The role of flexibility and complexity in response to regulatory change: A case study of innovation in a major Danish financial institution

Abstract

The link between regulations and innovation is puzzling. Some studies point to higher innovation performance as an effect of regulation, whereas other researchers disagree. A literature review shows empirical inquiries into various industries, with the financial sector attracting significant attention. The review also points to a company’s responses’ flexibility and complexity as variables mediating the relationship between regulation and innovation performance. These variables remain underexplored as empirical objects of analysis on a company level in the financial sector. By applying a case study research strategy, 100 launched financial service innovations’ performance is compared with qualitative data assessing flexibility and complexity in the project work, leading to the launch of these products into the marketplace by a major Danish financial company. Finally, these data are quantitatively tested with a multinomial logit model. The results contribute to the differing views on how regulations influence innovation by showing links between high flexibility and low complexity in firm response for improved innovation performance. Increased complexity, in turn, impedes performance. Hence, specific innovation efforts from management are critical for striking the right balance between flexibility and complexity to achieve success in connection with regulatory changes.

Keywords

complexity flexibility innovation innovation management regulation

Introduction

A question explored by scholars generating differing results is: What are the effects of regulations on innovation performance? Prior research has suggested that regulations affect innovation activities in companies (Ashford and Heaton, 1983; Merton, 1995; Smet, 2012; Taylor et al., 2005) and proposed different types of relationships. Among the potential adverse effects of regulations are reductions in new services offered to consumers. Prieger (2002) claims that consumers would have received more service if rules had not been in operation, while Jaffe and Palmer (1997) suggest that environmental regulations might lead to incremental innovation. Stewart (1981) proposes that regulation causes uncertainty, additional cost, and delays. Others indicate that regulations are essential for innovation (Jackson, 2007; Miller, 1986; Rossignoli and Arnaboldi, 2009; Silber, 1983).

Some researchers even claim that (‘disruptive’) regulators are the main force behind innovation (Pilkington and Dyerson, 2004) and that regulations are a force required to bring technical innovations to the market (Teece, 2006, 2018). Regulations can be at the centre of battles for market position between incumbents and entrepreneurs (Gurses and Ozcan, 2015). The results of how regulatory categorisation of products are established can determine the winners in new markets (Ozcan and Gurses, 2018). The engagement in regulatory propaganda can lead to innovation benefits for firms (Dobusch and Schüßler, 2014). Finally, some researchers point to potential mixed negative and positive aspects, for example, Majumdar and Marcus (2001) and Frame and White (2004) who indicate that regulations might impede or foster innovation in different situations but − in line with Cetindamar (2001) − ask for further research on the topic.

Empirically, scholars investigate different industries in dispersed regions: U.S. chemical industry (Ashford and Heaton, 1983), Indian pharmaceutical sector (Athreye et al., 2009), Turkish fertiliser industry (Cetindamar, 2001), German biodegradable materials (Delaplace and Kabouya, 2006), U.S. electric utilities (Majumdar and Marcus, 2001), U.S. SO2 power plant emissions (Taylor et al., 2005), durable goods industries (Salvador et al., 2002), the Grand-Duchy of Luxembourg and E.U. regulation (Smet, 2012), whereas some scholars focus on many sectors (Jaffe and Palmer, 1997; Jaspers et al., 2012).

A bulk of literature focuses on the financial services industry (Frame and White, 2004; Jackson, 2007; Jagtiani et al., 1995; Mention and Torkkeli, 2012; Merton, 1995; Miller, 1986; Ozcan and Santos, 2014; Rossignoli and Arnaboldi, 2009; Silber, 1983; Warren, 2008). The financial services industry is a ripe field for studying the impact of innovation from regulations (Naudé et al., 1998).

Rules play a central role in defining how firms act in an industry (Durkin et al., 2014; Smets et al., 2015). Avlonitis et al. (2001) investigate the relationship between financial success and innovativeness in the Greek banking industry. They explore how internal management devices like a formalisation of the innovation process connects with six types of service innovation and financial performance but not external regulation. The effects are valid for regulation as well as deregulation (Funk and Hirschman, 2014). A robust regulatory framework is central to the potential for innovation in the financial services industry (Salampasis et al., 2014). Since the first modern financial institutions were established (mid-18th century), regulations have evolved towards increased complexity and potential for innovation (Freij, 2018). This industry has even been characterised as a disaster-based, reckless institution (Jacobides and Winter, 2010). The evolution has gone from unregulated to a phase of necessary regulations, and then extensive regulation of products and processes. Cooper and de Brentani (1991) and de Brentani and Cooper (1992) identify success factors for innovation in the financial services industry but do not explore the role of regulations.

