IT capability,knowledge management,and product design

Abstract

This study analyzes the effects of information technology (IT) capacity with knowledge management (KM) on product design. IT capacity cannot provide competitive advantage in the market and thus the effect of KM as the mediating factor on this relationship must be examined. To test the hypotheses, data were collected through 220 questionnaires that were distributed to industrial companies in China. Results indicated that to achieve competitive advantage, companies should offer products that address customer needs through the design of identical product specifications, which are supported by KM and IT. Product design cannot succeed without knowledge of product qualities and characteristics. Moreover, KM in companies relies on IT capability, and IT capability with KM can be used to explain variations in product design. Thus, companies who want to achieve competitive advantage in the design of their products must use the capability of IT with KM in obtaining their objectives and be consistent with customer requirements to achieve consumer satisfaction. This study contributes to the literature of industrialization through the introduction of KM as a critical intermediary between IT capability and product design and elucidates the real industrial value of product design.

Keywords

Capability of using IT knowledge management classifications product design

Mariam Ibrahim is a doctoral student in business administration at the Graduate School of Management, Huazhong University of Science & Technology, China. Her research include the Information technology, knowledge management and production and operation management. before that, she was teacher at university of kufa. School of Administration and Economics. Business Administration Department.

Ma Huimin is the Associate Professor in the Department of Information Management, School of Management, Huazhong University of Science & Technology. Additionally he is also the Vice director of the Electronic Business Center of HUST in 2004. His major research field is the application of information technology in corporation, especially the application of Electronic Business.

1 Introduction

Technology is a statically complicated and dynamically complex human system. It has a lot to do both with or Art and with or Logos [1, p. 95]. While previously known, it is neither a thing, nor a tool or logical design: technology is a form of social relationship and only as such it can be properly understood, discussed and managed [2, p. 111]. According to the previous literature, “techne” is a Greek word meaning skill or art, and “logos” (language) is a concept focused on hardware, machines, software, and methods that convert input to output.

Companies use IT to improve the effectiveness and efficiency of employees and operations, reduce costs, and create innovative applications that lead to strategic advantage [3]. IT collects information that can help companies become more efficient, and companies work to exploit information for better work performance [4]. During the early 1970s and 1980s, many database technologies were used to address company information needs. In the 1980s and 1990s, IT was recognized as a means to sustain competitive advantage [5].

During the nineties 90s, questions were raised on the ability of IT to contribute significantly to the goal of a company. This phenomenon is called “paradox of productivity.” However, many studies have shown the ongoing importance of IT in creating business value and competitive advantage for companies [6].

Today, IT has become a basis of progress for countries and a benchmark for successful and leading companies [7].

Knowledge is widely recognized as the main source of the competitive advantage of firms, and in the increasingly turbulent and competitive environment [8 –10], knowledge is the basic rule of competition. Although the study of knowledge is as old as human history, this endeavor has only been recognized in the last decade as a crucial element in the industrial development of companies. Organizations believe that knowledge is the most valuable and strategic resource. Resource management and intellectual capabilities are essential to become and remain competitive. To this end, knowledge management (KM) has become a fundamental concept for understanding the ever-changing events in the business world. Organizations invest heavily in building KM systems, because they must strategically assess their knowledge and resources and develop their knowledge strategy to maintain competitive advantage [11]. Hence, a knowledge infrastructure consisting of technology, structure, and culture and a knowledge process architecture of acquisition, conversion, application, and protection are essential organizational capabilities required for effective KM [12].

The past decade has seen increased interest in design among scientists in management and management studies [13]. Early contributions of management scientists appeared mostly on niche outlets. This growing interest in administrative and management schools in design follows the increased awareness in education and the benefits derived by producers from well-designed goods and more intense cooperation with designers.

Design provides the diagram. When the design engineer’s keys are in a computer-aided design or when the artist designs the product, lines are drawn on a sheet of paper, and thus start activities. Product design has a huge impact on the choice of materials and components, suppliers, and machines or processes to be used for manufacturing the product, product storage, and product transport [14].

Even though most companies work to cope with environmental changes through the development of IT, they do not offer products with the same quality and do not achieve differentiation in the market. This study purpose to clarifying why industrial companies differ from achieving competitive advantage in their products. Although most companies are using IT and knowledge management in the design of their products.

2 Research model and hypotheses

Figure 1 illustrates the proposed model, which consists of three fundamental variables: capability of using IT; KM embodied by market, human, technological, and procedural knowledge; and product design. We assume the capability of using IT on KM and effects of KM on product design by its classifications.

Fig.1

Model proposed research.

