522
S. da Silva Neiva et al.
General Assembly Resolution 70/1, “Transforming our world: the 2030 Agenda”,
offers a broad framework of interdependent organizational goals covering issues of
social and economic development (UN General Assembly 2015). Thus, sustainability
science has emerged as a fresh line of academic research to create new knowledge
in tandem with the advancing policies and practices of sustainability. Consequently,
a growing body of work is now available in all disciplines which investigates how
sustainable development can be progressed. However, it is important to note that
issues such as water, energy, health, agriculture and biodiversity (i.e., the so-called
WEHAB targets) are not fully described by any single individual discipline but,
instead, require an inter-disciplinary approach.
Much of the literature on the subject is produced by higher education institutions
(HEIs) through non-profit making research but funding agencies still seek justification and an eventual market value that reflects the value of investment for people and
knowledge; Both are difficult to measure in a traditional sense of financial management. A sustainability assessment of the higher education sector should in some way
reflect the contribution that HEIs make to the implementation of SDGs. In turn, it
is recognized that applying such a processes aids in the integration of good sustainability practices into their organizational structure, helps their decision-making and
external communications with stakeholders (Hopwood et al. 2010). Accounting for
sustainability in terms of physical data and presenting this information in a clear
and meaningful way goes some way to addressing the major challenges and risks
posed by sustainability-related issues. But, deciding what key performance indicators (KPIs) should be gathered, measured, analyzed and reported on requires careful
consideration. A structured approach is necessary in order to assess their significance
to SDGs, the implications for individual HEIs and the higher education sector as a
whole.
There are many KPIs in the higher education sector that could be looked at in terms
of sustainability. The work presented in this article examines how an accounting for
sustainable analysis process could be applied to the publication output appearing in
the well-known International Journal of Sustainability in Higher Education (IJSHE).
The process investigates the frequency of terms appearing in these publications and
their significance to the implementation of sustainability in the operational management, research and teaching activities in HEIs. Accounting for sustainability in the
higher education sector in this way offers a readily understandable performance
metric and potential influencer on local communities and wider society in general.
This investigation is achieved through a data mining method based on a semi automatic approach for the recognition, correlation and extraction of named entities and
relations from textual documents as reported by Ceci et al. (2012) which are used to
create and classify Web-based data.
S. da Silva Neiva et al.
General Assembly Resolution 70/1, “Transforming our world: the 2030 Agenda”,
offers a broad framework of interdependent organizational goals covering issues of
social and economic development (UN General Assembly 2015). Thus, sustainability
science has emerged as a fresh line of academic research to create new knowledge
in tandem with the advancing policies and practices of sustainability. Consequently,
a growing body of work is now available in all disciplines which investigates how
sustainable development can be progressed. However, it is important to note that
issues such as water, energy, health, agriculture and biodiversity (i.e., the so-called
WEHAB targets) are not fully described by any single individual discipline but,
instead, require an inter-disciplinary approach.
Much of the literature on the subject is produced by higher education institutions
(HEIs) through non-profit making research but funding agencies still seek justification and an eventual market value that reflects the value of investment for people and
knowledge; Both are difficult to measure in a traditional sense of financial management. A sustainability assessment of the higher education sector should in some way
reflect the contribution that HEIs make to the implementation of SDGs. In turn, it
is recognized that applying such a processes aids in the integration of good sustainability practices into their organizational structure, helps their decision-making and
external communications with stakeholders (Hopwood et al. 2010). Accounting for
sustainability in terms of physical data and presenting this information in a clear
and meaningful way goes some way to addressing the major challenges and risks
posed by sustainability-related issues. But, deciding what key performance indicators (KPIs) should be gathered, measured, analyzed and reported on requires careful
consideration. A structured approach is necessary in order to assess their significance
to SDGs, the implications for individual HEIs and the higher education sector as a
whole.
There are many KPIs in the higher education sector that could be looked at in terms
of sustainability. The work presented in this article examines how an accounting for
sustainable analysis process could be applied to the publication output appearing in
the well-known International Journal of Sustainability in Higher Education (IJSHE).
The process investigates the frequency of terms appearing in these publications and
their significance to the implementation of sustainability in the operational management, research and teaching activities in HEIs. Accounting for sustainability in the
higher education sector in this way offers a readily understandable performance
metric and potential influencer on local communities and wider society in general.
This investigation is achieved through a data mining method based on a semi automatic approach for the recognition, correlation and extraction of named entities and
relations from textual documents as reported by Ceci et al. (2012) which are used to
create and classify Web-based data.