision recipients,

decisions will be impossible to be challenged, and

with the current situation where decision-making

powers are increasingly concentrated in the hands of

PISs technology companies, the needed balance

between decision makers and decision recipients will

be in peril and may eventually be lost. Decision-

making powers must be justly countered as a healthy,

harmonized society is one that is properly balanced.

With such balance comes along stability, yet, without

such balance, common interests can never be

identified, prosperity can hardly be sustained and the

danger of digital technology dictatorship looms. If we

are not ready to trust blindly digital decisions, if we

indeed fear to have to live in a “black box”

empowered society (Pasquale, 2015), and if we do

believe there are “weapons of math destruction”

(O'Neil, 2016), the interplays between PISs and

accountability must be investigated. Among the many

6451

1098

405

9054

1126

2294

4832

143

7957

259

2000

4000

6000

8000

10000

FR-HT 2018 - Special Session on Managing Digital Data, Information and Records: Firm Responses to Hard Technologies

414

examples, the Facebook scandal is telling us live:

digital decisions and the person(s) behind these

decisions can be invisible even to a PISs organization

itself and manipulations of user-generated data can be

unlimited. It is time for us ordinary people, the data

supplier, to be united, to take actions, and to make the

invisible visible. To that end, we need collaborations

from all fellow enthusiasts.

ACKNOWLEDGEMENTS

This study is supported by the Fundamental Research

Funds for Central Universities and the Research

Funds of Renmin University of China (15XNL032).

REFERENCES

Arnaboldi, M., Busco, C., and Cuganesan, S. (2017).

Accounting, accountability, social media and big data:

revolution or hype?. Accounting, Auditing and

Accountability Journal, 30 (4), 762-776.

Boos, D., Guenter, H., Grote, G., and Kinder, K. (2013).

Controllable accountabilities: the Internet of things and

its challenges for organisations. Behaviour and

Information Technology, 32 (5), 449-467.

Bovens, M. (2007). Public accountability. In E. Ferlie, L.

E. Lynn Jr., and C. Pollitt (eds.), The Oxford handbook

of public management (pp. 1-28). Oxford: Oxford

University Press.

Bovens, M. (2010) .Two concepts of accountability:

accountability as a virtue and as a mechanism. West

European Politics, 33(5), 946-967. DOI: 10.1080/

01402382.2010.486119.

Castiglione, D. (2018). Accountability. Retrieved 21

February 2018, from, https://www.britannica.com/

topic/accountability.

Davis, G. B. (1974). Management information systems:

conceptual foundations, structure, and development.

New York: McGraw-Hill.

Duranti, L. (2002). The Concept of electronic records. In L.

Duranti, T. Eastwood and H. MacNeil (eds.),

Preservation of the integrity of electronic records (pp.9-

20). Dordrecht; Boston: Kluwer Academic Publishers.

EBSCO (2018). Academic search complete. Retrieved 17

January 2018, from, http://resources.library.ubc.ca/?

searchtype=keywordsandsearch=academic+search+co

mplete.

Fox, J. (2007). The Uncertain relationship between

transparency and accountability. Development in

Practice, 17 (4-5), 663-671.

Gailmard, S. (2007). Accountability and principal–agent

theory. In E. Ferlie, L.E. Lynn Jr., and C. Pollitt (eds.),

The Oxford handbook of public management (pp. 1-

19). Oxford: Oxford University Press.

Gao, C., and Iwane, I. (2015). A Social network model for

big data privacy preserving and accountability

assurance. 2015 12th annual IEEE consumer

communications and networking conference (CCNC),

9-12 January 2015, Las Vegas.

Government of Canada (2018). Policy on information

management. Retrieved 16 January 2018, from

https://www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=12742.

Gregor, S. and Hevner, A. R. (2013). Positioning and

presenting design science research for maximum

impact. MIS Quarterly, 37 (2), 337-355.