2.1 Capacity to use IT

The IT capability of a company is the range of technologies required for storage, communication, and manipulation of information within the company. IT capability concentrates on information technologies, that is, hardware, software, and network, while information processing capacity involves mechanisms of structure, such as committees and teams. IT capability can increase the robustness of a company’s information processing. Therefore, IT capability is an attractive way of improving the efficiency and effectiveness of a company’s internal and inter-company operations. IT capability can assist the company in achieving competitive advantage [15]. According to [16] IT is the use of hardware, storage, networks, infrastructure, and processes to create, store, treat, secure, and share electronic data. Thus, IT capability may directly influence performance [15]. Therefore, we propose the following hypothesis:

H1: There is a positive relationship between capability of using IT and KM

2.2 Product Design

Product design determines the appearance of the product, sets standards for performance, identifies the materials to be used, and determines the dimensions and tolerances [17]. As product design is recognized as a means and valuable for differentiation and brand building, the brand is considered critical to creating and maintaining competitive advantage [18]. Product design is a key tool for gaining competitive advantage, as designers are directed to integrate targeted consumers into mental simulations to be more original, useful, and attractive to targeted customers [19], Product design is a process of knowledge and strategic efficiency that organizations apply to develop products and promote innovation, which contribute to the innovation process in the field of business. In addition, product design is a multi-functional activity that can be combined with different methods to adapt to institutional strategies and distinctive infrastructure and contextual and organizational structures. Product design converts a set of product requirements into a variety of materials, components, and elements [20]. This activity can affect the product appearance, manufacturing, and usage, as well as the effective use of materials and the functional performance, among others [21], This activity can address problems related to developing a successful product that is suitable to the needs of consumers [22], Thus, we proposed the following hypothesis:

H2: There is a positive relationship between capability of using KM and Product Design

2.3 Knowledge management

Knowledge Management (KM) is one of the factors involved in the success of product design [23]. KM is an essential process of product design and classified into four types [24, 25].

2.3.1 Market knowledge

Market knowledge refers to the external environment, which affects the decision of product design, such as knowing the number of people who will consume the product and what products are preferred [26] It involves knowing what users think of the performance and durability of the product and their position on the use of the product and expectations, and translating and applying that knowledge to the design of the product [27].

Knowledge of customer behavior and needs and market segments and competitors could come from information from the external environment. Hence, companies can benefit from the creative and educated human minds that characterize divergent information at all stages of the product cycle and convert this knowledge to benefit the company. The customer may intervene with producers in production, and the company must translate information correctly for product success and to prevent company loss in providing products that do not satisfy customer needs [28, 29].

A knowledge database for marketing is required to adapt quickly to the needs and desires of customers [30].

Through knowledge of the broad market, the company can design products that meet the diverse needs of the target sector, as various customers have different needs, and thus the company must comprehend and grasp the qualities of the external customers and competitors [31]. Thus, the following hypothesis is proposed:

H2a: There is a positive relationship between Market knowledge and product design

2.3.2 Human knowledge

Human knowledge includes tacit knowledge of the mental model of knowledge and tacit knowledge through courses, interviews, and others. Persons who do not have sufficient knowledge usually do not participate, and thus useless in work performance and hinder cooperative knowledge from reaching critical knowledge [32]. The haphazard design of the product through the human mind is not product design. Information, experience, and skill in human designers are required in decision-making in product design. Other activities include identifying resources, information retrieval, evaluation of results, encryption of data, and storage of information. In certain cases, the chosen design is inappropriate or ineffective because of the designer’s poor comprehension or misinterpretation of relevant information in product design [33]. Knowledge is embodied in the abilities, skills, and experiences of persons to engage in activities, such as product design. Certain companies hire experts with high knowledge in the manufacturing process because their expertise is important in the development and success of the company [26]. Experience within the framework of product design is the possession and awareness of knowledge, analysis and application, and the creative and analytical ability to extract and apply knowledge during the design process [34].

Therefore, we propose the following hypothesis:

H2b: There is a positive relationship between Human knowledge and product design

2.3.3 Technology knowledge

Technology knowledge includes complete designs in which humans and machines interact to complete the work [33]. Technology knowledge has a direct impact on product design, but is more effective with skill [35]. Under technological knowledge is the development and upgrade of operations and productions in most industrial companies, as confirmed by the scientific knowledge of the rules and principles and laws [36].

Previous studies have shown that technological development have a gradient effect on product design. Hence, technology knowledge base must allow the interaction between knowledge and the dimensions of the product [37], Technology knowledge is more than the laws, principles, theories, and variety of facts required to perceive and comprehend the outstanding features of products, as descriptive, prescriptive, and tacit knowledge is involved [38]. Thus, we propose the following hypothesis:

H2c: There is a positive relationship between Technology knowledge and product design.

2.3.4 Procedural knowledge

Procedural knowledge determines the manufacturing components of the product and works on the description of those components [39]. Here, knowledge is a series of steps or actions required to accomplish various goals or knowledge of the use of product, composition, skill, and action descriptions [40]. Procedural knowledge requires the conceptual comprehension [41] of product structure and knowledge of the product planning processes [42], Thus, we propose the following hypothesis:

H2d: There is a positive relationship between procedural knowledge and product design.

3 Data and sample

Data were collected using a questionnaire and the adoption of a 5-point Likert scale arranged from to strongly disagree (1) to strongly agree (5). The responses were analyzed using Excel and the SSPS system (correlation coefficient, t test, regression coefficient, mean, standard deviation, percentage of agreement, and relative importance). Samples were used to generate data and analyze and test hypotheses. Samples determine the failure or success of any study. A total of 220 questionnaires were distributed to selected Chinese company managers, department heads, engineers, designers, and employees engaged in product design. Table 1 describes the research sample.