This paper is organised in the following way: First, a review of research on the relationship between innovation and regulation is conducted. Second, flexibility and complexity are deduced as influencing factors in the connection between regulations and innovations. Third, data from a study is presented where the impact of flexibility and complexity are validated in cooperation with the case company. These variables are then applied to 100 new product and service innovations to investigate the effects of regulation. In the empirical data, the new innovation projects are analysed based on data from company documents, workshops, and interviews. Finally, the results are presented, discussed, and limitations are offered.

Regulations and innovation

Regulations have an impact on firms and organisations (Ungson et al., 1985). There are different models regulators use to influence firms and markets (Jensen and Wu, 2017). The actions of regulators create potential asymmetries and inefficiencies (Dassler et al., 2006). Regulators can mitigate the situation by providing information across networks with other institutions (Majone, 1997). The relationship between a regulator and the regulated firms influences the effect of regulations across an industry (Thatcher, 2002). The impact on firms and influence wielded by firms regarding changing regulations is difficult to project and manage (Hadani et al., 2017).

Extant studies of regulations and innovation suggest that external regulation has an effect on innovation activities in companies but also points to different types of relationships. Observing the US broadcasting industry, Funk (2015) finds it unclear if regulatory change creates or reduces entrepreneurial opportunity. Many researchers find that external regulation affects innovation activities in companies (Ashford and Heaton, 1983; Merton, 1995; Smet, 2012; Taylor et al., 2005). Ashford and Heaton (1983) suggest that regulations create a need for companies to be in compliance that often requires a change in technology. Strategic regulatory factors are central to driving innovation (Streak and Urban, 2013). Regulations will affect the company by changing the patterns of activity outside the firm and thereby establish new forms of competition. There is a link between regulations and the innovation potential of new products and services (Huesig et al., 2014).

Therefore, there is a link between external governmental regulation and internal innovation action. Similarly, Merton (1995) points to an interaction between government regulations and financial institutions, where governments deliberately affect financial institutions, while other effects are unintended. Smet (2012) proposes that regulations and innovation are linked since regulations are a part of both the external environment and a part of the organisation. Changes in regulations influence dynamic capabilities (Pettus et al., 2009). Regulations can be a driver of business model innovation (Berti and Casprini, 2018). Taylor et al. (2005) claim that government regulation can stimulate market innovation:

Government plays an important role in fostering knowledge transfer via technical conferences, as well as affecting the pattern of collaborative relationships within the technical research community via regulatory changes that affect the market for the technology. (p. 697)

However, researchers propose different types of relationships. Among those finding adverse effects is Prieger (2002) who discusses how external regulations hinders or stops product and innovation, he claims that firms in his study would have introduced 62% more services to consumers during the study period if the regulations had not been in place. Stewart (1981) suggests that regulations may adversely affect innovation in terms of imposing technical constraints on firms, forcing firms to make additional expenditures or outlays, causing uncertainty, and finally causing delays. Regulations can be a hindrance as they influence the possibilities for collaboration around innovation. Such influences are due to the different perceptions that firms have regarding the implications of regulatory frameworks (Jaspers et al., 2012). Based on a quantitative study, Jaffe and Palmer (1997) find that environmental regulation stimulates only certain types of innovation. Using a panel of manufacturing industries, they find that interaction between regulation and innovation is complex as the relationship between regulatory stringency and innovative activity depends on which measure of innovative activity is employed:

(…) lagged environmental compliance expenditures have a significant positive association with R&D expenditures when we control for unobserved industry specific effects. These results indicate that increases in compliance expenditures within an industry are associated with increases in R&D shortly thereafter. We find little evidence, however, that industries’ inventive output is related to compliance costs. (p. 611)

