Digital Social Matching Ecosystem for Knowledge Work

Jukka Huhtam

aki

and Thomas Olsson

Tampere University of Technology, Korkeakoulunkatu 8, Tampere, Finland

University of Tampere, Kanslerinrinne 1, Tampere, Finland

Keywords:

Social Matching, Digital Ecosystem, Knowledge Management, Human Resources, Collaboration, Decision-

Support Systems.

Abstract:

Knowledge work involves various so-called social matching decisions: who to recruit, who to pair up or team

up, who to ask for consultancy, etc. Despite the scale of effects such decisions can have on organizations,

social matching activities are little supported by technology. In this position paper, we describe an ongoing

venture to develop the enablers and a shared vision for forming digital ecosystems around social matching of

knowledge workers. Rather than developing monolithic, organization-speciﬁc systems, we argue for an API-

based ecosystemic approach that helps co-create value and develop more networked, innovative, and viable

business ventures. We elaborate our vision and work-in-progress by presenting requirements for and scenarios

of digital ecosystems for social matching in knowledge work.

1 INTRODUCTION

This position paper stems from a research venture be-

tween two university research groups and a consor-

tium of companies to develop the enablers and shared

vision for forming digital ecosystems around social

matching of knowledge workers.

What is social matching of knowledge workers?

The general notion of social matching refers to com-

putational ways of identifying and facilitating new so-

cial connections between people (Terveen and McDo-

nald, 2005). Our focus is on social matching in pro-

fessional life, particularly in creative and knowledge

work. Relevant activities include recruitment of new

personnel to knowledge-intensive organizations, team

formation within or across organizations for various

types and lengths of projects, seeking for mentors or

advisers as an individual or an organization – basi-

cally, establishing any kind of collaboration relations-

hips related to knowledge work. Today, social mat-

ching activities are labour-intensive and based on hu-

man judgment. Yet, the decisions have signiﬁcant

impact on the performance of organizations and the

wellbeing of individuals (Rogers and Blenko, 2006).

This makes professional matching decisions prone to

human error, and the risk of making an unsuccess-

ful choice is of high probability and high impact. We

consider this as a fruitful opportunity to envision new

forms of digital decision support systems.

What is an ecosystem, then? The key premise in

business ecosystems is that in order to be competitive,

companies must allow other companies to create ad-

ditional value to their own offering. James F. Moore

(1996) kicked off ecosystem discussion with his se-

minal article Predators and Prey: A New Ecology of

Competition in which he states that companies should

view themselves as part of a

“... business ecosystem that crosses a variety

of industries. In a business ecosystem, com-

panies coevolve capabilities around a new in-

novation: they work cooperatively and com-

petitively to support new products, satisfy cu-

stomer needs, and eventually incorporate the

next round of innovations.”

In some 20 years, academic research has mo-

ved further to deﬁne several complementing types

of ecosystems, including knowledge, innovation, and

business ecosystems (Valkokari, 2015). In know-

ledge ecosystems, companies and research organi-

zations come together to create new knowledge in

pre-competitive phase of research and development

arvi et al., 2018). Innovation ecosystems are inter-

connected, interdependent compositions of startups,

founders, investors, enterprises, universities, public

organizations that together drive the emergence of

new companies, products, and services (Russell et al.,

2011). Business ecosystems are composed of inter-

dependent companies co-creating value to their cus-

194

Huhtamäki, J. and Olsson, T.

Digital Social Matching Ecosystem for Knowledge Work.

DOI: 10.5220/0006950301940199

In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 3: KMIS, pages 194-199

ISBN: 978-989-758-330-8

tomers (Iansiti and Levien, 2004). Although the use

of ecosystem as concept in business and strategy lite-

rature remains a controversial topic (Oh et al., 2016),

scholars are actively investigating this form of colla-

borative value creation (Ritala and Almpanopoulou,

2017; Russell and Smorodinskaya, 2018).

