applied research journal articles

The relationship between basic and applied research in universities

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• Published: 25 March 2015
• Volume 70 , pages 689–709, ( 2015 )

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• Peter James Bentley 1 ,
• Magnus Gulbrandsen 2 &
• Svein Kyvik 3  

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What is the central research activity in modern universities? This paper uses a comprehensive survey among individuals from 15 countries to map differences in orientation towards basic/fundamental research, applied/practical research and a combination of the two. Despite some claims in the literature that basic research is no longer a preoccupation of universities, our findings point at a continued strong presence of basic research in universities but with large variations between countries and academic disciplines. At the individual level, most academics engage in a combination of basic and applied research, rather than specialising, with applied orientations generally more common. Academics specialising in basic research tend to receive less external funding, work in environments where applied research is less emphasised and hold weaker professional obligations to apply their knowledge to problems in society.

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Introduction

The higher education literature is bursting with examples of how universities are changing in response to societal transformations and new demands. Many of these changes are described in rather pessimistic terms because they imply reduced funding, different activities and numerous tensions and challenges (Enders 2013 ). Only by protecting their so-called academic heartland can universities retain a degree of autonomy and a connection with widely shared values among their employees (Clark 1998 ). Long-term scientific work is at the centre of this heartland.

Basic research, or research undertaken with a primary purpose of the advancement of knowledge for its own sake, has traditionally been fundamental to university missions and their public funding. The “linear model” of innovation, popular during the post-1945 era, saw basic research as the primary contributor to innovation and economic growth, with universities as the institutional locus for public funding (Mowery and Sampat 2005 ). However, the centrality of basic research has come under question. The Mode 2 thesis, first outlined in The New Production of Knowledge by Gibbons et al. ( 1994 ), argued that disinterested, academic-oriented, disciplinary and autonomous research conducted exclusively within universities (Mode 1) is no longer the core mode of knowledge production. Mode 2 knowledge, produced within the context of application, has become the dominant form. Revisiting the Mode 2 discourse, Nowotny et al. ( 2003 ) summarised: “… the research that is variously described as ‘pure’, ‘blue-skies’, fundamental, or disinterested, is now a minority preoccupation—even in universities” (p. 184).

The Mode 2 diagnosis is broader than just the decline of fundamental research, and it also includes shifts towards collaborative and transdisciplinary research, greater heterogeneity in the sites of knowledge production, deeper social accountability and broader forms of quality control (beyond discipline-based peer review). The exceptionally wide scope of the Mode 2 discourse has been criticised for its lack of coherence and empirical validity (Hessels and Van Lente 2008 ). To date, there has been little evidence to support or refute Nowotny et al. ( 2003 ) claims, yet the Mode 2 discourse has influenced higher education and research policy makers and has been cited in almost ten thousand documents, with the vast majority of scientific papers treating it as an accepted or background concept (Hessels and Van Lente 2008 ).

Mode 2 is not necessarily new or unique, and it competes with other popular characterisations of the changing research agenda, some of which predate it. In the 1980s, Irvine and Martin ( 1984 ) identified the emergence of “strategic research”, combining academic excellence with an orientation towards future practical utility. According to Rip ( 2004 ), strategic research is the basic way of carrying out research in the regime of “strategic science”. However, strategic research retains a linearity between basic research and practical utility, including basic research as a cornerstone of its definition: “Strategic research [is] basic research carried out with the expectation that it will produce a broad base of knowledge likely to form the background to the solution of recognized current or future practical problems” (Irvine and Martin 1984 ). A similar conceptualisation, “translational research”, shares many of these characteristics, focussing on the harnessing of basic research for practical uses, particularly within the health sciences (Woolf 2008 ). Other conceptualisations, such as “finalised science” and “post-normal science”, also emphasise strong practice orientation, but these concepts are much more nuanced in their interpretation, limiting their implications to certain disciplines or policy-relevant fields (Weingart 1997 ).

More recently, the triple helix model of university–industry–government relations has also had considerable impact on research policy (Etzkowitz and Leydesdorff 2000 ). Like Mode 2, it contends that the future legitimation of science depends upon application orientation and contribution to economic development. However, it distinguishes itself from Mode 2 because universities retain distinctiveness as the core knowledge institutions and the application orientation process is emergent, rather than complete (Etzkowitz and Leydesdorff 2000 ). Academic capitalism also stresses the growing engagement of academics in application-oriented and commercially funded research, but the extent of such engagement is limited based on the closeness of one’s discipline to the market (Slaughter and Leslie 1997 ). Overall, the Mode 2 claims regarding the shifting research agenda away from fundamental research is unique only in its comprehensiveness, particularly with reference to country and disciplinary context.

This study is in particular interested in the claim that universities are no longer preoccupied with basic or theoretical research. Our decision to limit ourselves to this component follows Hessels and van Lente’s ( 2008 ) recommendation that Mode 2’s impact necessitates empirical research on each of the constitutive parts separately. We do so by drawing on data from an international survey among academic staff in research universities in 15 countries and across 10 academic fields. The lack of international comparable indicators has been a crucial limitation for understanding how universities operate in national innovation systems (Mowery and Sampat 2005 , p. 234). The comparative dimension allows us to test whether the assumptions surrounding Mode 2 apply to different contexts. Firstly, we investigate the extent to which individual academics worldwide consider basic or theoretical research a core part of their current research. Secondly, we examine the extent to which academics specialise only in basic or applied research, or combine these activities. Thirdly, we investigate variations in the extent to which academics specialise only in basic or applied research. Why might some academics within certain countries and academic fields choose to engage in both types of research, while others choose not to? We investigate three possible explanations. We expect engagement in basic and applied research to relate to: access to external commercial research funding; institutional expectations/policies; and individual normative/social behaviour.

Question 1: To what extent is basic research still a defining characteristic of universities across the world?

Nowotny et al. ( 2003 ) are clear regarding three generalisable causes for the decline in fundamental research: increased steering of research priorities; increased commercialisation of research (resulting from decreased public funding and increased attention to intellectual property ownership); and broader accountability of science.

The limited evidence of how individual academics view their primary research points to the contrary that basic research continues to be sustained as a major activity. A large-scale Finnish investigation found that an “academic orientation” with strong emphasis on basic research remains central across disciplinary fields (Ylijoki et al. 2011 ). The authors question the thesis that a significant transformation is taking place in universities, arguing instead that academics find different ways to combine an academic orientation with various forms of societal engagement. A UK interview study found the same, although it also emphasised how the term “basic research” is flexible and may be adapted to varying circumstances (Calvert 2006 ). Gulbrandsen and Kyvik ( 2010 ) also found that 90 % of Norwegian academics undertook basic research, with slightly less than half declaring their research “very much” basic (maximum value on a five-point scale), confirming other findings that basic research remains a strong identity marker for university staff (Gulbrandsen and Langfeldt 2004 ). These results are indicative of the resilience of basic research within universities, but may not be generalisable to other countries. Public funding of university research retains a strong position in Norway (Kyvik 2007 ).

National context is potentially of greater importance to understanding changes in knowledge production than implied by the Mode 2 thesis. Even if one acknowledges that universities across the world have experienced increased steering of research priorities and accountability pressures, it is unlikely that the effects would be uniform across countries. Shinn ( 2002 ) argues there is little evidence that science has become de-nationalised and it is inappropriate to generalise broad changes across national settings. According to Shinn, despite globalisation trends, universities, business and government still function predominantly within the national settings. The interaction between these institutions has implications for university governance and research behaviours. European countries have stronger traditions of academic self-determination, whereas market coordination has stronger traditions in most English-speaking countries and state control in parts of Asia (Clark 1983 ). Even within regions of similar academic traditions, convergences in higher education policies have not diluted the national character of academic labour markets and their internal regulation of research (Musselin 2005 ). This makes a data set from different countries particularly valuable.

National governments remain the main funder of university research in almost all OECD countries, but this varies from over 80 % of total funding of R&D in higher education institutions in Argentina, Australia, Italy, Finland, Germany, Norway and the Netherlands to closer to two-thirds in the USA, UK and Canada, and just over half of all funding in China (OECD 2013 ). Countries also differ in how this public funding is allocated to universities for research. According to Auranen and Nieminen ( 2010 ), core public research funding is determined predominantly via input-orientated measures in many European countries (e.g. Norway, Finland, Netherlands and Germany), whereas output-oriented funding models are more dominant in Australia and the UK. Although many European countries have been moving towards output-oriented funding models, there has been stronger resistance from universities and their implementation has been weaker. This likely reflects the traditionally stronger academic oligarchy within these countries (Clark 1983 ).

The implications for engagement in basic research is not directly clear, but one would reasonably assume that country-level differences in how universities are governed and funded would lead to greater diversity in research behaviours. For example, systems with greater levels of core funding based on input measures have higher levels of stability compared to output-oriented and externally funded systems (Auranen and Nieminen 2010 ). By implication, risky, fundamental and disinterested basic research may be more likely to flourish in the countries with more predictable funding. Although we cannot test empirically why country-level differences in basic research engagement may exist (countries differ across a range of idiosyncratic characteristics, not just governance or funding), it is appropriate to test the robustness of Nowotny and colleague’s general claims.

Question 2: To what extent do academics specialise only in basic or applied research, or combine these activities?

If universities are no longer preoccupied with pure, fundamental and disinterested research, what have become the dominant research preoccupations? Nowotny et al. ( 2003 ) discuss how Mode 2 knowledge production is characterised by diversity, reflexivity and a shift away from the stable, clear and unchallengeable taxonomy of disciplinary research. Further, the original Mode 2 argument stated that Mode 2 did not replace Mode 1; it supplemented and grew alongside it (Gibbons et al. 1994 ). Accordingly, individual academics engaged in Mode 2 knowledge production may continue to be involved in basic research alongside application-oriented research. Adding further complexity, application orientation (and the four other attributes characterising Mode 2 knowledge) is “not present in every instance of Mode 2” (Gibbons et al. 1994 , p. 8). Thus, application orientation is neither a prerequisite, nor a sufficient, characterisation of Mode 2. This is similar to Stokes’ ( 1997 ) argument that considerations of use often go hand in hand with a quest for fundamental understanding. Despite these challenges, one may reasonably understand Nowotny et al. ( 2003 ) argument to imply a shift in the balance between basic and applied research, towards more applied research specialists.

The few studies that have reported on this issue indicate, however, that a traditional academic orientation is resilient rather than new combinations. Albert ( 2003 ) found no evidence that Canadian academics in sociology and economics departments had shifted their research attention away from disciplinary research intended for their peers and begun specialising in interdisciplinary and practical research. Studies of scientists in the Netherlands (van Rijnsoever and Hessels 2011 ) and Finnish academics in technology, social science and humanities departments (Hakala and Ylijoki 2001 ) also indicate high commitment to Mode 1-type research over purely applied research. Finally, Gulbrandsen and Kyvik ( 2010 ) found that few Norwegian academics in 2000 were engaged exclusively in basic research, but this was still greater than the proportion engaged exclusively in applied research, although a combination was most common for all university staff members. Each of these aforementioned studies is somewhat limited by their national context. Our international data provide us with a useful opportunity to examine this relationship.