Others suggest that external regulations are an important driver for innovation (Delaplace and Kabouya, 2006; Jackson, 2007; Mention and Torkkeli, 2012; Miller, 1986; Rossignoli and Arnaboldi, 2009; Silber, 1983). Miller (1986), for example, states, ‘The major impulse to successful innovation over the past twenty years has come (…) from regulations and taxes’ (p. 460). Silber (1983) suggests that regulations play an important role in regards to financial innovation: ‘(…) new financial instruments or practices are innovated to lessen the financial constraints imposed on firms’ (p. 89). Silber (1983) finds that the argument that regulation fosters financial innovation is too narrow to explain the entire innovation process, however, he does not doubt that regulations have an essential role to play when it comes to innovation. Rossignoli and Arnaboldi (2009) propose changes or modifications to regulations as one of the main drivers for change in the financial services industry. In studies of entrepreneurship some findings point to that regulation greatly affects the process through which new products come to market (Funk and Hirshman, 2014).

Therefore regulatory changes are an important element and provide regulation-induced innovation which is highly relevant for the innovativeness of a company. Regulations create a need for changes in the existing ways of working and require organisations to adapt to the external regulatory change (Rossignoli and Arnaboldi, 2009). Regulations might induce investments but not innovations (Cetindamar, 2001). He finds only minimal support for the claim that regulations have a positive effect on the firms’ innovativeness and competitiveness. It is also noted that a lack of clear regulations can hamper innovation and entrepreneurship due to actors not having a frame to direct investments towards (Ozcan and Santos, 2014). Clear regulations for institutions foster entrepreneurship, innovativeness, risk-taking and proactiveness (Urban, 2013, 2018). For entrepreneurial businesses to grow and thrive, sound legal and regulatory systems are needed (Nyarku and Oduro, 2018). On the other hand, non-uniform regulations can create opportunities for innovation (Wade et al., 1998).

Others, like Jackson (2007), call for more empirical evidence as the relationship exists and is important. Mention and Torkkeli (2012) points to the positive effects on the innovation process and performance outcomes like improved productivity. Some research points to potential mixed negative and positive aspects, e.g. Majumdar and Marcus (2001) and Frame and White (2004) who indicate that regulations might impede or foster innovation (2004) in different situations but − in line with Cetindamar (2001) – ask for further research on the topic. Athreye et al. (2009) observes that radical regulatory change generates ‘…both new economic opportunities and constraints in the wake of which the winners and losers were selected…’ (p. 3). Regulations can promote standardisation so as to encourage firms to sell the same product across national markets (Funk, 2003). At the same time, companies must often comply with different regulations and country-specific constraints, which might limit flexibility (Salvador et al., 2002). Majumdar and Marcus (2001) indicate that many scholars note that there are both positive and negative aspects to the growing legal regulation of organisational processes and structure, but more studies need to be developed regarding these two opposing views on regulations. Frame and White (2004) propose that regulation is a double-edged sword that can both foster and hamper innovation in the financial services industry. Jagtiani et al. (1995) found no relationship between more stringent capital requirements and innovations.

Finally, companies might have preferences for their position and their response to a regulatory change. There is a need to balance regulatory compliance and innovation (van den Broek and van Veenstra, 2018). Delaplace and Kabouya (2006) argue that companies individually affect the innovative outcome of a regulation by placing themselves either in advantage as a frontrunner or place themselves beyond the regulatory constraints. Firms can select to be more or less proactive in their relationship with regulators (Oliver and Holzinger, 2008). Striking a balance between innovation and entrepreneurship versus operating a stable business is a constant struggle (Penrose, 1959). Thus, companies might, by responding with more or less complexity and flexibility, be able to choose the effect regulations impose.

In conclusion, a relationship between regulation and innovativeness can be established. Nevertheless, the relationship is not uniquely defined, as the literature identifies both positive and negative aspects. Moreover, there is a call for more empirical enquiry, ‘since regulatory control of one sort or another is present in virtually all industries, this factor should receive more attention in the future’ (Wiseman and Catanach, 1997: 824), and also a synthesis that companies can choose how they want to deal with changes in regulations qualifying it as an innovation management problem.

Flexibility and complexity

Two variables are identified in the literature discussing regulations and innovation. Flexibility and complexity are found to have the potential to mediate the quality in company response and thereby performance (see Figure 1 below).

