The Impact of Innovation Management Systems on Firms’

Innovation Performance: The Mediating Role of Openness to

Innovation

Rita Giordano, Gian Marco Miele, Filippo Frangi, Antonio Ghezzi and Andrea Rangone

Politecnico di Milano, Department of Management, Economics and Industrial Engineering,

Via Lambruschini 4B, 20156, Milan, Italy

Keywords: Innovation Management Systems, ISO 56002, Open Innovation, Openness Innovation Performance,

Innovation, Innovation Management, Digital Transformation, Business Model.

Abstract: This study examines the impact of Innovation Management Systems (IMS) maturity on companies' Innovation

Performance, specifically emphasizing the ISO 56002 standard as a guiding framework. The present

investigation explores the mediating role of Open Innovation (OI) in this relationship, investigating how

openness to external collaboration affects the effectiveness of structured innovation processes. A Systematic

Literature Review (SLR) identifies significant gaps, notably the scarcity of empirical evidence regarding the

integration of IMS with OI techniques and their collective impact on performance outcomes. Empirical data

were gathered via a survey of 139 medium-to-large Italian enterprises spanning several sectors. The study

assesses organizations' IMS maturity, their openness to innovation, and the interaction between these factors

in influencing Innovation Performance. Structural Equation Modeling (SEM) demonstrates that an established

Innovation Management System (IMS) enhances Innovation Performance both directly and indirectly by

promoting openness to external knowledge transfer and collaboration. The results enhance the current IMS

literature by illustrating that a systematic approach to innovation management, in conjunction with Open

Innovation methods, can yield exceptional innovation results. These findings provide practical guidance for

managers and decision-makers aiming to improve their organizations' innovation capacities and attain durable

competitive advantages in progressively interconnected markets.

1 INTRODUCTION

In today's dynamic business landscape, innovation

serves as a cornerstone of competitive advantage,

enabling firms to adapt to rapid technological

advancements and shifting market demands. The

accelerating pace of change necessitates structured

approaches to innovation management, fostering the

ability to integrate new knowledge, enhance

operational efficiency, and sustain long-term growth.

Organizations are progressively implementing

organized frameworks, such as Innovation

Management Systems (IMS), to synchronize

innovation activities with strategic objectives and

improve efficiency. ISO 56002 has emerged as a

significant guideline for the implementation and

optimization of IMS across various sectors.

Nonetheless, whereas IMS frameworks are

acknowledged for their capacity to organize

innovation, empirical studies regarding their direct

influence on Innovation Performance (IP) and the

function of Openness to Innovation (OI) as a

mediating factor are still insufficiently established.

The current literature emphasizes that IMS improve

innovative capabilities by formalizing innovation

processes, optimizing resource allocation, and

minimizing inefficiencies. By offering a structured

framework for innovation management, IMS assists

organizations in aligning their innovation strategies

with overarching organizational objectives, hence

promoting both incremental and radical innovation

(Giménez et al., 2023; Silva, 2021). Research on

Open Innovation underscores the significance of

external information flows and collaborations in

enhancing innovation skills (Chesbrough, 2003;

Laursen & Salter, 2006; Ghezzi et al.,2022).

Notwithstanding these improvements, the interaction

between IMS and OI and their collective impact on IP

remains poorly investigated. Furthermore, the

absence of standardized instruments to assess IMS

maturity constrains both scholarly research and

Giordano, R., Miele, G. M., Frangi, F., Ghezzi, A. and Rangone, A.

The Impact of Innovation Management Systems on Firms’ Innovation Performance: The Mediating Role of Openness to Innovation.

DOI: 10.5220/0013470900003929

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 2, pages 907-914

ISBN: 978-989-758-749-8; ISSN: 2184-4992

907

practical implementation. The deficiencies in the

literature pose considerable obstacles. Theoretically,

they impede the advancement of integrated models

that incorporate both internal structures and exterior

interactions. In practice, they provide managers with

insufficient guidance on optimizing IMS and OI to

attain sustained innovation results. Addressing these

deficiencies is essential for enhancing knowledge and

providing practical guidance for companies operating

in increasingly competitive contexts. This study seeks

to address these deficiencies by investigating the

correlation between IMS maturity, OI, and IP. It

specifically addresses two research inquiries: What is

the correlation between IMS maturity and IP? What

function does OI serve in this relationship?

