Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 760 https://internationalpubls.com Analytical Study of Various Measures of Successful Innovation Management in IT Companies: -With Reference to Various IT Companies Located in Maharashtra State Dr. Smita Rohidas Temgire, HOD and Associate Professor, PVG’s College of Engineering and Technology and G K Pate (Wani ) Institute of management, Pune Prof. Ketaki Jayant Kulkarni Assistant Professor PVG’s College of Engineering and Technology and G K Pate (Wani ) Institute of management, Pune Dr. Rajesh Jha Associate professor, D Y Patil Institute of Management and Entrepreneur Development Pune Prof. Sonali Joshi, Assistant Professor, Trinity Institute of Management and Research, Pune Dr. Ashutosh Gadekar, Director ISMS Sankalp Business School, Pune Article History: Received: 02/02/2025 Revised: 15/03/2025 Accepted: 25/03/2025 Abstract: Knowledge Management (KM) catalyzes boosting organizational performance, encouraging innovation, and facilitating sustainable practices. This study employs a literature review-based approach to aggregate conclusions from key research on Knowledge Management practices, challenges, and impacts across diverse organizational contexts. The paper explores the influence of cultural dimensions on Knowledge Management, its role in innovation performance, and emerging trends such as Green Knowledge Management and digital transformation. Key findings reveal the importance of tailoring Knowledge Management frameworks to cultural and industrial settings, integrating Knowledge Management with sustainability goals, and the potential for technological advancements to enhance Knowledge Management practices. The research ends by offering suggestions for further studies to fill the gaps in KM implementation, digital integration, and sustainability. Keywords- Digital transformation, knowledge management, green knowledge management, sustainability Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 761 https://internationalpubls.com Introduction In today's increasingly competitive global business landscape, innovation has emerged as a key differentiator, enabling companies to gain a competitive edge in the market. As innovation is perceived from diverse angles, effective innovation management can have a profound impact on an organization, influencing various aspects of its operations. Literature Review New Research Streams which are relatively focusing on practices of innovation in a particular industry contexts is thus emerging (Kuester et al., 2013), and latest contributions include analysis of innovation patterns ( e.g., Chang et al., 2012) and success factors ( Kuester et al., 2013) also exploration of in detailed innovation practices in various service sectors which include experiential services( Zomerdijk and Voss, 2011) and non profit services (Barczak, Kahn, and Moss, 2006). Scholars have recently concentrated on identifying the characteristics of businesses that drive innovation and the factors that facilitate it (Fernandes et al., 2015; Ferreira et al., 2015; Hwang, 2004; Lemon and Sahota, 2004; Tidd and Bessant, 2009). Despite the growing body of research on innovation management, there is a notable lack of consensus on the metrics that define successful innovation management. This paper aims to contribute to the innovation management literature by exploring and addressing these metrics. Objectives To analyze various measures of successful Innovation management in IT companies Research Methodology The pertinent data for the paper was gathered from IT company employees located in Maharashtra State using a standardised questionnaire. 