Flexibility in company response

A regulatory change can alter a previously stable system and introduce the need for flexibility (Abernathy and Clark, 1985). Regulators can drive new ways of developing products, designing services, and collaborating (Cacciatori and Jacobides, 2005). Ashford and Heaton (1983) suggest that a flexible and politically intelligent company that influences and responds quickly to its external environment can exploit regulatory opportunities. Some companies may even be able to meet regulatory requirements without significant technological change, while others suffer as the regulations hamper their competitiveness because of the differential ability for companies to absorb compliance costs. Ashford and Heaton (1983) suggest that flexible firms respond to regulation more quickly than others and that a larger company always has an advantage where the smaller companies suffer more under rules.

In particular industries, managers share views on how regulations influence their organisation (Benner and Tripsas, 2012). It has been found that strategic flexibility is crucial to surviving regulatory change (Pettus et al., 2009). Firms that adapt when regulations change are more successful. The firms that make changes involving innovation and contingency perform better than their non-adaptive peers affected by the same regulatory change (Smith and Grimm, 1987). Also, firms have different readiness to deal with regulatory (and market and technology) change (Kobos et al., 2018). If a firm masters the certifications required by regulations, it can achieve a competitive position (Polidoro, 2013). For rules to have a positive effect on the innovativeness of the firms, the companies that can innovate as the regulation evolves by using the existing laws to their advantage thereby obtain a competitive advantage (Delaplace and Kabouya, 2006).

If regulations are open for interpretations, they provide more opportunities for companies to devise flexible responses (Majumdar and Marcus, 2001). A rule might set specific requirements for the companies to comply with; however, the means for reaching these goals are in the companies’ control. They are flexible in terms of implementation time and technology requirements (Majumdar and Marcus, 2001). Depending on the firm (e.g., prospector or defender), different responses can be launched on a scale from embracing to defending (Fox-Wolfgramm et al., 1998). Stewart (1981) supports this argument as he also finds that regulations tend to aid both market and social innovation by maximising the flexibility in implementing leeway available to firms allowing the market to dictate cost-efficient and commercially viable solutions.

Complexity in company response

The second characteristic in determining how regulations affect innovations is the complexity of the project solution decided by the implementing firm (Ashford and Heaton, 1983; Delaplace and Kabouya, 2006; Stewart, 1981). Ashford and Heaton (1983: 111) propose ‘that responses to regulation are often consistent with the historical patterns of technological change in a given industrial context’. Thus, how the companies work with regulations comes to influence its innovative decisions (Delaplace and Kabouya, 2006). Changes in rules and the need to interpret these changes for customers can lead to increased complexity in balancing existing and new products (Brusoni and Prencipe, 2001). Both Silber (1983) and Merton (1995) also conclude that studies of how regulations affect innovation in the financial service industry must also look at the greater picture as the relationship is more complicated than in other industries. Firms that manage to deal with complexity in the combined influence of regulations, market forces, and technology can create a competitive position (Geissinger et al., 2018). How companies respond to new rules and the processes and activities they undertake will determine the type of output they produce (Merton, 1995).

Research design

The innovations introduced from external regulations are perceived as complex and multifaceted (Jacobides and Winter, 2010; Mention and Torkkeli, 2012; Smet, 2012). In combination with the need to explore opposing research results, a single case study approach was selected as the most suitable research method (Yin, 2017) for this deductive study since the contextual circumstances are imperative, and the boundaries between phenomenon and context are not distinct (Yin, 2017).

The Danish financial services industry and the case company

In the current regulatory landscape driven mainly by the European Union (both directives and regulations) and global initiatives, most local financial services markets are part of a global industry context. The increasing level of regulations from both a national and international environment also characterises the Danish financial industry, which makes up the macro environment of the case company, Nykredit. The regulations introduced in recent years cover areas such as product design, service process execution, trading policies, funding and capital requirements (Freij, 2018). Since the so-called financial crisis in 2008, and the subsequent development to reduce costs and shrink margins, Danish financial services companies’ earnings have been under pressure mainly due to the cost of funding, expenses to government-driven bank rescue packages, increased loan losses and write-downs as well as the deposit guarantee scheme, which secure assets on accounts up to EUR 100,000. Moreover, the crisis also affects social factors as changes in consumer attitudes and opinions towards the Danish financial services companies exert a high degree of influence on social factors. The social pressure has translated into regulations dealing with (e.g.) anti-money laundry and financial advice. In addition, a stride of entrepreneurial firms, under the common label ‘Fintech’, are trying to enter the turf of the mature financial services industry (Schueffel, 2016).