The study employs data from 139 Italian

companies along with Partial Least Squares

Structural Equation Modeling (PLS-SEM) to

examine these correlations. IMS maturity is evaluated

using a scale derived from ISO 56002, whilst OI and

IP are tested by recognized multi-item assessments.

This research enhances the literature by providing

empirical proof of the positive correlation between

IMS maturity and IP, while recognizing OI as a

significant mediator. The validated IMS maturity

scale serves as a significant instrument for future

study and practice, facilitating more uniform

evaluations across various contexts. The study

illustrates the synergistic functions of IMS and OI,

offering practical guidance for managers and

enhancing theoretical comprehension, while

underscoring the need of cohesive innovation

strategies for attaining exceptional performance.

2 THEORETICAL

BACKGROUND

Innovation is crucial for sustaining competitiveness

in the contemporary business landscape, fostering

flexibility and growth in ever-changing markets

(Schumpeter, 1983; Hansen, 2014; Ghezzi et

al.,2016). Innovation, broadly defined as the

introduction of new products, processes, or practices,

spans various dimensions, including radical and

incremental methods, as well as modular and

architectural transformations (Goffin & Mitchell,

2005; Henderson & Clark, 1990). Value creation and

differentiation in competitive environments are

uniquely influenced by each of these dimensions

(Damanpour et al., 2009; OECD, 2005; Ghezzi et

al.,2014). The mechanisms of invention encompass

both internal and external factors. Internally,

organizations depend on the absorptive capacity to

recognize and apply external knowledge,

organizational structures that foster collaboration,

and research and development (R&D) capabilities

(Cohen & Levinthal, 1990; Burns & Stalker, 1961).

Externally, innovation success is increasingly

acknowledged to be contingent upon collaboration

with a variety of stakeholders, such as customers,

suppliers, universities, and entrepreneurs. Open

Innovation, as defined by Chesbrough (2003),

emphasizes the significance of integrating external

knowledge streams into internal processes. Von

Hippel’s (1988) user innovation framework similarly

underscores the essential significance of involving

lead users in the co-creation of new solutions,

accentuating their impact on market relevance.

2.1 Innovation Management System

Innovation Management Systems (IMS) provide

structured methods for the promotion, coordination,

and expansion of innovation initiatives. As defined in

ISO 56002:2019, IMS represents a comprehensive

and integrated framework including strategic,

tactical, and operational duties to systematically plan,

coordinate, and control innovation activities

(International Organization for Standardization,

2019). The maturity of an IMS indicates its degree of

implementation, optimization, and alignment with

strategic objectives, significantly contributing to the

enhancement of organizations' innovation capabilities

and outcomes (Santos & Almeida, 2022). Elevated

IMS maturity, defined by systematic management

and ongoing optimization, is associated with

enhanced innovation results, particularly in balancing

exploratory and exploitative innovation endeavours

(March, 1991; Martínez-Costa et al., 2019).

Research on standardized frameworks, such UNE

166002 and ISO 56002, indicates that companies with

high IMS maturity attain enhanced innovation

efficiency, customer satisfaction, and

competitiveness (Giménez et al., 2023; Mir et al.,

2016).

2.2 Innovation Performance

Innovation Performance evaluates the effectiveness

of a company's innovation initiatives in terms of

outputs, processes, and strategic results (Crossan &

Apaydin, 2010). It encompasses quantifiable

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indicators, like patents and product launches

(Hagedoorn & Cloodt, 2003), alongside more

extensive effects such as improved strategic

positioning and competitiveness (Tidd & Bessant,

2018). Quantitative metrics, such as R&D

expenditure and market share from new goods,

frequently function as benchmarks, whereas

qualitative assessments evaluate the congruence of

innovation endeavours with strategic objectives

(Gopalakrishnan & Damanpour, 1997; Camisón &

Villar-López, 2014).

The correlation between IMS maturity and

Innovation Performance is extensively recorded, with

advanced systems promoting both incremental

improvements and radical innovations (Martínez-

Costa et al., 2019). By leveraging structured

frameworks, organizations align their innovation

efforts with organizational strategy, attaining

quantifiable results such as enhanced operational

efficiency and market responsiveness (Mir et al.,

2016).