Hood, C. (2010) Accountability and transparency: Siamese

twins, matching parts, awkward couple?, West

European Politics, 33 (5), 989-1009. DOI: 10.1080/

01402382.2010.486122.

Grunwell, D., and Sahama, T. (2015). Information

accountability and health big data analytics: a consent-

based model. 17th International conference on e-health

networking, application and services (HealthCom),

13-17 October 2015, Boston.

InterPARES Project. (2007). Diplomatic analysis.

Retrieved 20 January 2018, from http://www.inter

pares.org/display_file.cfm?doc=ip3_template_for_dipl

omatic_analysis.pdf.

Jabbra, J. G. and Dwivedi, 0. P. (1989). Public service

accountability: a comparative perspective. Hartford,

CT: Kumarian.

Jiang, X. L., and Yang, S. (2016). Ethic accountability of

media technologies in the era of big data. Journal of

Southwest University for Nationalities (Humanities and

Social Science Edition), 7, 145-149.

Khan, M. H., Chan, G.Y., and Chua, F. F. (2016). An

Adaptive monitoring framework for ensuring

accountability and quality of services in cloud

computing. 2016 International Conference on

Information Networking (ICOIN), 13-15 January 2016,

Kota Kinabalu, Malaysia.

Ko, R. K. L., Lee, B. S., and Pearson, S. (2011). Towards

achieving accountability, auditability and trust in cloud

omputing. In A. Abraham, J. L. Mauri, J. F. Buford,

J. Suzuki, and S. M. Thampi (eds), Advances in

Computing and Communications. ACC 2011.

Communications in Computer and Information

Science, vol 193. Berlin, Heidelberg: Springer.

https://doi.org/10.1007/978-3-642-22726-4_45

Kourouthanassis, P. E., Giaglis, G. M., and Karaiskos, D.

C. (2010). Delineating ‘pervasiveness’ in pervasive

information systems: a taxonomical framework and

design implications. Journal of Information

Technology 25, 273–287.

Library of Congress (2018). Library of Congress Subject

Headings. Retrieved 23 January 2018, from

http://id.loc.gov/authorities/subjects.html.

Lindberg, S. (2009). Accountability: the core concept and

its subtypes. Retrieved 20 February 2018, from,

http://www.gsdrc.org/document-library/accountability-

the-core-concept-and-its-subtypes/.

Mansbridge, J. (2007). A Contingency theory of

accountability. In E. Ferlie, L.E. Lynn Jr., and C. Pollitt

Overview of the Balancing Pervasive Information Systems Project and a Call for Research Collaborations

415

(eds.), The Oxford handbook of public management

(pp. 1-17). Oxford: Oxford University Press.

Mulgan, R. (2000). Accountability: an ever-expanding

concept?. Public Administration, 78 (3), 555–573.

National Archives of Australia (2017). Information

governance framework. Retrieved 14 January 2018,

from http://www.naa.gov.au/about-us/organisation/

accountability/information-governance-framework/in

dex.aspx.

Obama, B. (2009). Transparency and open government.

Retrieved 15 January 2018, from https://web.archive.

org/web/20160229003506/https://www.whitehouse.go

v/the_press_office/TransparencyandOpenGovernment.

O'Neil, C. (2016). Weapons of math destruction: how big

data increases inequality and threatens democracy. New

York: Crown.

Parkinson, J. Accountability in Archival Science. MAS

Thesis. University of British Columbia. https://open.

library.ubc.ca/cIRcle/collections/ubctheses/831/items/

1.0086151

Pasquale, F. (2015). The Black box society: the secret

algorithms that control money and information.

Cambridge: Harvard University Press.

Pava, M. L. (2008). Corporate accountability. In R.W. Kolb

(ed.), Encyclopedia of business ethics and society (pp.

1-7). Thousand Oaks: SAGE Publications.

Peffers, K., Tuunanen, T., Rothenberger, M. A., and

Chatterjee, S. (2008). A Design science research

methodology for information systems research. Journal

of Management Information Systems, 24 (3), 45–77.