300 sample respondents were taken from different Maharashtra State districts by applying random sampling technique. The constructs utilised in the study are validated using exploratory factor analysis (EFA). 5-point Likert scale (responses ranging from totally disagree (1) to totally agree (5)) measured the opinion of respondents regarding measures of successful innovation management in IT companies of Maharashtra State. Data Analysis Bartlett's Test and KMO Kaiser-Meyer-Olkin Sampling Adequacy Measure, 548 The approximate Chi-Square value for Bartlett's Test of Sphericity is 615,583 df 105 Sig.,000 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 762 https://internationalpubls.com Im pro ved _Pr odu ctiv ity R e d u ce d _ C os t Incr ease d_C omp etiti vene ss Impr oved _Bra nd_R ecog nitio n Imp rove d_V alue Ne w_ Par tne rsh ip_ Rel ati ons hip Inc rea sed _T urn ov er Imp rov ed_ Mar ket_ Sha re Econ omic _Val ue_A dditio n_Sta ff Inc rea sed _N ew _Id eas Imp rov ed_ Qua lity _Id eas Effic ient_ Idea_ Impl emen tatio n Impr oved _Res ultan t_Su ccess Im pr ov ed _ Ra nd D Increased_ Customer _Satisfact ion A n ti - i m a g e C o v a ri a n c e Impro ved_P roduc tivity ,83 5 - ,0 5 7 - ,054 ,090 - ,072 ,04 4 ,06 8 ,009 -,190 ,05 7 - ,03 2 ,084 -,099 ,1 58 ,055 Redu ced_ Cost - ,05 7 ,7 8 3 - ,017 -,028 - ,023 ,14 5 - ,16 6 - ,208 ,089 - ,01 6 ,06 6 ,002 -,048 - ,1 65 - ,090 Increa sed_C ompet itiven ess - ,05 4 - ,0 1 7 ,885 -,175 - ,026 ,06 7 ,02 1 - ,080 ,006 - ,11 9 - ,01 6 -,039 ,034 - ,0 61 - ,020 Impro ved_ Brand _Rec ogniti on ,09 0 - ,0 2 8 - ,175 ,801 ,049 ,01 3 - ,18 1 ,047 ,038 - ,15 7 ,00 6 ,017 ,043 ,0 72 ,045 Impro ved_ Value - ,07 2 - ,0 2 3 - ,026 ,049 ,837 - ,18 6 - ,02 9 ,063 ,113 ,01 2 - ,14 1 ,014 ,089 ,0 10 ,051 New_ Partn ership _Rela tionsh ip ,04 4 ,1 4 5 ,067 ,013 - ,186 ,79 4 - ,12 5 - ,094 ,040 - ,01 6 - ,07 6 -,141 ,081 - ,0 09 - ,029 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 763 https://internationalpubls.com Increa sed_T urnov er ,06 8 - ,1 6 6 ,021 -,181 - ,029 - ,12 5 ,82 7 ,149 ,002 ,06 7 - ,08 4 -,038 ,018 ,0 65 ,010 Impro ved_ Mark et_Sh are ,00 9 - ,2 0 8 - ,080 ,047 ,063 - ,09 4 ,14 9 ,782 -,092 ,17 5 - ,05 8 -,078 ,046 ,1 12 - ,094 Econ omic_ Value _Addi tion_ Staff - ,19 0 ,0 8 9 ,006 ,038 ,113 ,04 0 ,00 2 - ,092 ,605 - ,19 7 - ,01 4 -,034 ,217 ,0 14 - ,142 Increa sed_ New_ Ideas ,05 7 - ,0 1 6 - ,119 -,157 ,012 - ,01 6 ,06 7 ,175 -,197 ,69 4 - ,12 1 ,070 ,027 - ,0 41 - ,082 Impro ved_ Qualit y_Ide as - ,03 2 ,0 6 6 - ,016 ,006 - ,141 - ,07 6 - ,08 4 - ,058 -,014 - ,12 1 ,82 6 -,163 ,003 - ,0 95 ,116 Effici ent_I dea_I mple menta tion ,08 4 ,0 0 2 - ,039 ,017 ,014 - ,14 1 - ,03 8 - ,078 -,034 ,07 0 - ,16 3 ,702 -,259 ,0 15 - ,092 Impro ved_ Resul tant_ Succe ss - ,09 9 - ,0 4 8 ,034 ,043 ,089 ,08 1 ,01 8 ,046 ,217 ,02 7 ,00 3 -,259 ,621 - ,1 25 ,036 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 764 https://internationalpubls.com Impro ved_ Rand D ,15 8 - ,1 6 5 - ,061 ,072 ,010 - ,00 9 ,06 5 ,112 ,014 - ,04 1 - ,09 5 ,015 -,125 ,7 54 - ,197 Increa sed_C ustom er_Sa tisfact ion ,05 5 - ,0 9 0 - ,020 ,045 ,051 - ,02 9 ,01 0 - ,094 -,142 - ,08 2 ,11 6 -,092 ,036 - ,1 97 ,758 A n ti - i m a g e C o r r e l a ti o n Impro ved_P roduc tivity ,45 9a - ,0 7 0 - ,063 ,110 - ,086 ,05 4 ,08 2 ,012 -,267 ,07 5 - ,03 8 ,110 -,137 ,1 99 ,070 Redu ced_ Cost - ,07 0 ,4 9 3a - ,021 -,035 - ,029 ,18 4 - ,20 7 - ,265 ,129 - ,02 1 ,08 2 ,002 -,068 - ,2 15 - ,116 Increa sed_C ompet itiven ess - ,06 3 - ,0 2 1 ,595 a -,208 - ,030 ,08 0 ,02 4 - ,096 ,008 - ,15 1 - ,01 9 -,049 ,045 - ,0 74 - ,025 Impro ved_ Brand _Rec ogniti on ,11 0 - ,0 3 5 - ,208 ,576a ,060 ,01 7 - ,22 3 ,060 ,054 - ,21 1 ,00 7 ,022 ,061 ,0 92 ,058 Impro ved_ Value - ,08 6 - ,0 2 9 - ,030 ,060 ,576 a - ,22 8 - ,03 5 ,078 ,158 ,01 5 - ,17 0 ,018 ,124 ,0 12 ,064 New_ Partn ership _Rela tionsh ip ,05 4 ,1 8 4 ,080 ,017 - ,228 ,55 4a - ,15 5 - ,119 ,058 - ,02 1 - ,09 4 -,188 ,115 - ,0 12 - ,037 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 765 https://internationalpubls.com Increa sed_T urnov er ,08 2 - ,2 0 7 ,024 -,223 - ,035 - ,15 5 ,50 7a ,185 ,003 ,08 9 - ,10 1 -,050 ,025 ,0 82 ,012 Impro ved_ Mark et_Sh are ,01 2 - ,2 6 5 - ,096 ,060 ,078 - ,11 9 ,18 5 ,421 a -,134 ,23 8 - ,07 3 -,105 ,067 ,1 45 - ,122 Econ omic_ Value _Addi tion_ Staff - ,26 7 ,1 2 9 ,008 ,054 ,158 ,05 8 ,00 3 - ,134 ,569a - ,30 5 - ,01 9 -,053 ,354 ,0 21 - ,210 Increa sed_ New_ Ideas ,07 5 - ,0 2 1 - ,151 -,211 ,015 - ,02 1 ,08 9 ,238 -,305 ,59 1a - ,16 0 ,100 ,041 - ,0 56 - ,114 Impro ved_ Qualit y_Ide as - ,03 8 ,0 8 2 - ,019 ,007 - ,170 - ,09 4 - ,10 1 - ,073 -,019 - ,16 0 ,54 1a -,214 ,004 - ,1 21 ,147 Effici ent_I dea_I mple menta tion ,11 0 ,0 0 2 - ,049 ,022 ,018 - ,18 8 - ,05 0 - ,105 -,053 ,10 0 - ,21 4 ,566a -,391 ,0 21 - ,126 Impro ved_ Resul tant_ Succe ss - ,13 7 - ,0 6 8 ,045 ,061 ,124 ,11 5 ,02 5 ,067 ,354 ,04 1 ,00 4 -,391 ,571a - ,1 82 ,053 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 766 https://internationalpubls.com Impro ved_ Rand D ,19 9 - ,2 1 5 - ,074 ,092 ,012 - ,01 2 ,08 2 ,145 ,021 - ,05 6 - ,12 1 ,021 -,182 ,5 40 a - ,261 Increa sed_C ustom er_Sa tisfact ion ,07 0 - ,1 1 6 - ,025 ,058 ,064 - ,03 7 ,01 2 - ,122 -,210 - ,11 4 ,14 7 -,126 ,053 - ,2 61 ,586 a a. Measures of sampling Adequacy(MSA) Communalities Initial Extraction Improved_Productivity 1,000 ,623 Reduced_Cost 1,000 ,600 Increased_Competitiveness 1,000 ,550 Improved_Brand_Recognition 1,000 ,628 Improved_Value 1,000 ,384 New_Partnership_Relationship 1,000 ,656 Increased_Turnover 1,000 ,570 Improved_Market_Share 1,000 ,726 Economic_Value_Addition_Staff 1,000 ,688 Increased_New_Ideas 1,000 ,677 Improved_Quality_Ideas 1,000 ,613 Efficient_Idea_Implementation 1,000 ,573 Improved_Resultant_Success 1,000 ,723 Improved_RandD 1,000 ,630 Increased_Customer_Satisfaction 1,000 ,663 Extraction Method: Principal Component Analysis. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 767 https://internationalpubls.com Explanation of Total Variance Compone nt Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Tota l % of Varianc e Cumulativ e % Tota l % of Varianc e Cumulativ e % Tota l % of Varianc e Cumulativ e % 1 2,15 8 14,388 14,388 2,15 8 14,388 14,388 1,91 5 12,768 12,768 2 1,79 8 11,987 26,376 1,79 8 11,987 26,376 1,64 5 10,967 23,735 3 1,69 0 11,264 37,639 1,69 0 11,264 37,639 1,55 6 10,375 34,110 4 1,43 5 9,570 47,209 1,43 5 9,570 47,209 1,41 4 9,424 43,534 5 1,14 3 7,619 54,828 1,14 3 7,619 54,828 1,39 3 9,284 52,818 6 1,07 8 7,190 62,018 1,07 8 7,190 62,018 1,38 0 9,200 62,018 7 ,977 6,510 68,528 8 ,895 5,969 74,497 9 ,757 5,050 79,547 10 ,639 4,260 83,807 11 ,596 3,973 87,780 12 ,550 3,668 91,448 13 ,484 3,226 94,675 14 ,447 2,983 97,658 15 ,351 2,342 100,000 Extraction Method: Principal Component Analysis. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 768 https://internationalpubls.com Component Matrixa Components 1 2 3 4 5 6 Improved_Productivity -,259 -,036 -,416 ,211 ,252 ,522 Reduced_Cost ,240 ,509 ,179 -,131 ,481 -,060 Increased_Competitiveness -,252 ,142 ,423 -,026 ,321 ,428 Improved_Brand_Recognition -,233 -,203 ,531 -,376 ,330 ,005 Improved_Value ,204 -,519 ,107 ,240 ,046 ,042 New_Partnership_Relationship ,242 -,425 ,238 ,513 -,044 -,308 Increased_Turnover ,208 -,322 ,425 -,183 ,370 -,269 Improved_Market_Share ,051 ,359 -,168 ,541 ,503 -,146 Economic_Value_Addition_Staff -,709 ,143 ,058 ,399 -,038 ,004 Increased_New_Ideas -,553 ,007 ,524 -,002 -,251 ,184 Improved_Quality_Ideas ,207 -,315 ,385 ,409 -,075 ,387 Efficient_Idea_Implementation ,584 ,131 ,228 ,362 ,002 ,178 Improved_Resultant_Success ,693 ,252 -,034 -,178 -,092 ,370 Improved_RandD ,267 ,484 ,417 -,046 -,383 ,031 Increased_Customer_Satisfaction -,160 ,598 ,304 ,306 -,131 -,276 Extraction Method: Principal Component Analysis. a. 6 components extracted. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 769 https://internationalpubls.com Rotated Component Matrixa Components 1 2 3 4 5 6 Improved_Productivity -,057 -,032 -,523 -,473 ,301 ,173 Reduced_Cost ,307 -,280 ,197 ,275 ,195 ,525 Increased_Competitiveness -,034 -,014 ,017 ,098 ,727 ,102 Improved_Brand_Recognition -,134 -,066 -,055 ,619 ,444 -,152 Improved_Value ,026 ,550 -,230 ,134 -,062 -,078 New_Partnership_Relationship -,157 ,709 ,075 ,187 -,281 ,095 Increased_Turnover ,064 ,203 -,057 ,720 ,028 ,047 Improved_Market_Share -,098 ,060 -,014 -,133 -,030 ,833 Economic_Value_Addition_Staff -,708 -,018 ,103 -,299 ,267 ,123 Increased_New_Ideas -,437 ,039 ,306 ,005 ,518 -,352 Improved_Quality_Ideas ,121 ,699 ,031 -,070 ,311 -,085 Efficient_Idea_Implementation ,459 ,461 ,265 -,082 ,047 ,265 Improved_Resultant_Success ,828 ,007 ,130 -,139 ,027 ,001 Improved_RandD ,295 -,025 ,722 -,051 ,110 -,081 Increased_Customer_Satisfaction -,264 -,089 ,682 -,098 ,056 ,328 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 12 iterations. Component Transformation Matrix Component 1 2 3 4 5 6 1 ,851 ,327 ,112 ,134 -,342 ,149 2 ,161 -,549 ,593 -,296 ,154 ,458 3 -,060 ,345 ,559 ,531 ,518 -,121 4 -,312 ,672 ,120 -,437 -,050 ,494 5 ,030 -,096 -,484 ,443 ,296 ,687 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 770 https://internationalpubls.com 6 ,386 ,113 -,274 -,475 ,708 -,192 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Exploratory Factor analysis is applied of collected data of 300 respondents for reducing the variables to few factors. The Kaiser-Meyer-Olkin measure of sampling adequacy (KMO = .548) is greater than the rule of thumb 0.50 and Bartlett’s test of sphericity is significant, which means that the included items in the scale do have correlation with each other. Thus factor analysis is found to be an approximate technique for construct validity. The variables which has loadings of less than 0.50 were excluded and dimensions with eigenvalues of more than 1 were retained. Results and Discussion For the present study 15 variables were analyzed to determine the employees’ perception about measures of innovation management in IT companies. Principal component analysis varimax rotation was employed for extracting the factors for data reduction in factor analysis; extracting only those factors having eigenvalues greater than 1. Six extracted components explained 62.018% variance of the data. So these six components will explain the combination of all the variables. As illustrated in the scree plot above, the maximum number of extractable factors is clearly indicated. The scree plot exhibits a steep downward slope initially, followed by a gradual levelling off into a nearly horizontal line. The inflection point, where the curve begins to straighten, suggests the optimal number of factors to extract. As shown in the scree plot, six factors meet this criterion. References 1. Barczak, G, KB Kahn and R Moss (2006). An Exploratory Investigation of NPD Practices in Nonprofit Organizations. Journal of Product Innovation Management, 23, 512-27. 2. Chang, Y-C, JD Linton and M-N Chen (2012). Service regime: An empirical analysis of innovation patterns in service firms. Technological Forecasting and Social Change, 79(9), 1569-1582. 3. Fernandes, C., Ferreira, J. And Marques, C. (2015) ‘Innovation management capabilities in rural and urban knowledge intensive business services: empirical evidence’ , Service Business, Vol. 9, No. 2, pp. 233-256, DOI: 10.1007/s11628-013-0225-7 4. Hwang, A. (2004) ‘ Integrating technology marketing and management innovation’, Research-Technology Management, Vol. 47, No. 4, pp. 27-31 5. Kuester, S, MC Schuhmacker, B Gast and Worgul (2013). Sectoral Heterogineity in New Service Development: An Exploratory Study of service Types and Success Factors. Journal of Product Innovation Management, 30(3), 533-544. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 1 (2025) 771 https://internationalpubls.com 6. Lemon, M. and Sahota, P.S.(2004) ‘ organizational culture as aknowledge repository for increased innovative capacity’ , Technovation, Vol. 24, No. 6, pp. 483-498 7. Tidd, J. and Bessant, J (2009) Managing Innovation – Integrating Technological, Market and Organizational change, John Wiley and Sons, England. 8. Zomerdijk, LG and CA Voss(2011). NSD Processes and Practices in Experiential Services. Journal Product Innovation Management, 28, 63-80.