Another contextual limitation of the Mode 2 thesis is academic field (Hessels and Van Lente 2008 ). Disciplinary cultures span international boundaries and influence what types of research are considered legitimate within the scientific community (Becher 1994 ). Weingart ( 1997 ) argues that Mode 2 attributes, such as application-oriented research, make little sense in scientific disciplines distanced from policy makers. Likewise, Godin ( 1998 ) notes the difficulty certain disciplines face when modifying their research towards applied and practical purposes. According to Becher ( 1994 ), academic disciplines differ in their cognitive knowledge structure from pure (theoretical) to applied (pragmatic). Applied disciplines within the technological sciences (e.g. engineering) are purposive and pragmatic in their knowledge, oriented towards mastery of the physical environment, resulting in products/techniques. In the pure sciences (e.g. physics), knowledge is cumulative, atomistic, well organised and results in discovery or explanation, rather than directly practical outputs. Social sciences and humanities may also be pure or applied, depending on whether they are oriented towards practical, utilitarian and functional improvements to professional practice (e.g. education), or understanding and interpretation (e.g. humanities).

Based on Becher’s typology, we may expect specialisation in basic research to be more common in the hard-pure disciplines (e.g. life sciences and physical sciences) and soft-pure disciplines (humanities and social sciences). By contrast, specialisation in applied research may be more common in the science-based professions within hard-applied disciplines (e.g. agriculture, engineering, medical sciences) and social professions within the soft-applied disciplines (e.g. education, law). Our data will allow us to extend disciplinary-based findings from specific countries to an international context, controlling for other individual-level factors.

Question 3: What might explain individual differences in specialisation versus combining basic and applied research?

Few universities are completely insulated from the increased steering of research priorities, commercialisation and accountability pressures, but this does not imply that individual academics will respond in consistent and uniform ways. Despite the decline in autonomy, academics still retain a relatively high degree of discretion over their non-teaching hours and have the freedom to seek research activities of professional and personal engagement, although the latter may not always be compatible with a secure academic career (Davies 2013 ). Why might some academics in comparable disciplines and countries engage predominantly in basic research, while others do not? We put forward three possible explanations: commercial research funding, institutional expectations and professional norms.

Nowotny et al. ( 2003 ) argue that inadequacy of public funding has caused universities to shift their research preoccupation away from basic research. Government core funding of research has declined in a range of OECD countries, and the mechanisms for disbursing public funds have shifted from stable block grants towards results-based models

(Auranen and Nieminen 2010 ). Given that basic research leads to unpredictable results and tends to attract little private sector funding, we may expect that access to business and industry funding will also be a differentiator between individual academics for basic and applied research engagement within countries. Academics able to source business and industry funding for research are less reliant on dwindling institutional funding and may be more likely to be engaged in applied research.

A second important factor is institutional expectations for research. Universities across the world have seen stakeholder expectations widen and deepen, including the expectations for producing practical knowledge and training. Clark ( 1998 ) describes the situation in terms of a demand-response imbalance: “demands on universities outrun their capacity to respond” (p. 6). In terms of functional differentiation, modern universities are characterised by “overcomplexity and underdifferentiation” (Enders 2013 ). However, universities are not entirely victims of past history and stakeholder demands. Universities are capable of choosing which stakeholder demands to respond to, where within the organisation these responses will occur and how. For example, entrepreneurial responses may occur at the “developmental periphery”, where traditional disciplinary research meets the outside world through interdisciplinary research, contract research, contract education and consultancy (Clark 1998 ). Such engagement with external stakeholders can diversify the funding base, promote an entrepreneurial culture throughout the university, as well as stimulate the “academic heartland” where traditional disciplinary and theoretical research is maintained. In other words, universities have the capability to respond to stakeholder needs by promoting commercial and interdisciplinary research. Therefore, we may expect that academics who work in such environments will be more likely to engage in applied research and not necessarily to the detriment of their engagement in basic research.

A third factor explaining diversity is professional norms. One of the defining characteristics of academia is autonomy and protection from external influence. The freedom of professors to teach without external control was a core principle of the mediaeval university and has influenced the development of universities, not just in Central and Western Europe, but also in the USA (Altbach 2001 ). The emergence of privately funded, practically oriented research for commercial or technology transfer purposes in the mid- to late twentieth century challenged established scientific norms, particularly the norms of disinterested research and public ownership of knowledge (Stuart and Ding 2006 ). The changing funding patterns have been described as placing the future of basic research in jeopardy (Altbach 2001 ). However, scientific norms are not static. Etzkowitz ( 1998 ) argues that scientific norms have progressively accepted commercial research and academic entrepreneurialism with profit-making motives. These normative changes may be considered the “second revolution” in academia, following the first revolution which made research a core function of universities. Given the challenges that applied research poses to traditional scientific norms, we expect that engagement in applied research will reflect how individuals view applied research. Those who view applied research as a professional obligation will be more likely to be more engaged in applied research.

Data and methodology

The data come from the changing academic profession (CAP) international survey of the academic profession across 19 countries during 2007–2008: Argentina, Australia, Brazil, Canada, China and Hong Kong, Finland, Germany, Italy, Japan, Malaysia, Mexico, the Netherlands, Norway, Portugal, South Africa, Republic of Korea, UK and the USA. The CAP project used a common random sampling protocol and survey instrument. The target population of the CAP survey was professionals in universities or other higher education institutions offering a baccalaureate degree or higher. Given that this article addresses research orientation, we restrict ourselves to academics employed in universities with research and teaching missions, generally those universities awarding doctorate degrees (i.e. excluding polytechnics, teaching-focused colleges, research institutes). We exclude four of the 19 countries from our analysis. This is due to an inability to differentiate between teaching-focused and research-focused universities or small sample sizes (Japan and Korea), low response rate (Portugal) or limited information on the sampling framework (South Africa).

The response rates, including partially completed responses, were mostly below 40 per cent. Therefore, there is a potential risk of non-response bias in some countries. However, national samples were found broadly representative of the respective populations on strata such as gender, academic rank and institutional type (Cummings and Finkelstein 2012 ; RIHE 2008 ; Vabø and Ramberg 2009 ). Further, there are few reasons to expect that non-respondents would differ substantially in terms of self-declared research orientation. After removing non-respondents to the core questions regarding basic and applied research orientation and respondents not employed in academic ranks, we were left with a sample of 12,379 academics (Table  1 ).

Dependent variable: measuring basic and applied research

We consider Nowotny et al. ( 2003 ) references to “pure”, “blue skies”, “fundamental” or “disinterested” research to be analogous to basic or theoretical research and context of application to be analogous to applied or practically oriented research. Our data for measuring research orientation come directly from the CAP survey. The relevant question asked: “How would you characterize the emphasis of your primary research this (or for the previous) academic year?” Respondents provided a score ranging from “very much” (1) to “not at all” (5) on seven separate criteria. This study examines the results for two of these criteria: “basic/theoretical” and “applied/practically oriented”. The explicit reference to theory and practical orientation is consistent with the extended definitions of basic and applied research in the OECD’s Frascati Manual ( 2002 ). Given that respondents provided scores on each criterion separately, it was up to individual to determine whether the concepts of basic and applied research were mutually exclusive or inclusive.

We are interested both in the extent to which basic research is a defining characteristic of academic work, as well as its relationship with applied research. We distinguish between five distinct research orientations: pure basic; leaning towards basic; equally, basic and applied; leaning towards applied; and pure applied. A sixth category for respondents with neither basic nor applied research orientation is a residual category for those not declaring a clear research orientation. The definition of each research orientation group and number of respondents in each group is presented as a cross-tabulated matrix in Table  2 .

There are certain limitations which may affect how the research results are interpreted. Firstly, there is a risk that the concepts of “basic”, “theoretical”, “applied” and “practically oriented” may be understood differently across countries, particularly after translation into local language. In countries where English was not the language of the survey, most used a combination of professional translators, academic translators, survey piloting and peer review (e.g. regions with common languages), in addition to internal review by the project team.

Although we are confident the concepts of basic and applied research were translated accurately, there is a risk the concepts are interpreted differently, particularly in countries where research has not traditionally been conducted within universities. Secondly, one assumes that differences in response to Likert-type questions reflect differences in substantive engagement and that engagement is related to contextual factors such as research norms of the discipline or institution. However, academics may exaggerate or define their research in accordance with norms, rather than contextual factors influencing substantive engagement.

Independent variables: factors associated with research orientation

The operationalisation of independent variables is outlined below. Means on each variable are listed for each country in Table  4 of “ Appendix ”.

Country is a dichotomous variable based on country of employment: Argentina; Australia; Brazil; Canada; China; Finland; Germany; Hong Kong; Italy; Malaysia; Mexico; Netherlands; Norway; UK; and USA.

Academic discipline is a dichotomous variable based on discipline of one’s current academic unit: education (teacher training and education science); humanities (humanities and arts); social sciences (social and behavioural sciences); commerce (business and administration, economics); law; life sciences; physical sciences (physical sciences, mathematics, computer sciences); engineering (engineering, manufacturing and construction, architecture); agriculture; medicine (medical sciences, health-related sciences, social services); and other (personal services, transport services, security services or other). Based on Becher’s ( 1994 ) disciplinary typology, we expect basic research to continue to be a core preoccupation of academics within hard-pure sciences and soft-pure social sciences and humanities.

Commercial research funding is the percentage of one’s research funding (in the current or previous academic year) which came from business firms or industry. We expect that engagement in applied research will be greater among those academics with greater access to external commercial research funding.

Individual normative/social behaviour includes one scale variable based on the ordinal responses (from “strongly agree” [5] to “strongly disagree” [1]) to the following statement: “Faculty in my discipline have a professional obligation to apply their knowledge to problems in society”. We expect that engagement in applied research will reflect the extent to which individuals view applied research as a professional obligation.

Institutional norms and expectations/policy/strategy includes one scale variable based on the ordinal responses (from “strongly agree” [5] to “strongly disagree” [1]) to the following statement: “Your institution emphasizes commercially-oriented or applied research”. We expect that academics working in environments emphasising commercially oriented and applied research will tend to engage more in applied research.

Basic research as a pre-occupation

Our first research question addressed the extent to which basic research is still a defining characteristic of universities. When asked to characterise the emphasis of their primary research in the previous year on a scale of 1 (“very much”) to 5 (“not at all”), 61 % reported significant engagement in basic research (values of 1 or 2). Very few academics (7 %) reported being “not at all” engaged in basic research, with the remainder engaged partly in basic research (32 %). Based on these results, Nowotny et al. ( 2003 ) claim that fundamental research is a minority preoccupation in universities received little empirical support, with 93 % reporting at least some engagement with basic research. At the very least, claims that fundamental research is a minority preoccupation should be qualified by country. Levels of significant engagement in basic research ranged from 78 % in China to half of all academics in Argentina (50 %). Universities in Argentina, and to a lesser extent, Brazil, tend to employ a large number of part-time and teaching-only staff, but this does not account for their relatively weak engagement in basic research. Part-time staff in these countries did not differ from their full-time colleagues in their engagement in basic research.

Although most academics engage in basic research, a greater proportion report engagement in applied research. To this extent, fundamental research may not be the primary preoccupation of universities, even though the majority engage in it. More than two-thirds (69 %) of academics characterised their research as applied to a large extent, one quarter (25 %) were partly engaged in applied research, and 6 % not at all. China was again the country where academics were most likely to report strong engagement in applied research (86 %). Norway and the Netherlands were the only countries where more academics reported significant engagement in basic research compared to applied research. The proportion of academics in each country reporting significant engagement in basic research (values of 1 or 2) is shown in Fig.  1 on the left-hand bar, while the proportion reporting significant engagement in applied research is shown on the right-hand bar. Note that the basic and applied characterisations were derived from separate questions. Therefore, it was up to the respondent to decide whether the two categories (basic and applied) were mutually exclusive characterisations. The fact that sum of both bars exceeds 100 % in all countries is evidence that many academics do not consider these characterisations as mutually exclusive.