2.3 Openness to Innovation

Open innovation enhances companies' capacity to

generate and implement novel concepts by promoting

information transfer beyond organizational limits

(Chesbrough, 2003; Chesbrough & Bogers, 2014). It

encompasses inbound innovation, wherein

companies assimilate external information via

collaborations, partnerships, and licensing, and

outbound innovation, which entails externalizing

internal innovations to optimize their value (van de

Vrande et al., 2009). Research has investigated its

influence on business performance through

theoretical frameworks such as the knowledge-based

view and resource-based view, emphasizing its effect

on innovation results (Ahn et al., 2015; Greco et al.,

2016). Diverse methodologies have been suggested to

evaluate openness, encompassing firm-level metrics

that consider external search breadth and depth

(Laursen & Salter, 2006) and project-level

frameworks like the IFO-Scale and ATOM method,

which assess openness via collaboration intensity,

transparency, and knowledge exchange (Alam et al.,

2022; Bellantuono et al., 2021).

3 METHODOLOGY

Innovation Management Systems (IMS) have

emerged as significant frameworks for aligning

organizational activities with strategic innovation

objectives. Despite their extensive use and the

systematic frameworks established by standards such

as ISO 56002, comprehending their concrete

influence on companies' Innovation Performance

necessitates additional investigation (Silva, 2021;

Idris & Durmusoglu, 2021). A crucial part of this

relationship involves evaluating how the maturity of

these systems results in quantifiable outcomes,

especially when firms embrace external knowledge

and collaboration (Chesbrough, 2003).

This research operationalizes three main

dimensions drawn from the theoretical framework:

IMS Maturity, Innovation Performance, and

Openness to Innovation. These notions establish the

basis for exploring the subsequent research questions:

• RQ1: What is the relationship between IMS

Maturity and Innovation Performance?

• RQ2: What role does a firm’s openness to

innovation play in this relationship?

3.1 Hypothesis Development

For a better overview, the hypothesized connections

between the study constructs, have been summarized

and displayed in Figure 1

Figure 1. Conceptual Framework.

Evidence from the literature indicates that the

implementation of a mature IMS improves innovation

performance across a variety of categories, including

product, process, organizational, and marketing

innovations. Standards like UNE 166002 and ISO

56002 assist organizations in fostering a culture that

optimizes both novel and established ideas (Martínez-

Costa et al., 2019).

In turn, it is reasonable to hypothesize that:

H1: A mature Innovation Management System

positively affects a company's Innovation

Performance.

As organizations advance their Innovation

Management Systems, it is likely that they will

incorporate increasingly advanced techniques for

The Impact of Innovation Management Systems on Firms’ Innovation Performance: The Mediating Role of Openness to Innovation

909

sourcing innovation from both internal and external

sources (Garechana et al., 2017). Consequently, it

may be posited that as firms' IMS maturity escalates,

they are likely to seek increased external

collaborations, promoting better transparency and

enhancing the formation of a more resilient

innovation ecosystem. Thus, it is hypothesized that:

H2: A mature Innovation Management System will

be positively related to company’s Openness to

Innovation.

Open Innovation (OI) enables companies to

utilize external knowledge resources, hence

enhancing their innovation capacity. Researches

show that firms implementing Open Innovation

exhibit enhanced R&D productivity, superior market

performance, and increased innovation output

(Laursen and Salter, 2006; West & Bogers, 2017).

Consequently, based on the literature, it is

hypothesized that:

H3: Firm’s Openness to Innovation positively

influences company's Innovation Performance.

The final inquiry focuses on assessing whether

companies that actively participate in Open

Innovation activities may moderate the beneficial

effects of mature IMS on innovation results. This

investigation explores if Openness to Innovation

serves as a mediator (Igartua et al., 2010), elucidating

the influence of innovation management systems on

innovation performance. Consequently, it is

postulated that:

H4: Firm’s Openness to Innovation mediates the

relationship between Innovation Management

Systems Maturity and Innovation Performance.

3.2 Research Design

The research relies on a quantitative methodology,

employing Partial Least Squares Structural Equation

Modeling (PLS-SEM) to evaluate the offered

hypotheses. PLS-SEM is especially appropriate for

models with numerous components and small to

medium sample sizes, as it does not necessitate

stringent normality assumptions (Hair et al., 2020).

This method facilitates concurrent assessment of

measurement and structural models, permitting

thorough investigation of direct, indirect, and

mediating impacts.

Data was gathered from July 8 to September 16,

2024, focusing on medium and large firms in Italy. A

survey was administered to 850 professionals,

comprising CIOs, CInOs, Innovation Managers, and

IT Directors, yielding 144 replies, of which 139 were

deemed valid following data cleansing.