The case company, Nykredit, is a Danish financial services provider with commercial and mortgage banking, insurance, leasing, pension services, and a real estate agency. They sell these products and services under a multi-brand strategy with a total of four different brands: Nykredit, Totalkredit, Nybolig, and Estate. Nykredit holds a market share of 42.6 per cent on mortgage banking and 5.2% within commercial banking, which makes Nykredit the largest lender in Denmark and one of the major private bond issuers in Europe and was chosen for these reasons. Nykredit employs approximately 4,100 employees primarily in Denmark, but also internationally, e.g. France, Spain, and Sweden. The assets under management amount to approximately EUR 172 billion.

Data collection

Nykredit is structured into four units: Customers, Products, Operations and Support. Several specialised competence centres, as well as central staff functions, support these units. The focus of this study is the Products unit that is responsible for product strategy, product development, as well as product-specific training and advisory tools.

Launched product development projects were sampled from 2008-2012, which is a suitable period due to the increase in regulatory activity launched intensively after the financial crisis in 2008. The launch year specifies the year of implementation. The identified sample includes the following types of projects: development of new products, optimisation of products or processes, and business implementation projects. The excluded projects in the data sample are I.T. optimisation projects, internal strategy projects, coordination projects, prototypes for product support tools projects, internal educational projects, and product closure projects. In total, 100 product development projects were identified in Product Implementation for the period 2008 until 2012 (both included).

All project managers responsible for the projects were identified. In total, 10 project managers were involved in launching the 100 new products, but only five were still employed by the company. These people were interviewed individually and participated in separate workshops where their project was assessed and questioned. Products unaccounted for were discussed with the Head of Product Implementation and the Chief Project Manager. A focus group session was included for validation purposes. Several interviews were conducted initially with the Head of Product Implementation, as the company did not have a complete and formal overview of launched products, to create an overview and database of the products and the relevant regulations influencing them. Based on the input from workshops and interviews, the projects were rated (see the section below), and the database was continuously validated. Originally 110 products were identified, but 10 products could not be accounted for and therefore were excluded.

Variables and measures

The previous literature indicates that the complexity and flexibility of responses to external financial regulations affect innovation outcomes. Hence, a flexible response to regulations might foster innovation; likewise, non-flexible responses may hamper innovation (Ashford and Heaton, 1983; Majaumdar and Marcus, 2001; Warren, 2008). Scholars such as Warren (2008), Ashford and Heaton (1983), and Majaumdar and Marcus (2001) find that if the response has a high level of flexibility in relationship to R&D resources and implementation timeframe, then a higher degree of innovation can be achieved.

The degree of flexibility in each product development project is also crucial in Nykredit. Head of Product Implementation argues that if the product development projects in Nykredit have low flexibility, it hampers the Project Manager’s ability to manage and shape the project, contrary to those product development projects where a highly flexible approach occurs. The Head of Product Implementation further exemplified the effect of low flexibility through the product ‘Aldersopsparing’ (‘Saving for Age’) from 2012 where regulators, i.e. the Danish Government provided all the parameters of the product and thus neither Nykredit nor the Project Manager had any flexibility to develop the product themselves. To this point, he notes that:

The point about the importance of high flexibility is very interesting, and it is something we think about a lot (…) for us I think it is more a matter of time – do we have enough time to create a good solution?

Thus, from a first empirical analysis, high flexibility is equal to product development projects where the company has sufficient time and space to develop and implement the product, including developing new technologies and methods for application. In contrast, low flexibility is equal to product development projects where Nykredit has insufficient time to develop and implement the product, including the development of new technologies and methods to use.

Complexity in the implementation of new regulations is equal to resource consumption, including cost, degree of stakeholder-involvement, and time spent per week depending on the project solution:

In Product Implementation all product development projects are divided into three different types of project profiles which indicate the resources used on the project i.e. light, medium, or high project profile. (Head of Product Implementation)

Hence, at Nykredit, the complexity of a solution to a regulatory requirement is mainly driven by resource dimensions and the effort spent over time. A ‘light’ project profile indicates that the Project Manager has spent approximately 10 hours or less per week on the project, used a minimum amount of resources, and involved few stakeholders in the project. A ‘medium’ project profile indicates that the Project Manager uses approximately 10–24 hours per week on the project with a medium amount of resources and stakeholders involved. Finally, a ‘heavy’ project profile is a project where the Project Manager spends approximately 25 hours per week or more, and a high amount of resources, and involves many stakeholders in the project’s solution to the change in legislation. Head of Implementation notes that:

The longitudinal timeframe is not an important indicator of the complexity for a product development project in Nykredit. For example, a long time period can still be light in relation to time spent per week, costs, resources, and stakeholders involved in the period.