Proportion of academics whose primary research was to a considerable extent basic/theoretical ( left-hand bar ) and applied/practical ( right-hand bar ), by country

Specialisation versus the coexistence of basic and applied research

In order to investigate the relationship between basic and applied research, and tendency towards specialisation, we exclude respondents who were not sufficiently engaged in basic or applied research. Rather than being engaged in research outside the basic/applied typologies, academics with no clear research orientation are better understood as insufficiently research active. For example, they published little, spent very few hours on research and were also unlikely to characterise their research in other ways (e.g. commercially oriented, single disciplinary or interdisciplinary). These 998 respondents were classified as “neither basic nor applied” in Table  2 (8 % of the total sample) and are subsequently excluded from the valid cases for further analysis.

Among academics with clear research orientations, 14 % specialised as “pure basic” researchers. On this basis, only a small minority of academics may be understood to be preoccupied entirely with basic research. However, the lack of specialisation was not primarily due to a tendency to specialise in applied research, and only 17 % could be considered “pure applied” researchers. The bulk of the sample engaged in both basic and applied research. The most frequent categorisation was “equally, basic and applied” (27 %), followed by “leaning towards applied” (24 %) and “leaning towards basic” (18 %). In other words, basic research is not the primary preoccupation of most academics, but this is because it is integrated with applied research in most cases.

Specialisation varied considerably across countries. Pure basic researchers were most common in Italy, Netherlands and Norway, comprising roughly one quarter of all academics and outnumbering those in the pure-applied category. At the other extreme, almost no academics were specialised in basic research in China (2 %) and Malaysia (5 %). However, this reflected a greater tendency to combine basic and applied research in these countries, rather than an aversion to basic research. The balance between basic and applied research was most skewed in Australia, where pure applied researchers were more than twice as common as pure basic researchers. The proportion of academics specialising in basic or applied research by country is shown in Fig.  2 . The full breakdown across the five research orientation categories is shown for each country in Table  5 of “ Appendix ”.

Proportion of academics engaged purely in basic/theoretical research ( left-hand bar ) and purely applied/practical research ( right-hand bar ), by country

Research specialisation was closely aligned with Becher’s ( 1994 ) disciplinary typology of pure/theoretical and applied/pragmatic knowledge structures. Pure basic researchers were most common in the humanities (28 %), life sciences (22 %), social sciences (19 %) and physical sciences (18 %). These discipline-based differences were broadly consistent across country samples, with humanities ranked in the top 3 basic research disciplines in all countries other than Australia. Comparably, few pure basic researchers were located in Becher’s applied disciplines within the science-based and social professions, such as medicine (9 %), business studies (8 %), education (7 %), agriculture (4 %) and engineering (3 %). The balance between specialisation in basic or applied research also aligns with Becher’s typology, with few disciplines having a roughly equal number of pure applied and pure basic researchers. One possible exception may be law, where pure applied research was rare (10 %) compared to pure basic (18 %). This appears to contradict the practice orientation of the discipline. However, it may be that purely “practically oriented” research is defined differently by law academics, given that research within a legal practice involves direct remuneration for specific clients. The proportion of academics specialising in basic or applied research by discipline is shown in Fig.  3 (full breakdown by discipline in Table  6 of “ Appendix ”).

Proportion of academics engaged purely in basic/theoretical research ( left-hand bar ) and purely applied/practical research ( right-hand bar ), by discipline of current academic unit

Compared to the country-level results where pure applied researchers were only marginally more common than pure basic researchers, research orientation is considerably more fragmented across disciplines. This also supports Becher’s claims that disciplinary cultures often span national boundaries and embody collective norms. Taken together, the large differences across countries and disciplines suggest that claims regarding basic, theoretical or blue sky research should be nuanced with reference to national and disciplinary context.

Multinomial results

Our third research question was how we might account for individual-level differences in engagement and specialisation in basic and applied research. From the above results, the disciplinary implications are clear. Given that countries differ in their proportion of academics by discipline, cross-country comparisons need to control for these differences. However, significant variation within countries and disciplines suggests that other factors may also influence specialisation. To examine this further, we computed a multinomial logistic regression with three categorical outcomes: pure basic, pure applied and basic and applied as the reference category (merging the three categories which involved both types of research). Controlling for country and discipline, we investigated whether specialisation in basic or applied research was also associated with differences in access to commercial research funding, institutional expectations/policies and individual normative/social behaviour. We also checked whether the results differed when controlling for demographic characteristics (gender, age and age squared), but the main results did not change. The multinomial logistic regression results are presented in Table  3 .

The likelihood ratio tests from the multinomial regression results (not shown) indicated a statistically significant relationship between research specialisation and commercial research funding, organisational expectations and individual norms. However, organisational expectations for commercially oriented or applied research (institutional norms) were not a statistically significant factor differentiating between all categories of research specialisation. Compared to the reference category for those engaged in both basic and applied research, and holding other factors constant, a one unit increase in agreement (on a five-point scale) with the statement “Your institution emphasizes commercially-oriented or applied research” was only statistically significant in its negative association with being a pure basic researcher (with a decrease in the multinomial log odds of 0.15 per unit increase). Agreement with this statement was not significant in its relationship with being a pure applied researcher. In other words, institutional norms emphasising research commercialisation appear to increase engagement in applied research but not specialisation in this type of research. On the other hand, individual norms were significantly associated with both types of specialisation. A one unit increase in agreement with the statement that “Faculty in my discipline have a professional obligation to apply their knowledge to problems in society” was associated with an increased likelihood of being pure applied. Agreement was even more strongly associated with a decrease in the likelihood of being a pure basic researcher.

External commercial research funding was a significant differentiator for research specialisation, but mostly in its negative relationship with pure basic research specialisation. A one point percentage increase in the proportion of one’s research funding coming from business or government sources was associated with a small but statistically significant decrease in the likelihood of being a pure basic researcher. To a lesser extent, commercial research funding was also positively associated with pure applied research. In other words, commercial research funding tends to increase the likelihood of being engaged in applied research, but mostly in combination with basic research rather than in specialisation. This relatively minor relationship is partly due to the small share of academics for whom commercial funding is a main source of research funding. In most countries, the mean proportion of research funding from business and industry was around 5 % or less. However, in Germany and China, these proportions were considerably greater at 10 and 15 %, respectively (see Table  4 of “ Appendix ”). This is consistent with their high proportion of higher education R&D expenditure financed by industry, at 14 and 33 %, respectively, versus 6 % across all OECD countries (OECD 2013 ).

The control variables for country and discipline remained statistically significant in their relationship to research specialisation. Specialisation in pure basic or pure applied research was significant and negatively associated with being located in China and Malaysia (compared to the reference category of USA), which was to be expected given the aversion towards specialisation found in the descriptive results. The other regional patterns that remained significant were the greater likelihood of pure basic researchers to be located in the Western European countries of Italy, Norway, the Netherlands and Finland. Disciplinary differences continued to reflect Becher’s ( 1994 ) typologies. Pure basic researchers were significantly more likely to be located in the humanities, while pure applied researchers were significantly more likely to be located in engineering, agriculture, medicine and commerce.

In summary, pure basic researchers could be more clearly differentiated from others based on their research funding, professional norms and perceptions of institutional expectations, while pure applied researchers share more in common with those engaged in both basic and applied research.

Discussion and conclusion

Basic research is an activity that continues to define academic work at most research universities around the world. The majority of academics (61 %) in our data material based on more than 10,000 researchers from 15 countries report significant engagement in basic research, and very few report no engagement in basic research (7 %). Although these aggregate results mask considerable diversity across countries, there is little evidence that basic research is a minority preoccupation in any of the countries for which we have data. However, more academics are engaged in applied research than basic research. Those academics that engaged in both types did not do so equally, with more academics leaning towards applied over basic. This suggests there are some trade-offs between the two activities. Therefore, basic research may be considered to be an important part of most academics’ research, but often secondary to applied research. Our cross-sectional data do not allow us to investigate the extent to which theoretical research has declined from a dominant position, but the resilience of theoretical research lends stronger support to the more nuanced position of the triple helix model (Etzkowitz and Leydesdorff 2000 ). The shift towards Mode 2 or practical research appears to be emergent, rather than complete, with universities remaining a core producer of theoretical knowledge.

Our research generally supports Stokes ( 1997 ) since a main result is that combining basic and applied work is the norm for the majority of academics, regardless of country and discipline. It also supports earlier work claiming that traditional academic orientations remain strong in universities, even in periods of decreased core funding and increased pressure for various forms of societal engagement (Ylijoki et al. 2011 ). We still do not have sufficient evidence to conclude whether the frequent “hybrid profiles” of academics are productive and meaningful or whether they to a greater extent represent incommensurable activities leading to stress and insecurity (c.f. Davies 2013 ).

Disciplinary-based analyses showed basic research to be relatively weaker in the professional social science disciplines (e.g. business and education) and the applied sciences (engineering, agriculture and medicine). Academics within these disciplines were rarely primarily focused on theoretical research, but these patterns probably reflect the cognitive knowledge structures of these disciplines and their orientation towards practical outputs (Becher and Trowler 2001 ), rather than a weakening importance of theoretical research. The diversity within these disciplines also suggests that theoretical research can be an important, and even dominant, motivation. Highly practical results may be the starting point for conducting research in some applied disciplines, but academic prestige has a strong appeal for academics across various disciplines (Hakala and Ylijoki 2001 ). Nevertheless, with up to around one-third of academics in the applied sciences engaging purely in applied research, compared to less than one-fifth in other fields, a complete devotion towards practice orientated research is clearly more relevant in certain fields. This lends stronger support to the field-dependent interpretations offered by Weingart ( 1997 ) and Woolf ( 2008 ) in their competing frameworks for understanding changes to the science system.

Our cross-country results are less easy to explain and should be treated with caution due to the limitations of the data. The strong position of basic research in Finnish and Norwegian universities has been noted previously (see Ylijoki et al. 2011 ; Gulbrandsen and Kyvik 2010 ), but our international results from other countries suggest a more nuanced interpretation. Although more academics were significantly engaged in basic research than applied research in Norway and the Netherlands (with an equal division in Italy), these countries were the exceptions. Applied research dominated in most countries, particularly in Australia where academics were roughly twice as likely to specialise or lean towards applied research compared to basic research. This tendency was also noticeable in the USA and Hong Kong. One possibility is that applied research engagement reflects academic governance systems, with a stronger academic oligarchy protecting the place of basic research compared to market-driven systems (Clark 1983 ), but this was not consistent with results in all countries.

Institutional norms emphasising research commercialisation were weakest in Mexico, Argentina and Brazil, and relatively weaker in most European countries (see Table  4 of “ Appendix ”). This was consistent with traditional systems of bureaucratised governance within Latin American universities, which have been criticised for creating few incentives to engage in research commercialisation and research productivity more generally (Thorn and Soo 2006 ). However, although each of these institutional aspects differentiated between individuals within countries in consistent ways, the differences across countries remained. For example, strong institutional-level norms towards research commercialisation in the UK were not consistent with the rather moderate engagement in applied research in this country. Nor were weak commercialisation norms in Brazil and Argentina consistent with the moderate engagement in applied research and weak engagement in basic research. In Latin America, it has been claimed that university discourse has shifted enthusiastically towards Mode 2, but the academic evaluation system remains rooted in Mode 1 outputs, leading to an “evaluation system schizophrenia” (Arocena and Sutz 2001 , p. 1231). Engagement in basic and applied research clearly has strong country-level features, implying that generalised statements about basic or applied research must be qualified for local context.