What makes an ecosystem digital? Digital as-

sets are increasingly important in enabling and fa-

cilitating the emergence and success of ecosystems.

API ecosystems are a recent example of enabling co-

creation between companies through digital interfaces

that help exchange data and simple services (Evans

and Basole, 2016). Although Application Program-

ming Interface (API) is a core concept in modular

software development, in this context it refers speci-

ﬁcally to Web APIs, that is, application programming

that are available online for developers to use. Web

APIs have existed since the early days of the World

Wide Web and they had a core role for example in

the Web 2.0 vision (O’Reilly, 2007). However, only

recently technical and business developers have joi-

ned to explore this place of digital value creation they

both ﬁnd themselves in (cf., Evans and Basole, 2016).

Digital ecosystem researcher Rahul Basole recaps:

“Businesses must both own and participate in

ecosystems. APIs make that happen. #digital

#ecosystem”

Our research venture has two key objectives. First,

we seek to develop data-driven, interactive service

concepts for professional social matching and rela-

ted methodology. Second, we aim to explore ways to

implement some of these service concepts at ecosy-

stem level, that is, in co-creation between companies

rather than within the corporate ﬁrewall. This pa-

per describes our work-in-progress analysis of what

type of ecosystems could be feasible from technology

perspective and desirable from the perspectives of or-

ganization studies and social psychology, particularly

in the relatively understudied area of social matching

in knowledge work. We argue for the opportunities

ecosystem thinking can bring in creating new digi-

tal services that facilitate professional collaboration.

Ecosystem building starts from shared vision (J

arvi

et al., 2018; Russell et al., 2011) and our shared vision

for ecosystem thinking starts with the API ecosystem

approach. That is, instead of designing a monolithic

platform-based ecosystem, our objective is to iden-

tify potential API-based collaborations on social ma-

tching, either among project consortium members or

between consortium members and third parties.

https://twitter.com/basole/status/1001477372460371969

2 NEED FOR DIGITAL SUPPORT

IN SOCIAL MATCHING

Why the social matching activities in knowledge work

require digital support? Choosing potential matches

are traditionally manual tasks performed by busy ma-

nagers or Human Resource professionals (e.g., mat-

ching an employer with a suitable employee). Ho-

wever, it is well known that decision-making is in-

herently limited by the human capacity of informa-

tion processing—based on intuition, heuristics, and

cognitive shortcuts (Kahneman and Tversky, 1973),

and striving for minimizing cognitive effort (Fiske

and Taylor, 1991). Human decision-making can re-

sult in tendencies like homophily, the preference of

interacting with like-minded others (McPherson et al.,

2001), and leaning on existing social networks and a

geographically limited pool of candidates. For exam-

ple, forming working groups in organizations often

display arbitrary and ill-justiﬁed choices even though

the combination of people can signiﬁcantly inﬂuence

the productivity of the group. This calls for digi-

tal support that can help considering options beyond

the obvious (cf., Gal et al., 2017) and identify unex-

pected, yet meaningful social ties between actors.

Why should we care about ecosystems in the con-

text of professional social matching? People are in-

herently interconnected with various other individu-

als and organizations, also outside their professional

role. This means that the consideration of certain so-

cial ties should not limit to a particular matching acti-

vity; for example, a “good match” for a new headhun-

ted recruit could turn into a relevant mentor or mentee

in another context. Similarly, the needs for enhanced

collaboration do not limit to one’s primary professio-

nal role but relate also to secondary and tertiary roles,

not to mention collaboration in the third sector, hob-

bies, and others.

Importantly, if we take a textbook approach to im-

plement computational social matching, we end up

developing a people recommender system that seeks

to maximize there relevance of the recommendation.

Actor similarity, the number of shared existing con-

nections, and triadic formation of new connections are

the key predictors of a connection. Therefore, the in-

troduction of social recommender will only boost the

formation of the traditional connections. On the other

hand, knowledge work is fueled by complementary

information, viewpoints and skills; this calls for sys-

tems that help increase epistemic diversity and unex-

pected social ties in and between organizations.