To guarantee representativeness, the sample

encompasses several sectors and organization sizes:

• Company Size: Medium (26%), Large

(29%), Very Large (45%).

• Sectors: Manufacturing (34%), Services

(32%), Energy (11%), Retail (18%), and

Construction (5%).

• Innovative Sectors: Pharmaceuticals (7%),

ICT (9%), Electronics (6%), and

Automotive & Transportation (12%).

The experimental design for this study was

developed in collaboration with Startup Thinking

Observatory of School of Management of Politecnico

di Milano.

In addition to the survey data, secondary data was

sourced from the AIDA database, providing firm-

specific information such as revenues and 2007

ATECO codes for sectoral classification. Market

concentration (CR4) metrics were calculated to assess

competitive dynamics within industries.

3.3 Conceptual Framework

This study employs a conceptual framework to

analyze the interaction among three key constructs:

Innovation Management System (IMS) Maturity,

Openness to Innovation (OI), and Innovation

Performance (IP). The framework establishes a basis

for examining the interconnections among structured

internal procedures, external collaborations, and

innovative outputs. The model asserts that a firm's

IMS Maturity directly affects its Openness to

Innovation and Innovation Performance, with

Openness to Innovation acting as a mediating variable

that enhances the influence of IMS on IP. This

framework facilitates a comprehensive understanding

of how internal mechanisms and external interactions

collectively influence innovation success.

To augment the explanatory capacity, the model

integrates control variables including Firm Size

(SIZE), Market Concentration (CR4), and Sector-

Specific Dynamics. These variables account for

disparities in resources, competitive landscapes, and

industry-specific innovation practices.

3.4 Operationalization of Constructs

The study operationalizes the three constructs—IMS

Maturity, Openness to Innovation, and Innovation

Performance—using established psychometric scales

and measurement frameworks to guarantee reliability

and validity.

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3.4.1 IMS Maturity

Drawing on the framework proposed by Santos &

Almeida (2022), IMS Maturity is conceptualized as a

multi-level construct reflecting the degree to which

firms have implemented, optimized, and aligned their

innovation systems with strategic objectives.

A five-point maturity scale grounded in ISO 56002

standards is employed to evaluate essential

dimensions, such as leadership, planning, support,

operations, and performance assessment.

3.4.2 Innovation Performance

Innovation Performance is conceptualized as a

multidimensional construct that includes innovation

outputs (e.g., patents, product launches), processes

(e.g., R&D activities), and strategic results (e.g.,

market share increase, profitability) (Crossan &

Apaydin, 2010; Martínez-Costa et al., 2019).

Quantitative metrics, including the percentage of

revenue generated from new goods and the count of

innovations launched, are utilized in conjunction with

qualitative assessments of strategic alignment.

3.4.3 Openness to Innovation

The operationalization of the third construct

concerning a firm's Openness to Innovation, was

developed in accordance with Startup Thinking

Observatory of School of Management of Politecnico

di Milano definitions. The measurement framework

is based on Laursen and Salter's (2006) framework,

which quantifies external search breadth (number of

external sources leveraged) and depth (extent of

reliance on specific external partners). This construct

captures both inbound (knowledge absorption) and

outbound (knowledge sharing) innovation practices

(Chesbrough, 2003).

3.5 Experimental Procedure

The research used Partial Least Squares Structural

Equation Modeling (PLS-SEM) to investigate the

relationships among IMS Maturity, Openness to

Innovation, and Innovation Performance. This

methodology, executed with SmartPLS software, is

appropriate for the exploratory characteristics of the

research and the comparatively limited sample size.

The analysis consists of two primary stages:

assessing the measuring model to verify the reliability

and validity of constructs, and examining the

structural model to evaluate the importance and

strength of links. Essential measures, including factor

loadings, composite reliability, and the R² coefficient,

are employed to assess the model's robustness.

Bootstrapping techniques are utilized to assess the

relevance of both direct and indirect impacts,

encompassing mediation pathways. This

methodological rigor facilitates a deeper

comprehension of how organizational systems and

openness affect innovation outcomes.

4 EMPIRICAL RESULTS

4.1 Measurement Model Evaluation

The statistical evaluation of the measurement model

demonstrated robust internal consistency and

convergent validity for the constructs involved. The

Innovation Management System (IMS) exhibited

factor loadings ranging from 0.711 to 0.897, alongside

a high Cronbach’s alpha of 0.923 and a composite

reliability (CR) of 0.938. The average variance

extracted (AVE) for IMS was 0.686. Openness to

Innovation (OI) exhibited factor loadings between

0.526 and 0.853, with a Cronbach’s alpha of 0.794, a

composite reliability (CR) of 0.859, and an average

variance extracted (AVE) of 0.554. Notwithstanding

the reduced loading of 0.526 for a single OI item, it

was preserved due to its theoretical significance. The

variables of Innovation Performance (IP) exhibited

Table 1: Psychometric properties of the measures.