China and Malaysia are particularly interesting countries because both applied research and basic research were strong self-characterisations. Neither country has strong university research traditions, but their respective governments have recently increased their expectations for basic research as part of innovation and development plans (Zhu and Gong 2008 ; Lee et al. 2013 ). Up until the mid-1980s, Chinese universities were predominantly teaching focused, receiving no specific funding for research. Basic research was the responsibility of the Chinese Academy of Science and other national research institutes, while applied research was conducted by research institutes in industrial sectors (Zhu and Gong 2008 ). Malaysian universities have also been largely teaching focused and lacked an academic research culture (Lee 2003 ). Strong engagement in both basic and applied research may reflect an emerging research culture which supports both basic and applied research. According to Mohrman et al. ( 2008 ), at the same time that Chinese institutions are encouraging basic research, universities are also responding to demands for applied research as a service to society. This appeared to translate into individual-level norms. Academics in these two countries were most likely to report stronger agreement that academics had a professional obligation to apply their knowledge to problems in society.

However, the results for China and Malaysia also highlight a broader difficulty in defining basic and applied research engagement. Even though we are confident that concepts of basic/theoretical research and applied/practically oriented research were understood in each country and the investigation teams went to great lengths to ensure translations were accurate and comparable, there is a risk that subtle differences in understanding might be responsible for parts of the differences across countries in responses. Academics in some countries may declare a greater engagement in basic research due to, for example, a broader definition of what “theoretical” or “practical” research means. This also goes beyond linguistic differences to general understanding, acceptance and reward structures for basic and applied research. The strong relationship between individual norms, institutional expectations and self-reported engagement in applied research could indicate that academics are responsive to institutional policies supporting applied research. Unfortunately, individuals may also simply exaggerate behaviours in accordance with what they perceive to be the norms of their discipline and institution. This has implications for the growing expectations of research “relevance” from higher education stakeholders around the world (Enders and Musselin 2008 ). If most academics genuinely believe they are already deeply engaged in practical research, it may indicate they hold a different understanding of applied and practically oriented research compared to the policy makers articulating the need for greater engagement.

We have shown that the emphasis on applied research is not only related to funding and university strategies; it is even more strongly related to individual norms about academics obligations. National and institutional policies for more applied and practically related activities are therefore not necessarily out of touch with core academic values. Our main finding is that different types of research activities are mixed and combined at the individual level, despite variations between countries and between academic disciplines. This does not imply that the balance between basic and applied research is equal, or that basic research has not lost its pre-eminence, but it does imply that basic research retains a core position within the research mind sets of most academics. As such, our results can be interpreted as a warning against policies striving for clear division in the higher education landscape between institutions primarily doing basic research and others applied. This seems out of touch with widely shared norms and practices among the researchers themselves, who overwhelming combine these activities.

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Bentley, P.J., Gulbrandsen, M. & Kyvik, S. The relationship between basic and applied research in universities. High Educ 70 , 689–709 (2015). https://doi.org/10.1007/s10734-015-9861-2

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What is applied research anyway?

Revista de Gestão

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Marotti de Mello, A. and Wood Jr, T. (2019), "What is applied research anyway?", Revista de Gestão , Vol. 26 No. 4, pp. 338-339. https://doi.org/10.1108/REGE-10-2019-128

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Published in Revista de Gestão . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

The above-mentioned question brings no huge mystery. The respected Frascati Manual has a good answer for it. Applied research is original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific, practical aim or objective ( OECD, 2015 ). It is about using the existing stock knowledge with the appropriate methodology towards a specific objective, which is usually related to the resolution of a practical problem.

It all would be simple, were it not for the unrestrained creativity of scientists to appropriate, interpret, (re)signify and transmute concepts. This is how the above-mentioned question tends to get different answers, which are influenced by the perspective and personal interest of respondents.

Many scholars advocate that every research in Administration is applied; after all, this is an essentially practical field. It does not mean, however, that executives and entrepreneurs will look forward to the latest edition of Academy of Management Journal or of RAUSP to solve their problems. There is a time and place for everything. It is expected that the significant amount of published articles in good scientific journals contribute for the advance of the administrative science. In some point, the accurate accumulated knowledge will serve as a basis for books, popular articles, lectures and consulting projects, reaching therefore its final destination.

However, there is an increasing unease regarding the functioning of the massive machine that generates such knowledge. Renowned researchers and academic leaders from several areas and places have been criticizing the system for two decades. They affirm that this powerful machine became wicked, too expensive, inefficient, obsessed with precision and focused only on its self-interests, ignoring the real world. There has been several propositions of diagnosis for a structural reform, which involved scientific journals, business schools, academic associations and certification and regulation entities. In short, it is necessary to redirect the system towards its original noble aim: to help managers facing challenges and building a better world.

Institutional changes are always difficult and time-consuming. It is necessary to overcome the interests of established groups and the inertia of embedded practices. Still, several initiatives towards the construction of bridges between theory and practice have been emerging, such as events that gather academics and practitioners, academic journals focused on managers and professional master and doctorate programs. Important agents, such as the Academy of Management , Academy of Management Journal and the Association to Advance Collegiate Schools of Business ( AACSB ) have been showing clear signs of alignment with new times.

Considering the context of change, it is important to invite all the readers of this special edition to consider an important aspect: the differentiation between applicable and applied research. Both are important. An applicable research can contribute to the transformation of reality in different ways. It can bring an original explanation of a new phenomenon or a new explanation for an already known phenomenon. It can still offer a new model to help organizations to solve problems or to benefit from business opportunities.

However, the current scenario seems to ask for the accomplishment of real applied research; i.e. the research that follows Mode 2 production of knowledge (observed by Michael Gibbons and his peers Gibbons et al. (1994) a quarter of century ago): it emerges from a relevant practical issue; it is developed collectively in a multidisciplinary or transdisciplinary way; it results in an effectively implemented solution; and whose quality is evaluated by groups composed of researchers and managers. In short, a research that is demonstrably relevant and accurate.

The conduction of real applied research is a significant challenge. It implies leaving the academia shelter in order to interact with players that have different mindsets, developing the ability to translate scientific knowledge to these players, and, specially, being open to learn from this practice. This is the path we started to follow.

Aligned with this idea, REGE has been accepting applied research papers since 2018. This special issue intends to contribute to the dissemination and development of applied research.

We have received more than 30 papers for this special issue, from Brazil and Latin America. After a careful process of double blind review, six were accepted, bringing a broad view of production in applied research in management, with articles from different regions, countries and research areas.

Gibbons , M. , Limoges , C. , Nowotny , H. , Schwartzman , S. , Scott , P. and Trow , M. ( 1994 ), The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies , Sage , London .

OECD ( 2015 ), “ Frascati manual 2015: guidelines for collecting and reporting data on research and experimental development ”, available at: www.conicyt.cl/wp-content/uploads/2014/07/Manual-Frascati-2015.pdf (accessed June 1, 2019) .

Acknowledgements

  REGE would like to thank all the researchers that placed their trust in the journal and submitted their work for the evaluation. The guest editors would also like to thank all reviewers that contributed to this special issue with their precious time and knowledge.

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Balancing basic and applied research

Traditionally, basic and applied research were seen as activities of a different nature, carried out by different institutions and financed from different sources. But in the 1970s and 1980s, the information and communication technologies (ICT)—later reinforced by biotechnology—started a trend in which it became increasingly important to turn scientific research into concrete products. Whole industries were built around the notion of developing new commodities and services from basic research as quickly as possible. As a result of these new economic developments, the strict division between basic and applied research in those fields has weakened, until the boundaries have become obsolete and sometimes artificial.

Consequently, the way in which researchers co-operate and forge partnerships with industry, as well as the regional importance of academic research and its role in the local economic setting, changed dramatically during the 1990s. While universities were predominantly government funded with the logical consequence that research results were made fully available to society as a whole, more recently they have evolved into institutions funded through a variety of sources and with different stakeholders, who expect the knowledge generated to be protected and exploited in an appropriate way. Of course, these changes have been more prevalent in fields such as biomedical research or ICT where basic research can be translated fairly rapidly into viable applications, unlike in other domains such as astronomy or particle physics.

But the ‘intrusion’ of private enterprises and funding into the academic world is not to everybody’s liking. Some critics fear that financial dependence on private sources will diminish the independence of the university and its scientists ( Nature , 2001). Others see conflicts of interest in publishing results that can be developed into potential products, and fear that private stakeholders will eventually undermine some basic principles of research, most notably the free exchange of information. They also believe that the reputation of scientists as independent experts might become damaged if they have financial interests, for instance as shareholders of biotechnology companies ( Hall and Scott, 2001 ).

But a lot of this criticism disregards the potential benefits to be gained from private and public co-operation in the academic world. Indeed, this new strategic attitude promises to create advantages both from a general economic point of view as well as royalties that can be re-invested into research and thus strengthen the universities’ research base. Varga (1998) confirms that it is precisely the mix of different types of research and development activities that has the most significant impact on science’s contribution to the economic performance of individual countries and regions.

What are also not being taken into account by many critics are increasing budget constraints, a problem that has intensified over the last decade. While governments have been reducing spending on basic science, research has become more expensive and increasingly under pressure to contribute to economic growth. These constraints, as well as the traditional dualism between basic and applied research, have weakened the European position in research and development (R&D). However, many problems of both basic and applied research are comparable: the total R&D budget of the EU member states is substantially lower than that of Japan or the USA; the employment of researchers in European industry accounts for only 2.5 per thousand as compared with 6.7 per thousand in the USA; the brain-drain that was observed at the beginning of the 1990s is still ongoing and, finally, many studies indicate that Europe is less innovative and losing ground in several important markets, particularly ICT and biotechnology (European Commission, 1996). Thus, it is not necessary and rather counterproductive to worry about the appropriate distinction between basic and applied research when, in fact, they share many problems.

Instead, a proper mix of basic and applied research can be a large contributor to local economies, creating high-quality jobs as well as revenues both for universities and society. Varga (1998) , when investigating the impact of both R&D expenditures and of academic research in 125 regional statistical entities—so-called ‘Metropolitan Statistical Areas’ or MSAs—in the USA, found that R&D employment in industry has a positive effect on the innovation output of each region. Also, a region’s university level research can only have a positive effect on that innovation output if there is sufficient interaction between academic research and a flourishing entrepreneurial ‘texture’ of high-tech ventures and start-ups. But an overly strong presence of large, established manufacturing-intensive firms appears in turn to have a significant but negative effect. In short, the picture that emerges from these studies emphasises the need for sufficient diversity and critical mass in terms of the interactions between university research on the one hand and a high-tech, R&D-intensive industrial environment on the other.

The interaction of universities with the economy has also become more varied in nature. Whereas traditionally the education of students tended to be the major mechanism by which universities transferred their know-how to society, this has now changed. Besides nurturing human talent, universities are now expected to devote at least part of their research to solving practical problems. A recent study ( Verbeek et al. , 2002 ) modelled the linkage between science and technology by assessing scientific journal citations within patent documents. It shows that the distribution is very skewed—about 90% of all references in the patent literature occur within less than 20% of all technology domains, with life sciences and ICT being the leading areas. Furthermore, whenever the science linkage occurs, there is a significant correlation of articles published in so-called basic science journals to the patent literature; in other words, there is a direct link between basic research and technological application. These findings again support the hypothesis that the boundaries between basic research and technological applications, at least in certain areas, are now blurred.