The statistical model

In order to investigate the dependence among the variables, we use cross-tabulation of independent variables with respect to a dependent variable. Using Performance as the dependent variable, we compute Chi-square statistics to investigate the evidence of the relationship among the variables.

Chi-square test of independence:

Dependent variable	Independent variable	Chi-square	DF	p-value
Performance	Flexibility	62.4468	3	<0.0001
	Innovativeness	16.3055	15	0.3620
	Complexity	27.3417	6	<0.0001

From the table, we observe that Innovativeness seems to be independent of the dependent variable (Performance). Based on this independency, the two variables do not add information for the prediction of the dependent variable, and they will not be included in the prediction.

The two other variables, Flexibility and Complexity, are not independent of Performance. Hence, we will apply these two variables to predict the dependent variable. The binary logistic regression model can be used to determine the impact for predicting among the four categories of the dependent variable (see Table 1).

Table 1.

Constructs and measurements.

	Construct	Prior research	Case company	Measurements
Mediating constructs
	Complexity	The level of complexity must be taken into account when analysing regulations’ effects on innovation	Product development projects in Nykredit are divided into specific project profiles, which indicate the resources used and stakeholders involved; this indicates the project complexity	Low complexity
				Medium complexity
				High complexity
	Flexibility	The flexibility of the company response must be taken into account when studying regulations’ effects on innovation	Flexibility in Nykredit relates to sufficient/insufficient time to develop and implement the product, including the development of new technologies and their methods of use	High flexibility
	Flexibility			Low flexibility
Product Development Performance Characteristics	Innovativeness	The innovativeness of a product depends on the level of activities within idea generation, screening, business and market analysis, technological development and cross-functional collaboration	Nykredit does not measure its performance of the products according to the degree of innovativeness	High degree – New to the market – New to the company
				Medium degree – New delivery processes – Service modifications
				Low degree – Service line extensions – Service repositioning
	Performance	Product development performance in the financial services industry clusters into three overall categories: financial success, non-financial success, and failure	Nykredit measures the performance of the product after launch on a case-by-case basis based on the product’s sales in units, sales in volume and generated revenue; furthermore, based on our interviews, we found that a product can also be categorised as compliant.	Financial success
				Non-financial success
				Compliant
				Failure

With four levels of the dependent variable (Performance), we have a multinomial response, and the multinomial logit model is:

M_{0} : log (\frac{p}{1 - p}) = α + β_{2 (=Flexibility)} + β_{3(=Complexity)}

Results

From the estimated parameter ML-estimates, we compute the model predicted probabilities:

Performance	Flexibility	Complexity	Probability	Std. Err.
Compreg	High	High	0.0001	0.0001
Failure	High	High	0.2856	0.1207
Financial	High	High	0.5000	0.1336
NonFinancial	High	High	0.2143	0.1097
Compreg	High	Low	0.0001	0.0001
Failure	High	Low	0.0435	0.0425
Financial	High	Low	0.6086	0.1018
NonFinancial	High	Low	0.3478	0.0993
Compreg	High	Medium	0.0001	0.0001
Failure	High	Medium	0.0370	0.0257
Financial	High	Medium	0.3518	0.0650
NonFinancial	High	Medium	0.6111	0.0663
Compreg	Low	High	0.3331	0.2722
Failure	Low	High	0.6667	0.2722
Financial	Low	High	0.0001	0.0001
NonFinancial	Low	High	0.0001	0.0001
Compreg	Low	Low	0.9997	0.0025
Failure	Low	Low	0.0001	0.0001
Financial	Low	Low	0.0001	0.0001
NonFinancial	Low	Low	0.0001	0.0001
Compreg	Low	Medium	0.3333	0.2722
Failure	Low	Medium	0.3333	0.2722
Financial	Low	Medium	0.3333	0.2722
NonFinancial	Low	Medium	0.0001	0.0001

Highest probabilities in bold.