6, Perri. (2010). Ethics, regulation and the new artificial

intelligence, part I: accountability and power.

Information, Communication and Society, 4 (2), 199-

229. http://www.tandfonline.com/doi/abs/10.1080/713

768525.

Peters, B. G. (2014). Accountability in public

administration. In M. Bovens, R. E. Goodin, and T.

Schillemans (eds.), The Oxford handbook of public

accountability, Oxford: Oxford University Press.

Stewart, J. D. (1984). The Role of information in public

accountability. In A. G Hopwood, and C. Tomkins

(eds.) Issues in public sector accounting (pp. 13-34).

Oxford: P. Allan.

Thompson, D. F. (2014). Responsibility for failures of

government: the problem of many hands. American

Review of Public Administration, 44 (3), 259–273.

Thorne, D. M. (2008). Social accountability. In R.W. Kolb

(ed.), Encyclopedia of business ethics and society (pp.

1-4). Thousand Oaks: SAGE Publications.

United Kingdom (2009). Lord Chancellor’s code of

practice on the management of records issued under

section 46 of the Freedom of Information Act 2000.

Retrieved 18 January 2018, from http://www.national

archives.gov.uk/documents/foi-section-46-code-of-

practice.pdf.

Vedder, A., and Naudts, L. (2017). Accountability for the

use of algorithms in a big data environment.

International Review of Law, Computers and

Technology, 31 (2), 206-224.

Weber, R. H. (2011). Accountability in the Internet of

Things. Computer Law and Security Review, 41, 133-

138.

Weiser, M. (1991). The Computer for the 21st Century.

Scientific American, 265 (3, Special issue), 94-105.

Wiesche, M., Jurisch, M. C., Yetton, Y. W., and Krcmar,

H. (2017). Grounded theory methodology in

information systems research. MIS Quarterly, 41 (3),

685-701.

Xhafa, F. et al. (2015). Privacy-aware attribute-based PHR

sharing with user accountability in cloud computing.

Journal of Supercomputing, 71, 1607–1619.

Xie, S. L. (2012). The Concept of record and its revolution

in digital environments: the InterPARES perspective.

The Journal of Archival Science, 3, 46-51.

InterPARES Project. (2012). Terminology database.

Retrieved 20 January 2018, from http://interpares.org/

ip2/ip2_terminology_db.cfm.

Xie, S. L. (2012). Digital records management: the lacking

foundation for continuing accessibility. Online

Information Review, 36 (2), 299-306.

Xie, S. L. (2015). Co-design of information systems with

digital records management: a proposal for research. In

A. L. N. Fred, J. L. G. Dietz, D. Aveiro, K. Liu, and J.

Filipe (eds.), Proceedings of the 7th international joint

conference on knowledge discovery, knowledge

engineering and knowledge management, volume 3:

KMIS (knowledge management and information

Sharing) (pp. 222-238).

Xie, S. L. (2016). Retention in ‘the right to be forgotten’

scenario: a records management examination. Records

Management Journal, 26 (3), 279 – 292.

Xie, S. L., and Fan, A. (2016). Organizational records

systems - an alternative view to (enterprise) information

systems. In A. Fred, J. Dietz, D. Aveiro, K. Liu, J.

Bernardino, and J. Filipe (eds.), Proceedings of the 8th

international joint conference on knowledge discovery,

knowledge engineering and knowledge management,

Volume 3: KMIS (pp. 82-91).

Xie, S. L (2017). Records and information in the

government of Canada: a Grounded Theory study.

Hangzhou, China: Zhejiang University Press.

Zhu, G., Yang, J. Y., Feng, M. N., and Chen Y. (2016).

Research on big data privacy traceability framework in

the context of accountability. Research on Library

Science, 13, 67-73.

FR-HT 2018 - Special Session on Managing Digital Data, Information and Records: Firm Responses to Hard Technologies

416