For many research groups, participation in industrial research projects often increases their visibility, profile and scientific importance. In some cases, this attitude results in a change of mission, and, consequently, in an emphasis on short-term and policy orientated research. In many European countries, this trend has been enhanced by the stagnation, or even reduction, of government support for basic research. In addition, the EC has created new funding opportunities for ‘problem-solving’, ‘pre-competitive’ and applied research, but a structured funding of basic research is not yet a priority ( Goffeau, 2001 ). These trends in European R&D funding lead to problems in innovation behaviour, which have been documented previously in the ‘Green paper on innovation in Europe’. Figure ​ Figure1 1 shows a certain convergence in R&D spending at the international level. For instance, in 1990 Japan’s R&D budget represented 0.5% of its Gross Domestic Product (GDP), compared with 1.4% in France. The comparable percentages in 1996 showed a narrowing of the gap: 1.1% for France and 0.6% for Japan. Is this purely coincidental or are there specific reasons behind this trend? The ongoing internationalisation of science makes knowledge accessible anywhere in the world. The idea of ‘anchoring knowledge’ in a country or region is extremely difficult in an economic environment dominated by multinationals that buy knowledge or technology where it is available in order to implement it where it is needed. Therefore, why should a country or region invest substantially more in R&D than its neighbours?

Fig. 1. Governmental R&D expenditure in some EU countries, the USA and Japan as a percentage of GDP (*43% of structural funding of universities included in the R&D statistics, **25% of structural funding of universities included in the R&D statistics).

On the other hand, the opportunities for industrial contracts and partnerships have increased dramatically. In some cases, academic institutions have gradually transformed themselves into partially or largely self-financed ‘profit’ centres. One can hardly believe that this trend will become a dominant factor, but it shows that the academic world is undergoing significant restructuring and reshaping. And mobility of scientists and rapid exchange of knowledge via the Internet will lead to even more changes. Indeed, the European research centres should also take into account that ‘competition on the knowledge market’ is growing very rapidly. Europe is still at the forefront of scientific progress, but its historical strength in scientific research is more ‘volatile’ than ever before. In the long term, one may expect that new economies will establish their own centres for the development of new knowledge.

In order to maintain Europe’s level of scientific excellence, it is thus desirable and necessary that R&D investments become a common top priority, comparable to the financial objectives that were imposed to create the European Monetary Union. A common ‘Maastricht standard’ for R&D funding should therefore be created: 1% of the GDP to be spent on R&D would seem to be a feasible minimum.

These developments on a European and International level are now confronting the universities with two important challenges. In many countries, government funding for basic research is stagnating and, in addition, a considerable share of it is now provided on a contract basis, conditional on specific, measurable short-term objectives. And this will eventually impinge on academic freedom and creativity due to the workload caused by such short-term projects—creativity requires a maximum degree of independence as well as a reasonable assurance of stable, long-term funding. The dilemma between the objective of additional funding versus the objective of academic freedom is a point of concern for nearly every modern university laboratory.

Indeed, when Marcia Angell (2000) analysed clinical research and its ties to the pharmaceutical industry, she drew some conclusions that, in fact, affect the whole academic research structure and reinforce some of the criticisms mentioned earlier. Many governments now have created specific programmes to encourage partnerships between academia and industry to facilitate technology transfer and to provide an additional source of funding for basic research. The most remarkable conclusion that Angell draws, however, is the concern that such partnerships with industry may bias research, and ultimately influence the ‘scientific judgement’ of the scientist involved.

These findings have raised some important questions. Should universities adopt stronger conflict-of-interest guidelines, not only to meet the traditional university mission, but also in order to protect the sustainable progress of basic research? Should universities enforce rules with respect to personal engagements of academic staff in industry? Should scientific journals mention the source of funding for the projects leading to their published articles? And should universities maintain the balance between basic and applied research in each of their research groups?

But these questions might be missing the point. Indeed, because of its added value for both universities and industry, efforts to enhance the transfer of technology will undoubtedly play an essential role in future science policies. A rapid translation of new knowledge into technologies reduces the overall costs of research and facilitates the launching of new products or services onto the market place. But the main task for the academic sector is the creation of new knowledge. A recent study, ‘New forces at work: industry views critical technologies’, based on a number of interviews with high-level industrialists in the USA, confirmed the importance of universities in the innovation chain ( Popper et al. , 1999 ). The interviewees unanimously responded that ‘Our greatest sources of strength are our universities. They are the “Mecca” of higher education world-wide’. In general, business holds the view that universities add value by focusing primarily on fundamental research as well as on high-level teaching.

But those representatives from US industry also voiced concerns about the university research sector. First, the changing attitude of universities towards intellectual property ownership was expressed by several interviewees as: ‘Frictions are beginning to appear [between industry and the university sector] as the university tries to own all of the intellectual property that it has produced. The university connection has been very important to us, but universities are getting greedy.’ A second problem—already mentioned, but confirmed by US industrialists—is that government funding of the university sector appears to be decreasing, which weakens a critical link in the innovation chain. From the university’s point of view, this second problem is closely related to the first one: with their efforts to capitalise on intellectual property, universities are attempting to collect royalties to compensate for reductions in government support.

These significant economic developments in recent years have affected the research-related mission of the academic sector. Universities are still at a loss as to how best to react to these changes and are becoming more diverse in structure and more orientated towards social and economic challenges. But this is sometimes at the expense of basic research, the most important task of academia and its true strength. These trends raise many questions concerning new balances between research and knowledge transfer as well as between industrial partnerships and academic freedom. Those universities that face these challenges and actively manage these balances will maintain their unique position as important players in long-term basic research and are likely to be the most successful ones in the future.

• Angell M. (2000) Is academic medicine for sale? N. Engl. J. Med. , 342 , 1516–1518. [ PubMed ] [ Google Scholar ]
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• Varga A. (1998) University research and regional innovation: a spatial econometric analysis of academic technology transfers. Economics of Science, Technology and Innovation , Vol. 13. Kluwer Academic, Boston, MA.
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Journal of Applied Research on Industrial Engineering 

 ISO Abbreviation: 

J. Appl. Res. Ind. Eng.

Publisher: Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher Education

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Problematization and IoT studies in Logistics and Supply Chain Management: In search of theory through literature review

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Published on 11.4.2024 in Vol 26 (2024)

Evaluating the Digital Health Experience for Patients in Primary Care: Mixed Methods Study

Authors of this article:

Original Paper

• Melinda Ada Choy 1, 2 , BMed, MMed, DCH, MD   ; 
• Kathleen O'Brien 1 , BSc, GDipStats, MBBS, DCH   ; 
• Katelyn Barnes 1, 2 , BAPSC, MND, PhD   ; 
• Elizabeth Ann Sturgiss 3 , BMed, MPH, MForensMed, PhD   ; 
• Elizabeth Rieger 1 , BA, MClinPsych, PhD   ; 
• Kirsty Douglas 1, 2 , MBBS, DipRACOG, Grad Cert HE, MD  

1 School of Medicine and Psychology, College of Health and Medicine, The Australian National University, Canberra, Australia

2 Academic Unit of General Practice, Office of Professional Leadership and Education, ACT Health Directorate, Canberra, Australia

3 School of Primary and Allied Health Care, Monash University, Melbourne, Australia

Corresponding Author:

Melinda Ada Choy, BMed, MMed, DCH, MD

School of Medicine and Psychology

College of Health and Medicine

The Australian National University

Phone: 61 51244947

Email: [email protected]

Background: The digital health divide for socioeconomic disadvantage describes a pattern in which patients considered socioeconomically disadvantaged, who are already marginalized through reduced access to face-to-face health care, are additionally hindered through less access to patient-initiated digital health. A comprehensive understanding of how patients with socioeconomic disadvantage access and experience digital health is essential for improving the digital health divide. Primary care patients, especially those with chronic disease, have experience of the stages of initial help seeking and self-management of their health, which renders them a key demographic for research on patient-initiated digital health access.

Objective: This study aims to provide comprehensive primary mixed methods data on the patient experience of barriers to digital health access, with a focus on the digital health divide.

Methods: We applied an exploratory mixed methods design to ensure that our survey was primarily shaped by the experiences of our interviewees. First, we qualitatively explored the experience of digital health for 19 patients with socioeconomic disadvantage and chronic disease and second, we quantitatively measured some of these findings by designing and administering a survey to 487 Australian general practice patients from 24 general practices.

Results: In our qualitative first phase, the key barriers found to accessing digital health included (1) strong patient preference for human-based health services; (2) low trust in digital health services; (3) high financial costs of necessary tools, maintenance, and repairs; (4) poor publicly available internet access options; (5) reduced capacity to engage due to increased life pressures; and (6) low self-efficacy and confidence in using digital health. In our quantitative second phase, 31% (151/487) of the survey participants were found to have never used a form of digital health, while 10.7% (52/487) were low- to medium-frequency users and 48.5% (236/487) were high-frequency users. High-frequency users were more likely to be interested in digital health and had higher self-efficacy. Low-frequency users were more likely to report difficulty affording the financial costs needed for digital access.

Conclusions: While general digital interest, financial cost, and digital health literacy and empowerment are clear factors in digital health access in a broad primary care population, the digital health divide is also facilitated in part by a stepped series of complex and cumulative barriers. Genuinely improving digital health access for 1 cohort or even 1 person requires a series of multiple different interventions tailored to specific sequential barriers. Within primary care, patient-centered care that continues to recognize the complex individual needs of, and barriers facing, each patient should be part of addressing the digital health divide.

Introduction

The promise of ehealth.

The rapid growth of digital health, sped up by the COVID-19 pandemic and associated lockdowns, brings the promise of improved health care efficiency, empowerment of consumers, and health care equity [ 1 ]. Digital health is the use of information and communication technology to improve health [ 2 ]. eHealth, which is a type of digital health, refers to the use of internet-based technology for health care and can be used by systems, providers, and patients [ 2 ]. At the time of this study (before COVID-19), examples of eHealth used by patients in Australia included searching for web-based health information, booking appointments on the web, participating in online peer-support health forums, using mobile phone health apps (mobile health), emailing health care providers, and patient portals for electronic health records.

Digital health is expected to improve chronic disease management and has already shown great potential in improving chronic disease health outcomes [ 3 , 4 ]. Just under half of the Australian population (47.3%) has at least 1 chronic disease [ 5 ]. Rates of chronic disease and complications from chronic disease are overrepresented among those with socioeconomic disadvantage [ 6 ]. Therefore, patients with chronic disease and socioeconomic disadvantage have a greater need for the potential benefits of digital health, such as an improvement in their health outcomes. However, there is a risk that those who could benefit most from digital health services are the least likely to receive them, exemplifying the inverse care law in the digital age by Hart [ 7 ].

Our Current Understanding of the Digital Health Divide

While the rapid growth of digital health brings the promise of health care equity, it may also intensify existing inequities [ 8 ]. The digital health divide for socioeconomic disadvantage describes a pattern in which patients considered socioeconomically disadvantaged who are already marginalized through poor access to traditional health care are additionally hindered through poor access to digital health [ 9 ]. In Australia, only 67.4% of households in the lowest household income quintile have home internet access, compared to 86% of the general population and 96.9% of households in the highest household income quintile [ 10 ]. Survey-based studies have also shown that even with internet access, effective eHealth use is lower in populations considered disadvantaged, which speaks to broader barriers to digital health access [ 11 ].

The ongoing COVID-19 global pandemic has sped up digital health transitions with the rapid uptake of telephone and video consultations, e-prescription, and the ongoing rollout of e-mental health in Australia. These have supported the continuation of health care delivery while limiting physical contact and the pandemic spread; however, the early evidence shows that the digital health divide remains problematic. A rapid review identified challenges with reduced digital access and digital literacy among the older adults and racial and ethnic minority groups, which are both groups at greater health risk from COVID-19 infections [ 12 ]. An Australian population study showed that the rapid uptake of telehealth during peak pandemic was not uniform, with the older adults, very young, and those with limited English language proficiency having a lower uptake of general practitioner (GP) telehealth services [ 13 ].