Discussion

While Cooper and de Brentani (1991), Naudé et al. (1998), and Thwaites (1992) based their findings on assumptions about deregulation of the financial services industry, the present study focuses on increasing regulation. The results of the study of the 100 innovation projects give additional insight into the intersection between innovation, entrepreneurship, and regulatory influence (Streak and Urban, 2013; Urban, 2013, 2018).

From the results, the following observations are made with the highest likelihood of success. When Flexibility = High and Complexity = High, the predicted probability of financial success is equal to 50%, but when Complexity = Low, the expected chance of Financial success is equal to 60%. Ashford and Heaton (1983) and Majumdar and Marcus (2001) suggest that flexible firms are more efficient in dealing with regulation, but these findings indicate a delicate relationship between Flexibility and Complexity. This state was suggested by Silber (1983) and Merton (1995) and is an empirical response to strategic flexibility (Pettus et al., 2009) and contingencies in strategy execution (Smith and Grimm, 1987).

The literature proposed Complexity (Ashford and Heaton, 1983; Delaplace and Kabouya, 2006; Stewart, 1981) when a concern for productivity was raised. Productivity exerts an effect whether Complexity is high or low, but Complexity = High is not in itself an impediment to productivity as the likelihood of success is 50 per cent. Still, Lee and Markham (2016), based on a PDMA survey, identifies that the best-performing companies in consumer services (including banks) have a 60.3 per cent likelihood of success; thereby suggesting that Complexity = High is problematic if achieving the highest performance is the goal.

When Flexibility = High and Complexity = Medium, the predicted probability of non-financial success is equal to 67%, suggesting a paradox for the firm since how the organisation manages Complexity produces different achievements. Flexibility = Low is also interesting. The case study of Nykredit showed that the company also has cases that they consider compliant projects which do not affect financial or non-financial success. With these cases, the needs of the project do not require high flexibility in the management of the project. Moreover, Complexity = Low suggesting uncertainty is lower as they do not require success in the marketplace. Still, again Complexity is interesting as Complexity = Medium shows a lower predicted probability of Compliant, Failure, and Financial success where each is equal to 33%.

This empirical inquiry into regulations and innovation performance showed no direct causality, but rather a relationship mediated by complexity and flexibility. More importantly, the study highlights the role of management to strike the right levels of complexity and flexibility in project implementation.

Conclusion

The theoretically derived variables of complexity and flexibility mediate the relationships between regularity change. These variables have never empirically been explored on the company level of analysis. The case study examines 100 developed and launched new services by a major Danish financial institution. The results contribute to the differing views on how regulations influence innovation by showing links between high flexibility and low complexity in firm response for improved innovation performance. However, the relationship between these variables is paradoxical, as both variables in conjunction affect performance.

Managerial implications

Managers in financial services firms should be concerned about the increasing volume and complexity of regulation in several ways. Managers must be attentive to how the company responds to coming regulations, planning well ahead and work across business units to find innovation benefits. Current examples of failure to balance regulatory compliance and innovation are present not only in the financial services industry (Zoltners et al., 2016) but also in (e.g.) the automotive industry (Liker, 2015). Emerging development in the platform – and the sharing economy are also ripe for further study (Laurell and Sandström, 2016). Benefits will arise to those firms that focus on both results in terms of compliance and innovation potential, while at the same time applying a low complexity and high flexibility regulatory implementation.

Limitations

Legislation and regulations are influencing almost all projects, in particular in the financial services industry. Initial studies showed that only three projects could be claimed to be without regulatory interference. This condition made it impossible to test for projects not influenced by regulations vs influenced. A manual count of all legislation was conducted initially, but project managers could not link specific regulation and legislation to individual projects unambiguously. None of the project managers were educated in the law. Hence, this condition was also excluded from the analysis, but further studies should examine the role of experience in understanding complexity and flexibility.

The focus on one firm allows a deep understanding of one particular organisation and access to valuable research data concerning a long list of regulatory implementation projects. Due to an increasing level of commonality in regulations in the E.U. (based on emerging directives and regulations), it could be argued that also banks in other countries are faced with similar challenges as Nykredit. Hence, the relevance of a single firm in a single country is appropriate in balance to the access to the rich data. Nevertheless, this study should be extended to cover more firms and sectors across other geographies. It could also address other industries where regulations are interwoven into product and service development as a single case study has limitations for the generalizability of the findings.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Claus J Varnes

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