Construct Factor Loading

(t-value)

Cronbachs’𝜶

Innovation

Management

System

Maturity

IMS1

IMS2

IMS3

IMS4

IMS5

IMS6

IMS7

0.871 (36.939)

0.875 (37.708)

0.778 (22.178)

0.897 (52.946)

0.868 (43.391)

0.711 (16.152)

0.782 (16.449)

0.923

Openness to

Innovation

OI1

OI2

OI3

OI4

OI5

0.853 (36.107)

0.526 (7.796)

0.751 (19.052)

0.780 (21.757)

0.771 (20.370)

0.794

Innovation

Performance

IP1

IP2

IP3

IP4

IP5

0.616 (7.296)

0.793 (19.140)

0.788 (17.942)

0.848 (30738)

0.790 (20.857)

0.830

Construct

Cronbachs’𝜶

CR AVE

Innovation

Management

System

Maturity

0.923 0.938 0.686

Openness to

Innovation

0.794 0.859 0.554

Innovation

Performance

0.830 0.879 0.595

The Impact of Innovation Management Systems on Firms’ Innovation Performance: The Mediating Role of Openness to Innovation

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Table 2: Hypothesis Testing and Control Variables.

Model Relationship

Path

Value

St-Dev p-value

Empirical

Evidence

Hypothesis H1

IMS → IP

0.411 0.100 0.000 Yes

Hypothesis H2

IMS → OI

0.583 0.061 0.000 Yes

Hypothesis H3

OI → IP

0.205 0.097 0.035 Yes

Hypothesis H4

Direct Effect

IMS → IP

0.411 0.100 0.000 Yes

Indirect Effect

IMS → OI → → IP

0.119 0.058 0.041 Yes

Total Effect

IMS → IP

0.531 0.072 0.000 Yes

Control Variables

SIZE → IMS

0.240 0.080 0.003

ICT → IMS

0.904 0.349 0.010

ELT → IMS

0.612 0.243 0.012

SIZE → OI

0.149 0.071 0.036

CR4 → OI

0.189 0.061 0.002

PHA → OI

0.849 0.274 0.002

loadings ranging from 0.616 to 0.848, with a

Cronbach’s alpha of 0.830, a composite reliability

(CR) of 0.879, and an average variance extracted

(AVE) of 0.595. The model's robustness corresponds

with the literature indicating that loadings between

0.50 and 0.70 are acceptable when overall construct

reliability and validity are substantial (Hair et al.,

2017).

4.2 Structural Model Evaluation

The results presented in Table 2 demonstrate that a

mature Innovation Management System (IMS)

substantially improves Innovation Performance (IP),

as evidenced by a path coefficient of 0.411 and a p-

value of 0.000. This underscores the critical role of

the IMS. Firm size has a positive impact on IMS, but

it does not directly affect IP. Sectors such as ICT and

Electronics exhibit a significant influence on IMS

maturity. Furthermore, IMS maturity significantly

enhances Openness to Innovation (OI), evidenced by

a path coefficient of 0.583 and a p-value of 0.000,

indicating that organizations with advanced IMS are

generally more receptive to innovation. This

transparency enhances IP, as evidenced by a positive

correlation (path coefficient of 0.205, p = 0.035).

Moreover, OI serves as a partial mediator in the IMS-

IP interaction, demonstrated by a notable indirect

effect (path coefficient of 0.119, p = 0.041), while the

overall IMS-IP effect remains robust at 0.531 (p <

0.001). The CR4 concentration ratio of the sector

positively influences OI (path coefficient = 0.189, p

= 0.002), while the pharmaceuticals sector (PHA)

demonstrates a strong positive effect on OI (path

coefficient = 0.849, p = 0.002).