To ensure that digital health improves health care outcome gaps, it is essential to better understand the nature and nuance of the digital health divide for socioeconomic disadvantage. The nature of the digital health divide for socioeconomic disadvantage has been explored primarily through quantitative survey data, some qualitative papers, a few mixed methods papers, and systematic reviews [ 11 , 14 - 16 ]. Identified barriers include a lack of physical hardware and adequate internet bandwidth, a reduced inclination to seek out digital health, and a low ability and confidence to use digital health effectively [ 16 ]. The few mixed methods studies that exist on the digital health divide generally triangulate quantitative and qualitative data on a specific disease type or population subgroup to draw a combined conclusion [ 17 , 18 ]. These studies have found digital health access to be associated with education, ethnicity, and gender as well as trust, complementary face-to-face services, and the desire for alternative sources of information [ 17 , 19 ].

What This Work Adds

This project sought to extend previous research by using an exploratory mixed methods design to ensure that the first step and driver of our survey of a larger population was primarily shaped by the experiences of our interviewees within primary care. This differs from the triangulation method, which places the qualitative and quantitative data as equal first contributors to the findings and does not allow one type of data to determine the direction of the other [ 18 ]. We qualitatively explored the experience of digital health for patients with socioeconomic disadvantage and chronic disease and then quantitatively measured some of the qualitative findings via a survey of the Australian general practice patient population. Our key objective was to provide comprehensive primary mixed methods data, describing the experience and extent of barriers to accessing digital health and its benefits, with a focus on the digital health divide. We completed this research in a primary care context to investigate a diverse community-based population with conceivable reasons to seek digital help in managing their health. Findings from this mixed methods study were intended to provide health care providers and policy makers with a more detailed understanding of how specific barriers affect different aspects or steps of accessing digital health. Ultimately, understanding digital health access can influence the future design and implementation of digital health services by more effectively avoiding certain barriers or building in enablers to achieve improved digital health access not only for everyone but also especially for those in need.

Study Design

We conducted a sequential exploratory mixed methods study to explore a complex phenomenon in depth and then measure its prevalence. We qualitatively explored the experience of digital health for patients with chronic disease and socioeconomic disadvantage in the first phase. Data from the first phase informed a quantitative survey of the phenomenon across a wider population in the second phase [ 18 ]. Both stages of research were conducted before the COVID-19 pandemic in Australia.

Recruitment

Qualitative phase participants.

The eligibility criteria for the qualitative phase were as follows: English-speaking adults aged ≥18 years with at least 1 self-reported chronic disease and 1 marker of socioeconomic disadvantage (indicated by ownership of a Health Care Card or receiving a disability pension, unemployment, or a user of public housing). A chronic disease was defined to potential participants as a diagnosed long-term health condition that had lasted at least 6 months (or is expected to last for at least 6 months; examples are listed in Multimedia Appendix 1 ). The markers of socioeconomic disadvantage we used to identify potential participants were based on criteria typically used by local general practices to determine which patients can have lower or no out-of-pocket expenses. Apart from unemployment, the 3 other criteria to identify socioeconomic disadvantage are means-tested government-allocated public social services [ 20 ]. Qualitative phase participants were recruited from May to July 2019 through 3 general practices and 1 service organization that serve populations considered socioeconomically disadvantaged across urban, regional, and rural regions in the Australian Capital Territory and South Eastern New South Wales. A total of 2 recruitment methods were used in consultation with and as per the choice of the participating organizations. Potential participants were either provided with an opportunity to engage with researchers (KB and MAC) in the general practice waiting room or identified by the practice or organization as suitable for an interview. Interested participants were given a detailed verbal and written description of the project in a private space before providing written consent to be interviewed. All interview participants received an Aus $50 (US $32.68) grocery shopping voucher in acknowledgment of their time.

Quantitative Phase Participants

Eligibility for the quantitative phase was English-speaking adults aged ≥18 years. The eligibility criteria for the quantitative phase were deliberately broader than those for the qualitative phase to achieve a larger sample size within the limitations of recruitment and with the intention that the factors of socioeconomic disadvantage and having a chronic disease could be compared to the digital health access of a more general population. The quantitative phase participants were recruited from November 2019 to February 2020. Study information and paper-based surveys were distributed and collected through 24 general practices across the Australian Capital Territory and South Eastern New South Wales regions, with an option for web-based completion.

Ethical Considerations

Qualitative and quantitative phase research protocols, including the participant information sheet, were approved by the Australian Capital Territory Health Human Research Ethics Committee (2019/ETH/00013) and the Australian National University Human Research Ethics Committee (2019/ETH00003). Qualitative phase participants were given a verbal and written explanation of the study, including how and when they could opt out, before they provided written consent. All interview participants received an Aus $50 (US $32.68) grocery shopping voucher in acknowledgment of their time. Quantitative participants were given a written explanation and their informed consent was implied by return of a completed survey. Participants in both phases of the study were told that all their data was deidentified. Consent was implied through the return of a completed survey.

Qualitative Data Collection and Analysis

Participants were purposively sampled to represent a range in age, gender, degree of socioeconomic disadvantage, and experience of digital health. The sampling and sample size were reviewed regularly by the research team as the interviews were being completed to identify potential thematic saturation.

The interview guide was developed by the research team based on a review of the literature and the patient dimensions of the framework of access by Levesque et al [ 21 ]. The framework by Levesque et al [ 21 ] is a conceptualization of health care access comprising 5 service and patient dimensions of accessibility and ability. The patient dimensions are as follows: (1) ability to perceive, (2) ability to seek, (3) ability to reach, (4) ability to pay, and (5) ability to engage [ 21 ]. The key interview topics included (1) digital health use and access, including facilitators and barriers; (2) attitudes toward digital health; and (3) self-perception of digital health skills and potential training. The interview guide was reviewed for face and content validity by the whole research team, a patient advocate, a digital inclusion charity representative, and the general practices where recruitment occurred. The questions and guide were iteratively refined by the research team to ensure relevance and support reaching data saturation. The interview guide has been provided as Multimedia Appendix 1 . The interviews, which took 45 minutes on average, were taped and transcribed. An interview summary sheet and reflective journal were completed by the interviewer after each interview to also capture nonverbal cues and tone.

Interview transcriptions were coded and processed by inductive thematic analysis. Data collection and analysis were completed in parallel to support the identification of data saturation. Data saturation was defined as no significant new information arising from new interviews and was identified by discussion with the research team [ 22 ]. The 2 interviewers (MAC and KB) independently coded the first 5 transcripts and reflected on them with another researcher (EAS) to ensure intercoder validity and reliability. The rest of the interviews were coded independently by the 2 interviewers, who regularly met to reflect on emerging themes and thematic saturation. Data saturation was initially indicated after 15 interviews and subsequently confirmed with a total of 19 interviews. Coding disagreements and theme development were discussed with at least 1 other researcher (EAS, ER, and KD). Thematic saturation and the final themes were agreed upon by the entire research team.

Quantitative Survey Development

The final themes derived in the qualitative phase of the project guided the specific quantitative phase research questions. The final themes were a list of ordered cumulative barriers experienced by participants in accessing digital health and its benefits ( Figure 1 ). The quantitative survey was designed to test the association between barriers to access and the frequency of use of digital health as a proxy measure for digital health access.

In the survey, the participants were asked about their demographic details, health and chronic diseases, knowledge, use and experience of digital health tools, internet access, perception of digital resource affordability, trust in digital health and traditional health services, perceived capability, health care empowerment, eHealth literacy, and relationship with their GP.

Existing scales and questions from the literature and standardized Australian-based surveys were used whenever possible. We used selected questions and scales from the Australian Bureau of Statistics standards, the eHealth Literacy Scale (eHEALS), the eHealth Literacy Questionnaire, and the Southgate Institute for Health Society and Equity [ 17 , 23 - 26 ]. We adapted other scales from the ICEpop Capability Measure for Adults, the Health Care Empowerment Inventory (HCEI), the Patient-Doctor Relationship Questionnaire, and the Chao continuity questionnaire [ 23 , 27 - 29 ]. Where an existing scale to measure a barrier or theme did not exist, the research team designed the questions based on the literature. Our questions around the frequency of digital health use were informed by multiple existing Australian-based surveys on general technology use [ 30 , 31 ]. Most of the questions used a Likert scale. Every choice regarding the design, adaptation, or copy of questions for the survey was influenced by the qualitative findings and decided on by full agreement among the 2 researchers who completed and coded the interviews. A complete copy of the survey is provided in Multimedia Appendix 2 .

Pilot-testing of the survey was completed with 5 patients, 2 experts on digital inclusion, and 3 local GPs for both the paper surveys and web-based surveys via Qualtrics Core XM (Qualtrics LLC). The resulting feedback on face and content validity, functionality of the survey logic, and feasibility of questionnaire completion was incorporated into the final version of the survey.

The survey was offered on paper with a participant information sheet, which gave the patients the option to complete the web-based survey. The survey was handed out to every patient on paper to avoid sampling bias through the exclusion of participants who could not complete the web-based survey [ 32 ].

Quantitative Data Treatment and Analysis

Data were exported from Qualtrics Core XM to an SPSS (version 26; IBM Corp) data set. Data cleaning and screening were undertaken (KB and KO).

Descriptive statistics (number and percentage) were used to summarize participant characteristics, preference measures, and frequency of eHealth use. Significance testing was conducted using chi-square tests, with a threshold of P <.05; effect sizes were measured by the φ coefficient for 2×2 comparisons and Cramer V statistic for all others. Where the cells sizes were too small, the categories were collapsed for the purposes of significance testing. The interpretation of effect sizes was as per the study by Cohen [ 33 ]. The analysis was conducted in SPSS and SAS (version 9.4; SAS Institute).

Participant Characteristics

Participants’ self-reported characteristics included gender, indigenous status, income category, highest level of education, marital status, and language spoken at home.

Age was derived from participant-reported year of birth and year of survey completion as of 2019 and stratified into age groups. The state or territory of residence was derived from the participant-reported postcode. The remoteness area was derived using the postcode reported by the participants and mapped to a modified concordance from the Australian Bureau of Statistics. Occupation-free text responses were coded using the Australian Bureau of Statistics Census statistics level 1 and 2 descriptors. The country of birth was mapped to Australia, other Organisation for Economic Cooperation and Development countries, and non–Organisation for Economic Cooperation and Development countries.

Frequency of eHealth Use

A summary measure of the frequency of eHealth use was derived from the questions on the use of different types of eHealth.

Specifically, respondents were asked if they had ever used any form of web-based health (“eHealth“) and, if so, to rate how often (never, at least once, every now and then, and most days) against 6 types of “eHealth” (searching for health information online, booking appointments online, emailing health care providers, using health-related mobile phone apps, accessing My Health Record, and accessing online health forums). The frequency of eHealth use was then classified as follows:

• High user: answered “most days” to at least 1 question on eHealth use OR answered “every now and then” to at least 2 questions on eHealth use
• Never user: answered “no” to having ever used any form of eHealth OR “never” to all 6 questions on eHealth use
• Low or medium user: all other respondents.

The frequency of eHealth use was reported as unweighted descriptive statistics (counts and percentages) against demographic characteristics and for the elements of each of the themes identified in phase 1.

Overview of Key Themes

Data were reported against the 6 themes from the phase 1 results of preference, trust, cost, structural access, capacity to engage, and self-efficacy. Where the components of trust, cost, capacity to engage, and self-efficacy had missing data (for less than half of the components only), mean imputation was used to minimize data loss. For each theme, the analysis excluded those for whom the frequency of eHealth use was unknown.