Digital Social Matching Ecosystem for Knowledge Work

195

3 STARTING POINTS FOR

SOCIAL MATCHING

ECOSYSTEMS

3.1 Social Matching Analytics

Our approach to social matching is computational and

data-driven. Speciﬁcally, we explore the use of Big

Social Data (BSD) in identifying potential connecti-

ons between actors. Olshannikova et al. (2017) deﬁne

BSD as

“any high-volume, high-velocity, high-variety

and/or highly semantic data that is generated

from technology-mediated social interactions

and actions in digital realm, and which can be

collected and analyzed to model social inte-

ractions and behavior.”

Three types of BSD is available on individual ac-

tors. First, data on digital relationships that repre-

sent actors’ social network. Second, transactional

data on the interactions between actors. Third, actor-

produced content, including their self-representations

and discussions.

Social matching starts from the creation of net-

work representation of the existing social connections

between actors. Various data processing methods are

used to extract features that represent the knowledge,

competences, and interests of individual actors in al-

gorithms. Once the network is composed and actors

have their representation in algorithms, several alter-

native analytical option are available. Many of these

approaches are based on pairwise analytics of the ac-

tors, including measuring their social distance and the

similarity of their interests or produced content (Tsai

and Brusilovsky, 2018).

Importantly, relevance-ﬁrst approach is not advi-

sed in social matching analytics. Instead, social mat-

ching should seek ways to nudge actors to diversity-

seeking behavior. Thaler and Sunstein (2008) deﬁne

nudging as means to “alter people’s behavior in a pre-

dictable way without forbidding any options or sig-

niﬁcantly changing their economic incentives.” Me-

ans to implement nudging effects in social matching

systems include the transparency, controllability, and

explainability (Tintarev and Masthoff, 2015; Tsai and

Brusilovsky, 2018). At the same time, we want to note

that in general, social matching is an act of optimi-

zing for the diversity-bandwidth trade-off (Aral and

Van Alstyne, 2011). That is, both strong ties with high

bandwidth and weak ties as sources of potential novel

information are valuable to users.

3.2 API-based vs. Platform-based

Ecosystems

Two basic architectures exist for digital ecosys-

tems, that is, platform-based and API-based. In the

platform-based approach, a keystone company opera-

tes a platform and provides the other companies me-

ans to develop complementary products. Apps built

on mobile platforms is a prime example of a comple-

mentary product. On the other hand, API ecosystems

are composed of dyadic service combinations. In API

ecosystems, companies co-create value by providing

Web APIs to each others consumption.

APIs are an important example of boundary re-

sources. Formally, boundary resources are “the soft-

ware tools and regulations that serve as the interface

for the arm’s length relationship between the plat-

form owner and the application developer” (Ghazaw-

neh and Henfridsson, 2013). Compared to platform-

based ecosystems, API ecosystems are nimble and

ﬂexible. New combinations are easy to form and re-

move. API ecosystems are loosely connected com-

pared to platform-based ecosystems. Twitter, for ex-

ample, provides an API for developers to access data,

including tweets and user proﬁles. Moreover, IBM

Watson can be used through APIs.

Two basic types of APIs exist, data APIs and

functional APIs. Data APIs have dominated in the

early years of API development. However, functional

APIs present an approach that does not insist a com-

pany to release their data to other actors but instead

create value by implementing their own data products.

In our venture, the quest to develop ecosyste-

mic social matching functionalities and services starts

from an API-based approach. This is because we do

not have a clear candidate to serve in the role of keys-

tone that operates a platform. From technical view-

point, social matching comes with a versatile set of

use cases on collecting, cleaning, reﬁning, modeling,

and analyzing data. Due to this versatility, it is in

practice impossible to implement a one-size ﬁts all

technical solution.