5 DISCUSSIONS

The findings validate that developed IMS

frameworks, particularly those aligned with ISO

56002, markedly improve innovation outputs by

formalizing systematic procedures and promoting

coherence with strategic goals (Silva, 2021; Rezak et

al., 2023). The formalization of innovation processes

allows firms to deploy resources more efficiently,

minimize inefficiencies, and maintain a competitive

edge through ongoing innovation initiatives. This

study illustrates that a properly executed Innovation

Management System (IMS) can harmonize structure

and adaptability, promoting innovation while

ensuring operational consistency, contrary to the

belief that standardization suppresses creativity

(Blind et al., 2013; Giménez et al., 2023).

Furthermore, the mediating function of OI highlights

its significance in enhancing the advantages of IMS

maturity. Companies that actively pursue external

collaborations—such as alliances with startups,

research institutions, or other enterprises—can

enhance their internal capabilities with external

insights, technology, and skills (Chesbrough, 2003;

West & Bogers, 2017). The integration of internal and

external knowledge flows is especially advantageous

in dynamic sectors such as ICT and Pharmaceuticals,

where innovation frequently relies on the capacity to

access and leverage varied external resources

(Laursen & Salter, 2006). Integrating OI methods into

a systematic IMS enables organizations to improve

their agility, resilience, and ability to sustain

innovation. The research emphasizes sectoral and

firm-specific dynamics, indicating that industries

characterized by high technological intensity derive

the greatest advantages from established IMS and OI

processes. It contests the notion that business size is

directly linked to enhanced innovation performance,

indicating that the strategic management of

innovation processes is more pivotal than mere

resource availability (Tidd & Bessant, 2018).

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6 CONCLUSIONS AND

LIMITATIONS

The interplay between Innovation Management

System (IMS) maturity, Openness to Innovation (OI),

and Innovation Performance (IP) provides new

insights into the domain of innovation management.

This research emphasizes the essential function of

structured innovation frameworks in improving

organizations' innovation results by implementing

IMS maturity according to ISO 56002 (International

Organization for Standardization, 2019). The results

validate that a developed IMS allows firms to manage

resources effectively, synchronize innovation

initiatives with strategic objectives, and establish

mechanisms that maintain competitive advantage

through regular innovation outcomes (Silva, 2021;

Giménez et al., 2023). The mediating role of

Openness to Innovation underscores its significance

as a complementary element in enhancing the

advantages of IMS maturity. Organizations with

robust IMS frameworks exhibit enhanced ability to

assimilate external knowledge and partnerships,

resulting in superior innovation performance

(Chesbrough, 2003; Laursen & Salter, 2006). This

synergy highlights the importance of integrating

strong internal systems with dynamic external

interactions to enhance adaptation and resilience in

innovation efforts (West & Bogers, 2017). Sectoral

differences revealed as a crucial factor affecting the

correlation between IMS maturity and innovation

results. Industries with high technological intensity,

such as ICT and Pharmaceuticals, gain substantial

advantages from organized IMS and OI procedures

due to their dependence on external information flows

and collaborations (Idris & Durmusoglu, 2021).

Conversely, industries characterized by dominant

internal innovation capabilities, such as Automotive,

demonstrate less significant effects, indicative of

sector-specific dynamics.

Despite its merits, this work has specific

limitations requiring consideration. The dependence

on self-reported data creates the potential for

subjective biases, as replies may not accurately reflect

the objective reality of organizations' innovation

activity. Subsequent investigations may rectify this

by incorporating objective criteria, such as patent

tallies or new product introductions, to enhance self-

reported assessments (Mir et al., 2016). The

geographical emphasis on Italian enterprises restricts

the generalizability of the results. Incorporating

organizations from varied cultural and economic

backgrounds might enhance the comprehension of

IMS and OI procedures (Laursen & Salter, 2006). The

application of arithmetic means for calculating IMS

dimensions and the uniform weighting of these

dimensions pose methodological constraints.

Subsequent research may enhance the analysis by

employing weighted scoring models or sophisticated

methods such as factor analysis to address the

differing importance of IMS components in affecting

innovation results (Blind et al., 2013). Moreover,

although Openness to Innovation was recognized as a

mediator, its partial influence indicates that additional

mediators—such as digital transformation or

organizational learning—could elucidate the

relationship between IMS and IP more

comprehensively.

This research highlights the revolutionary potential of

combining structured IMS frameworks with strategic

openness to external innovation. By leveraging these

complimentary dimensions, companies can improve

their innovation capability, sustain competitiveness,

and adapt to evolving market dynamics. Subsequent

research ought to further enhance IMS evaluation

instruments, investigate additional mediating factors,

and broaden the analysis to an international

framework to enrich the understanding of innovation

management practices.

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