Preference measures (survey section D1 parts 1 to 3) asked participants to report against measures with a 4-point Likert scale (strongly disagree, disagree, agree, and strongly agree). Chi-square tests were conducted after the categories were condensed into 2 by combining strongly disagree and as well as combining strongly agree and agree.

Summary measures for trust were created in 4 domains: trust from the eHealth Literacy Questionnaire (survey section D1 parts 4 to 8), trust from Southgate—GPs, specialists, or allied health (survey section D2 parts 1 to 5), trust from Southgate—digital health (survey section D2 parts 6, 7, 9, and 10), and trust from Southgate—books or pamphlets (survey section D2 part 8). The data were grouped as low, moderate, and high trust based on the assigned scores from the component data. Chi-square tests were conducted comparing low-to-moderate trust against high trust for GP, specialists, or allied health and comparing low trust against moderate-to-high trust for book or pamphlet.

Summary measures for cost were created from survey item C10. To measure cost, participants were asked about whether they considered certain items or services to be affordable. These included cost items mentioned in the qualitative phase interviews relating to mobile phones (1 that connects to the internet, 1 with enough memory space to download apps, downloads or apps requiring payment, repairs, and maintenance costs), having an iPad or tablet with internet connectivity, a home computer or laptop (owning, repairs, and maintenance), home fixed internet access, and an adequate monthly data allowance. These 9 items were scored as “yes definitely”=1 or 0 otherwise. Chi-square tests were conducted with never and low or medium eHealth users combined.

Structural Access

Structural access included asking where the internet is used by participants (survey section C8) and factors relating to internet access (survey section C8 parts 1-3) reporting against a 4-point Likert scale (strongly disagree, disagree, agree, and strongly agree). Chi-square tests were conducted with strongly disagree, disagree, agree, or strongly agree, and never, low, or medium eHealth use combined.

Capacity to Engage

Summary measures for capacity to engage were created from survey section E1. To measure the capacity to engage, participants were asked about feeling “settled and secure,” “being independent,” and “achievement and progress” as an adaptation of the ICEpop Capability Measure for Adults [ 27 ], reporting against a 4-point Likert-like scale. Responses were scored from 1 (“I am unable to feel settled and secure in any areas of my life”) to 4 (“I am able to feel settled and secure in all areas of my life”).

The summary capacity measure was derived by the summation of responses across the 3 questions, which were classified into 4 groups, A to D, based on these scores. Where fewer than half of the responses were missing, mean imputation was used; otherwise, the record was excluded. Groups A and B were combined for significance testing.

Self-Efficacy

Summary measures for self-efficacy were adapted from the eHEALS (E3) and the HCEI (E2) [ 23 , 24 ].

Survey section E3—eHEALS—comprised 8 questions, with participants reporting against a 5-point Likert scale for each (strongly disagree, disagree, neither, agree, and strongly agree). These responses were assigned 1 to 5 points, respectively. The summary eHEALS measure was derived by the summation of responses across the 8 questions, which were classified into 5 groups, A to E, based on these scores. Where fewer than half of the responses were missing, mean imputation was used; otherwise, the record was excluded. Groups A to C and D to E were combined for significance testing.

Survey section E2—HCEI—comprised 5 questions, with participants reporting against a 5-point Likert scale for each (strongly disagree, disagree, neither, agree, and strongly agree). Strongly disagree and disagree and neither were combined, and similarly agree and strongly agree were combined for significance testing.

Qualitative Results

The demographic characteristics of the patients that we interviewed are presented in Table 1 .

The key barriers found to accessing digital health included (1) strong patient preference for human-based health services; (2) low trust in digital health services; (3) high financial costs of necessary tools, maintenance, and repairs; (4) poor publicly available internet access options; (5) reduced capacity to engage due to increased life pressures; and (6) low self-efficacy and confidence in using digital health.

Rather than being an equal list of factors, our interviewees described these barriers as a stepped series of cumulative hurdles, which is illustrated in Figure 1 . Initial issues of preference and trust were foundational to a person even when considering the option of digital health, while digital health confidence and literacy were barriers to full engagement with and optimal use of digital health. Alternatively, interviewees who did use digital health had been enabled by the same factors that were barriers to others.

a GP: general practitioner.

b Multiple answers per respondent.

Strong Patient Preference for Human-Based Health Services

Some patients expressed a strong preference for human-based health services rather than digital health services. In answer to a question about how digital health services could be improved, a patient said the following:

Well, having an option where you can actually bypass actually having to go through the app and actually talk directly to someone. [Participant #10]

For some patients, this preference for human-based health services appeared to be related to a lack of exposure to eHealth. These patients were not at all interested in or had never thought about digital health options. A participant responded the following to the interviewer’s questions:

Interviewer: So when...something feels not right, how do you find out what’s going on?

Respondent: I talk to Doctor XX.

Interviewer: Do you ever Google your symptoms or look online for information?

Respondent: No, I have never even thought of doing that actually. [Participant #11]

For other patients, their preference for human-based health care stemmed from negative experiences with technology. These patients reported actively disliking computers and technology in general and were generally frustrated with what they saw as the pitfalls of technology. A patient stated the following:

If computers and internet weren’t so frigging slow because everything is on like the slowest speed network ever and there’s ads blocking everything. Ads, (expletive) ads. [Participant #9]

A patient felt that he was pushed out of the workforce due his inability to keep up with technology-based changes and thus made a decision to never own a computer:

But, you know, in those days when I was a lot younger those sorts of things weren’t about and they’re just going ahead in leaps and bounds and that’s one of the reasons why I retired early. I retired at 63 because it was just moving too fast and it’s all computers and all those sorts of things and I just couldn’t keep up. [Participant #17]

Low Trust in Digital Health Services

Several patients described low trust levels for digital and internet-based technology in general. Their low trust was generally based on stories they had heard of other people’s negative experiences. A patient said the following:

I don’t trust the internet to be quite honest. You hear all these stories about people getting ripped off and I’ve worked too hard to get what I’ve got rather than let some clown get it on the internet for me. [Participant #11]

Some of this distrust was specific to eHealth. For example, some patients were highly suspicious of the government’s motives with regard to digital health and were concerned about the privacy of their health information, which made them hesitant about the concept of a universal electronic health record. In response to the interviewer’s question, a participant said the following:

Interviewer: Are there any other ways you think that eHealth might help you?

Respondent: I’m sorry but it just keeps coming back to me, Big Brother. [Participant #7]

Another participant said the following:

I just would run a mile from it because I just wouldn’t trust it. It wouldn’t be used to, as I said, for insurance or job information. [Participant #16]

High Financial Costs of the Necessary Tools, Maintenance, and Repairs

A wide variety of patients described affordability issues across several different aspects of the costs involved in digital health. They expressed difficulty in paying for the following items: a mobile phone that could connect to the internet, a mobile phone with enough memory space to download apps, mobile phone apps requiring extra payment without advertisements, mobile phone repair costs such as a broken screen, a computer or laptop, home internet access, and adequate monthly data allowance and speeds to functionally use the internet. Current popular payment systems, such as plans, were not feasible for some patients. A participant stated the following:

I don’t have a computer...I’m not in the income bracket to own a computer really. Like I could, if I got one on a plan kind of thing or if I saved up for x-amount of time. But then like if I was going on the plan I’d be paying interest for having it on like lay-buy kind of thing, paying it off, and if it ever got lost or stolen I would still have to repay that off, which is always a hassle. And yeah. Yeah, I’m like financially not in the state where I’m able to...own a computer right now as I’m kind of paying off a number of debts. [Participant #9]

Poor Publicly Available Internet Access Options

Some patients described struggling without home internet access. While they noted some cost-free public internet access points, such as libraries, hotel bars, and restaurants, they often found these to be inconvenient, lacking in privacy, and constituting low-quality options for digital health. A patient stated the following:

...it’s incredibly slow at the library. And I know why...a friend I went to school with used to belong to the council and the way they set it up, they just got the raw end of the stick and it is really, really slow. It’s bizarre but you can go to the X Hotel and it’s heaps quicker. [Participant #15]

In response to the interviewer's question, a participant said the following:

Interviewer: And do you feel comfortable doing private stuff on computers at the library...?

Respondent: Not really, no, but I don’t have any other choice, so, yeah. [Participant #9]

Reduced Capacity to Engage Due to Increased Life Pressures

When discussing why they were not using digital health or why they had stopped using digital health, patients often described significant competing priorities and life pressures that affected their capacity to engage. An unemployed patient mentioned that his time and energy on the internet were focused primarily on finding work and that he barely had time to focus on his health in general, let alone engage in digital health.

Other patients reported that they often felt that their ability to learn about and spend time on digital health was taken up by caring for sick family members, paying basic bills, or learning English. Some patients said that the time they would have spent learning digital skills when they were growing up had been lost to adverse life circumstances such as being in jail:

So we didn’t have computers in the house when I was growing up. And I didn’t know I’ve never...I’ve been in and out of jail for 28 odd years so it sort of takes away from learning from this cause it’s a whole different… it’s a whole different way of using a telephone from a prison. [Participant #11]

Low Self-Efficacy and Confidence in Starting the Digital Health Process

Some patients had a pervasive self-perception of being slow learners and being unable to use technology. Their stories of being unconfident learners seemed to stem from the fact that they had been told throughout their lives that they were intellectually behind. A patient said the following:

The computer people...wouldn’t take my calls because I’ve always been dumb with that sort of stuff. Like I only found out this later on in life, but I’m actually severely numerically dyslexic. Like I have to triple-check everything with numbers. [Participant #7]

Another patient stated the following:

I like went to two English classes like a normal English class with all the kids and then another English class with about seven kids in there because I just couldn’t I don’t know maybe because I spoke another language at home and they sort of like know I was a bit backward. [Participant #6]

These patients and others had multiple missing pieces of information that they felt made it harder to engage in digital health compared to “easier” human-based services. A patient said the following:

Yeah I’ve heard of booking online but I just I don’t know I find it easier just to ring up. And I’ll answer an email from a health care provider but I wouldn’t know where to start to look for their email address. [Participant #11]

In contrast, the patients who did connect with digital health described themselves as independent question askers and proactive people. Even when they did not know how to use a specific digital health tool, they were confident in attempting to and asking for help when they needed it. A patient said the following:

I’m a “I will find my way through this, no matter how long it takes me” kind of person. So maybe it’s more my personality...If I have to ask for help from somewhere, wherever it is, I will definitely do that. [Participant #3]

Quantitative Results

A total of 487 valid survey responses were received from participants across 24 general practices. The participant characteristics are presented in detail in Table S1 in Multimedia Appendix 3 .

The mean age of the participants was approximately 50 years (females 48.9, SD 19.4 years; males 52.8, SD 20.0 years), and 68.2% (332/487) of the participants identified as female. Overall, 34.3% (151/439) of respondents reported never using eHealth, and 53.8% (236/439) reported high eHealth use.

There were statistically significant ( P <.05) differences in the frequency of eHealth use in terms of age group, gender, state, remoteness, highest level of education, employment status, occupation group, marital status, and language spoken at home, with effect sizes being small to medium. Specifically, high eHealth characteristics were associated with younger age, being female, living in an urban area, and being employed.

Table 2 presents the frequency of eHealth use against 3 internet preference questions.

Preference for using the internet and technology in general and for health needs in particular were significantly related to the frequency of eHealth use ( P <.05 for each), with the effect sizes being small to medium.

a Excludes those for whom frequency of eHealth use is unknown.

b Chi-square tests conducted with strongly disagree and disagree combined, and agree and strongly agree combined.

Table 3 presents the frequency of eHealth use against 4 measures of trust.