In order to manage the technical diversity, we take

a component-based approach (Nyk

anen et al., 2007)

to implement data-processing pipelines for social ma-

tching. This approach is an intuitive extension of

our previous work on data-driven visual analytics that

manifests as the Ostinato Process Model (Huhtam

aki

et al., 2015).

From a technical viewpoint, it is relatively straig-

htforward to assign individual components to be run

as services that different companies provision to each

other. That is, reaching technical modularity is achie-

vable simply by following API design principles. Ho-

KMIS 2018 - 10th International Conference on Knowledge Management and Information Sharing

196

wever, potential issues related to organizational mo-

dularity truly hinder the design of ecosystems. Once

companies enter an ecosystemic collaboration, by de-

ﬁnition they become interdependent to each other

both at technical and organizational level (Ghazaw-

neh and Henfridsson, 2011; Yoo et al., 2010). Data is

a core asset to companies and therefore they are un-

derstandably reluctant to share the data to others.

3.3 Legal Constraints

The newly launched General Data Protection Regu-

lation (GDPR) is truly a gamebreaker in Professio-

nal Social Matching. From BSD mining viewpoint,

GDPR adds major restrictions. Data on individual ac-

tors can only be collected for a dedicated purpose and

with informed consent. Moreover, acts of data-driven

proﬁling must be reported to the actors that the data

and resulting proﬁles describe.

It seems that platform-based services such as Lin-

kedIn and Duunitori have an advantage in the GDPR

age. These platforms are able to collect informed con-

sent from users to a) proﬁle them and b) make auto-

mated decisions (e.g., position recommendations for

candidates and candidate recommendations for com-

panies). Using harvested data does not seem to be an

option at all.

4 SCENARIOS OF API-ENABLED

MATCHING ECOSYSTEMS

FOR KNOWLEDGE WORK

To make the intersection of social matching, know-

ledge work, and ecosystems more tangible, the fol-

lowing presents two scenarios of desirable futures.

These shed light on our empirical work in progress

and highlight the potential ecosystemic beneﬁts and

different types of API provider and customer roles in

different realms of knowledge work.

4.1 Global Innovation Platform

A global innovation platform operates by taking pro-

blem statements (case projects) from companies and

combining university student teams to work on the

tasks for an intensive period of problem based lear-

ning. The innovation platform has staff to facilitate

the projects, and each project team is able to exchange

views with university-based topic expert. The pro-

jects produce different beneﬁts for different stakehol-

ders: important experience and connections for stu-

dents, new learning platforms for universities, fresh

ideas for the companies, and atmosphere of open in-

novation to the local community. This is an oppor-

tune ground for ecosystemic social matching scena-

rios. But how to identify which students form an

effective team together? How to identify appropri-

ate expert advisers for each project? How to identify

which possible project topics are most suitable for this

kind of innovation projects?

Data-driven team formation refers to a data-driven

approach to compose and structure teams for new

projects (cf., Zhou et al., 2018). Services for mana-

ging the workforce of an organization (here, a pool

of available students) would provide a workable star-

ting point for this scenario. With the growing trend to

create digital portfolios and professional proﬁles on-

line, these data about students could be analyzed for

team formation, along with the data the university has

about them. While these data can be privacy sensi-

tive and indeed have multiple origins, the innovation

platform would need an access only to an API that

provides a list of key qualities that each student has

or would like to learn. Ideally, the qualities would

include not only skill and knowledge areas but also

information about their general cognitive styles and

suitable roles in team-based innovation work.

Second, universities are increasingly using Cur-

rent Research Information Systems (CRIS) to help

maintain researcher portfolios and gather data about

academic output, such as publications, talks, awards

and intellectual property. This portfolio data – inten-

ded to be public anyway – could be sourced to identify

key interests and competences for each researcher and

teacher in order to create features representing them

in a matchmaking algorithm. These features would be

matched with the project descriptions and the student

groups. This insists that the university provides an

API to the CRIS data. Interestingly, such API provi-

sion is well in line with open science and open access

ideals but in stark contrast with GDPR.

Third, the innovation platform can accumulate

data about past successful innovation projects and

their characteristics. Using for example machine lear-

ning, we might identify what types of organizations,

which project topics or what kind of customer invol-

vement have yielded best results for the customer or-

ganization, for the student group, or for the society

as a whole. This insight can be used to headhunt for

suitable project from the local businesses and to se-

lect the most suitable teams for each case. As such

data does not yet exist, it is crucial to deﬁne suitable

measurements and practices of gathering relevant data

systematically about each project.

Digital Social Matching Ecosystem for Knowledge Work

197

4.2 Matching as a Service for

Leadership

Matching as a Service (MaaS) is envisioned as a new

initiative to provide an organization and its employees

with digital support for various internal social mat-

ching cases. Particularly leadership and managerial

activities, such as individuals’ career development as-

sistance, activity partnering for task forces and leisu-

rely activities, ﬁnding a mentor, and ad hoc team for-

mation for various short-term projects would be fruit-

ful cases that are currently missing proper digital sup-

port. At the same time, in knowledge-intensive orga-

nizations and professions, people possess latent, un-

tapped potential and tacit knowledge: many epistemic

problems could be solved with the help of peers rather

than by utilizing the conventional chain of command

through the hierarchy. But how to efﬁciently iden-

tify what latent skills and knowledge different people

have and who might need them?

The vision comes with an online platform that the

employees use to state needs and requirements for lea-

dership services, of which many fall under social ma-

tching. In order for this scenario to become ecosys-

temic, platform developer must provide boundary re-

sources to allow third-party development of comple-

mentary services to the platform. The ecosystemic

perspective could mean, for example, offering the

identiﬁed skills or knowledge outside the organization

to customers and partners. Partnering organizations

could offer API-enabled leadership services to each

other, especially in smaller companies without an es-

tablished HR department. Alternatively, ecosystemic

thinking could be advocated in smaller scale within

the organization: giving more room for grass-root ini-

tiatives and for example internal startups. Particularly

in large enterprises the organizational rigidity and si-

los call for enhanced interplay between actors in dif-

ferent parts of the organization.

5 CONCLUDING REMARKS

In this position paper, we described our vision of the

building blocks of digital social matching ecosystems

for knowledge work. We argued for the need for

social matching within and in-between organizations

and point to Web APIs, a key category of boundary

resources, as digital means to implement ecosystemic

co-creation relationships between organizations. We

hope the vision encourages further transdisciplinary

investigation of the practicality and real-life desira-

bility of digital social matching in general and the

ecosystemic approach in particular.

The current shift in legislation toward increased

privacy and right to be forgotten and therefore limited

data access (GDPR), as well as increased user cont-

rol and machine readability (MyData) must be consi-

dered when planning ecosystemic digital social mat-

ching concepts. Need for informed consent from each

individual separately seems to effectively prevent mi-

ning big social data en masse. That is, the existence

of a social supercollider (Watts, 2013) for social ma-

tching seems unlike at this stage. This implies that

global platforms for digital work posses a major ad-

vantage in developing analytical capability for social

matching.

Future research should look into what are suitable

and ethically sustainable design goals for social ma-

tching particularly in knowledge work; what kind of

business models best serve API-based ecosystems in

this domain; and how to enable gathering and analy-

zing relevant data in the current legislative landscape.

Ecosystemic service concepts for social matching that

cross the boundaries of individual organizations are

yet to be developed. We will continue our venture

to develop some of these concepts. We call for the

exchange of viewpoints on privacy-opportunity trade-

off for ecosystemic digital social matching.

ACKNOWLEDGEMENTS

This work was conducted under Business Finland

project Big Match (3166/31/2017 and 3074/31/2017).

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