The degree of trust was not statistically significantly different for the frequency of eHealth use for any of the domains.

b eHLQ: eHealth Literacy Questionnaire.

c Derived from survey question D1, parts 4 to 8. Mean imputation used where ≤2 responses were missing. If >2 responses were missing, the records were excluded.

d Derived from survey question D2, parts 1 to 5. Mean imputation used where ≤2 responses were missing. If >2 responses were missing, the records were excluded.

e Chi-square test conducted comparing low-to-moderate trust against high trust.

f Derived from survey question D2, parts 6, 7, 9, and 10. Mean imputation used where ≤2 responses were missing. If >2 responses were missing, the records were excluded.

g Derived from survey question D2 part 8.

h Chi-square test conducted comparing low trust against moderate-to-high trust.

Affordability of items and services was reported as No cost difficulty or Cost difficulty. eHealth frequency of use responses were available for 273 participants; among those with no cost difficulty , 1% (2/204) were never users, 14.2% (29/204) were low or medium users, and 84.8% (173/204) were high users of eHealth; among those with cost difficulty , 1% (1/69) were never users, 26% (18/69) were low or medium users, and 73% (50/69) were high users. There was a statistically significant difference in the presence of cost as a barrier between never and low or medium eHealth users compared to high users ( χ 2 1 =5.25; P =.02), although the effect size was small.

Table 4 presents the frequency of eHealth use for elements of structural access.

Quality of internet access and feeling limited in access to the internet were significantly associated with frequency of eHealth use ( P <.05), although the effect sizes were small.

b N/A: not applicable (cell sizes insufficient for chi-square test).

c Chi-square tests conducted with strongly disagree and disagree combined, agree and strongly agree combined, and never and low or medium categories combined.

Table 5 presents the frequency of eHealth use against respondents’ capacity to engage.

Capacity to engage was not significantly different for the frequency of eHealth use ( P =.54). 

b Derived from survey item E1. Where 1 response was missing, the mean imputation was used. If >1 response was missing, the record was excluded.

c Chi-square tests conducted with groups A and B combined.

Table 6 presents the frequency of eHealth use for elements of self-efficacy.

Statistically significant results were observed for the relationship between self-efficacy by eHEALS (moderate effect size) and frequency of eHealth use as well as for some of the questions from the HCEI (reliance on health professionals or others to access and explain information; small effect size; P <.05).

b eHEALS: eHealth Literacy Scale.

c eHEALS derived from item E3 (8 parts). Where ≤ 4 responses were missing, mean imputation was used. If >4 responses were missing, the records were excluded. Groups A to C as well as groups D to E were combined for the chi-square test.

d Strongly disagree, disagree, neither, and agree or strongly agree combined for significance testing.

Principal Findings

This paper reports on the findings of a sequential exploratory mixed methods study on the barriers to digital health access for a group of patients in Australian family medicine, with a particular focus on chronic disease and socioeconomic disadvantage.

In the qualitative first phase, the patients with socioeconomic disadvantage and chronic disease described 6 cumulative barriers, as demonstrated in Figure 1 . Many nonusers of digital health preferred human-based services and were not interested in technology, while others were highly suspicious of the technology in general. Some digitally interested patients could not afford quality hardware and internet connectivity, a barrier that was doubled by low quality and privacy when accessing publicly available internet connections. Furthermore, although some digitally interested patients had internet access, their urgent life circumstances left scarce opportunity to access digital health and develop digital health skills and confidence.

In our quantitative second phase, 31% (151/487) of the survey participants from Australian general practices were found to have never used a form of digital health. Survey participants were more likely to use digital health tools frequently when they also had a general digital interest and a digital health interest. Those who did not frequently access digital health were more likely to report difficulty affording the financial costs needed for digital access. The survey participants who frequently accessed digital health were more likely to have high eHealth literacy and high levels of patient empowerment.

Comparison With Prior Work

In terms of general digital health access, the finding that 31% (151/487) of the survey participants had never used one of the described forms of eHealth is in keeping with an Australian-based general digital participation study that found that approximately 9% of the participants were nonusers and 17% rarely engaged with the internet at all [ 34 ]. With regard to the digital health divide, another Australian-based digital health divide study found that increased age, living in a lower socioeconomic area, being Aboriginal or Torres Strait Islander, being male, and having no tertiary education were factors negatively associated with access to digital health services [ 17 ]. Their findings correspond to our findings that higher-frequency users of eHealth were associated with younger age, being female, living in an urban area, and being employed. Both studies reinforce the evidence of the digital health divide based on gender, age, and socioeconomic disadvantage in Australia.

With regard to digital health barriers, our findings provide expanded details on the range of digital health items and services that present a cost barrier to consumers. Affordability is a known factor in digital access and digital health access, and it is measured often by general self-report or relative expenditure on internet access to income [ 30 ]. Our study revealed the comprehensive list of relevant costs for patients. Our study also demonstrated factors of cost affordability beyond the dollar value of an item, as interviewees described the struggle of using slow public internet access without privacy features and the risks involved in buying a computer in installments. When we reflected on the complexity and detail of the cost barrier in our survey, participants demonstrated a clear association between cost and the frequency of digital health use. This suggests that a way to improve digital health access for some people is to improve the quality, security, and accessibility of public internet access options as well as to provide free or subsidized hardware, internet connection, and maintenance options for those in need, work that is being done by at least 1 digital inclusion charity in the United Kingdom [ 35 ].

Many studies recognize the factors of eHealth literacy and digital confidence for beneficial digital health access [ 36 ]. Our interviews demonstrated that some patients with socioeconomic disadvantage have low digital confidence, but that this is often underlined by a socially reinforced lifelong low self-confidence in their intellectual ability. In contrast, active users, regardless of other demographic factors, described themselves as innately proactive question askers. This was reinforced by our finding of a relationship between health care empowerment and the frequency of eHealth use. This suggests that while digital health education and eHealth literacy programs can improve access for some patients, broader and deeper long-term solutions addressing socioeconomic drivers of digital exclusion are needed to improve digital health access for some patients with socioeconomic disadvantage [ 8 ]. The deep permeation of socially enforced low self-confidence and lifelong poverty experienced by some interviewees demonstrate that the provision of free hardware and a class on digital health skills can be, for some, a superficial offering when the key underlying factor is persistent general socioeconomic inequality.

The digital health divide literature tends to identify the digital health divide, the factors and barriers that contribute to it, and the potential for it to widen if not specifically addressed [ 16 ]. Our findings have also identified the divide and the barriers, but what this study adds through our qualitative phase in particular is a description of the complex interaction of those barriers and the stepped nature of some of those barriers as part of the individual’s experience in trying to access digital health.

Strengths and Limitations

A key strength of this study is the use of a sequential exploratory mixed methods design. The initial qualitative phase guided a phenomenological exploration of digital health access experiences for patients with chronic disease and socioeconomic disadvantage. Our results in both study phases stem from the patients’ real-life experiences of digital health access. While some of our results echo the findings of other survey-based studies on general digital and digital health participation, our method revealed a greater depth and detail of some of these barriers, as demonstrated in how our findings compare to prior work.

As mentioned previously, the emphasis of this study on the qualitative first phase is a strength that helped describe the interactions between different barriers. The interviewees described their experiences as cumulative unequal stepped barriers rather than as producing a nonordered list of equal barriers. These findings expand on the known complexity of the issue of digital exclusion and add weight to the understanding that improving digital health access needs diverse, complex solutions [ 17 ]. There is no panacea for every individual’s digital health access, and thus, patient-centered digital health services, often guided by health professionals within the continuity of primary care, are also required to address the digital health divide [ 37 ].

While the sequential exploratory design is a strength of the study, it also created some limitations for the second quantitative phase. Our commitment to using the qualitative interview findings to inform the survey questions meant that we were unable to use previously validated scales for every question and that our results were less likely to lead to a normal distribution. This likely affected our ability to demonstrate significant associations for some barriers. We expect that further modeling is required to control for baseline characteristics and determine barrier patterns for different types of users.

One strength of this study is that the survey was administered to a broad population of Australian family medicine patients with diverse patterns of health via both paper-based and digital options. Many other digital health studies use solely digital surveys, which can affect the sample. However, we cannot draw conclusions from our survey about patients with chronic disease due to the limitations of the sample size for these subgroups.

Another sample-based limitation of this study was that our qualitative population did not include anyone aged from 18 to 24 years, despite multiple efforts to recruit. Future research will hopefully address this demographic more specifically.

While not strictly a limitation, we recognize that because this research was before COVID-19, it did not include questions about telehealth, which has become much more mainstream in recent years. The patients may also have changed their frequency of eHealth use because of COVID-19 and an increased reliance on digital services in general. Future work in this area or future versions of this survey should include telehealth and acknowledge the impact of COVID-19. However, the larger concept of the digital health divide exists before and after COVID-19, and in fact, our widespread increased reliance on digital services makes the digital divide an even more pressing issue [ 12 ].

Conclusions

The experience of digital health access across Australian primary care is highly variable and more difficult to access for those with socioeconomic disadvantage. While general digital interest, financial cost, and digital health literacy and empowerment are clear factors in digital health access in a broad primary care population, the digital health divide is also facilitated in part by a stepped series of complex and cumulative barriers.

Genuinely improving digital health access for 1 cohort or even 1 person requires a series of multiple different interventions tailored to specific sequential barriers. Given the rapid expansion of digital health during the global COVID-19 pandemic, attention to these issues is necessary if we are to avoid entrenching inequities in access to health care. Within primary care, patient-centered care that continues to recognize the complex individual needs of, and barriers facing, each patient should be a part of addressing the digital health divide.

Acknowledgments

The authors are thankful to the patients who shared their experiences with them via interview and survey completion. The authors are also very grateful to the general practices in the Australian Capital Territory and New South Wales who kindly gave their time and effort to help organize interviews, administer, and post surveys in the midst of the stress of day-to-day practice life and the bushfires of 2018-2019. The authors thank and acknowledge the creators of the eHealth Literacy Scale, the eHealth Literacy Questionnaire, the ICEpop Capability Measure for Adults, the Health Care Empowerment Inventory, the Patient-Doctor Relationship Questionnaire, the Chao continuity questionnaire, and the Southgate Institute for Health Society and Equity for their generosity in sharing their work with the authors [ 17 , 19 - 25 ]. This study would not have been possible without the support of the administrative team of the Academic Unit of General Practice. This project was funded by the Royal Australian College of General Practitioners (RACGP) through the RACGP Foundation IPN Medical Centres Grant, and the authors gratefully acknowledge their support.

Data Availability

The data sets generated during this study are not publicly available due to the nature of our original ethics approval but are available from the corresponding author on reasonable request.

Authors' Contributions

MAC acquired the funding, conceptualized the project, and organized interview recruitment. MAC and KB conducted interviews and analyzed the qualitative data. EAS, ER, and KD contributed to project planning, supervision and qualitative data analysis. MAC, KB and KO wrote the survey and planned quantitative data analysis. MAC and KB recruited practices for survey administration. KO and KB conducted the quantitative data analysis. MAC and KO, with KB drafted the paper. EAS, ER, and KD helped with reviewing and editing the paper.

Conflicts of Interest

None declared.

Phase 1 interview guide.

Phase 2 survey: eHealth and digital divide.

Phase 2 participant characteristics by frequency of eHealth use.

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Abbreviations

Edited by T Leung; submitted 03.07.23; peer-reviewed by T Freeman, H Shen; comments to author 16.08.23; revised version received 30.11.23; accepted 31.01.24; published 11.04.24.

©Melinda Ada Choy, Kathleen O'Brien, Katelyn Barnes, Elizabeth Ann Sturgiss, Elizabeth Rieger, Kirsty Douglas. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2024.

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