Table 1
Describes the research sample

Variable Category Number Percent

The Enterprise Type Foreign-funded 20 10.0

Joint venture 20 10.0

State-owned 120 60.0

Joint-stock 20 10.0

Private 20 10.0

Period of the enterprise development more than 20 200 90.9

11-20 20 9.1

Total sales in 2015 (unit: Yuan) more than 500 million 120 54.5

100-500 m 60 27.3

50-100 m 40 18.2

Age categories 51-70 14 6.4

41-50 76 34.5

31-40 65 29.5

20-30 65 29.5

education Master’s degree or above 73 33.2

bachelor’s degree 146 66.4

Associate degree 1 0.5

period of service in the company more than 15 82 37.3

10-15 15 6.8

5-10 70 31.8

1-5 53 24.1

Variable	Category	Number	Percent
The Enterprise Type	Foreign-funded	20	10.0
	Joint venture	20	10.0
	State-owned	120	60.0
	Joint-stock	20	10.0
	Private	20	10.0
Period of the enterprise development	more than 20	200	90.9
	11-20	20	9.1
Total sales in 2015 (unit: Yuan)	more than 500 million	120	54.5
	100-500 m	60	27.3
	50-100 m	40	18.2
Age categories	51-70	14	6.4
	41-50	76	34.5
	31-40	65	29.5
	20-30	65	29.5
education	Master’s degree or above	73	33.2
	bachelor’s degree	146	66.4
	Associate degree	1	0.5
period of service in the company	more than 15	82	37.3
	10-15	15	6.8
	5-10	70	31.8
	1-5	53	24.1

4 Operationalization of constructs and instrument validation

The questionnaire was designed according to the literature review and other questionnaire standards, as well as the indicators consistent with the requirements of the research. Table (1) shows the results and measurements of the questionnaire results, while Table (2) shows the variables of the research.

Table 2
The variables of research [43 –52]

Main variables Main variables Number of items Code of items Reference

Capability of IT CIT 3 CIT1 [43, 44]

CIT2

CIT3

Market Knowledge MK 4 MK1 [24 , 45]

MK2

MK3

MK4

Human Knowledge HK 4 HK 1 [24 , 46]

HK2

HK3

HK 4

Technological Knowledge TK 3 TK 1 [24 , 48]

TK 2

TK 3

Procedural Knowledge PK 4 PK1 [24 , 49]

PK2

PK3

PK4

Product Design PD 4 PD1 [24 , 51].

PD2

PD3

PD4

It was emphasized the Reliability of scale through Cronbach’s coefficient where it was >0.70 (%) as in the Table (3).

Table 3

Reliability statistics

The main variable	The dimension	Cronbach Alpha
Capability of IT	Capability of IT	0.750633
Classification of Knowledge management	Market knowledge	0.929362
	Human knowledge	0.904401
	technological knowledge	0.913514
	Procedural knowledge	0.894040
Product design	Product design	0.900064

The validity of the measurement through internal correlation between items where we saw in the Table (4) as >90 and <20 [52].

Table 4

Inter-item correlation matrix

	CIT1	CIT2	CIT3	MK1	MK2	MK3	MK4	HK1	HK2	HK3	HK4	TK1	TK2	TK3	PK1	PK2	PK3	PK4	PD1	PD2	PD3	PD4
CIT1	1
CIT2	0.497^**	1
CIT3	0.553^**	0.471^**	1
MK1	0.547^**	0.486^**	0.684^**	1
MK2	0.617^**	0.473^**	0.745^**	0.786^**	1
MK3	0.605^**	0.449^**	0.685^**	0.749^**	0.827^**	1
MK4	0.410^**	0.399^**	0.705^**	0.752^**	0.757^**	0.736^**	1
HK1	0.534^**	0.378^**	0.780^**	0.694^**	0.773^**	0.700^**	0.769^**	1
HK2	0.417^**	0.358^**	0.725^**	0.658^**	0.700^**	0.632^**	0.797^**	0.787^**	1
HK3	0.508^**	0.410^**	0.641^**	0.698^**	0.761^**	0.808^**	0.675^**	0.651^**	0.567^**	1
HK4	0.605^**	0.548^**	0.752^**	0.892^**	0.876^**	0.862^**	0.824^**	0.772^**	0.718^**	0.792^**	1
TK1	0.559^**	0.435^**	0.842^**	0.685^**	0.742^**	0.692^**	0.707^**	0.776^**	0.732^**	0.654^**	0.751^**	1
TK2	0.500^**	0.415^**	0.809^**	0.716^**	0.767^**	0.715^**	0.746^**	0.776^**	0.724^**	0.672^**	0.780^**	0.792^**	1
TK3	0.546^**	0.431^**	0.762^**	0.717^**	0.764^**	0.707^**	0.738^**	0.711^**	0.692^**	0.658^**	0.779^**	0.756^**	0.792^**	1
PK1	0.421^**	0.306^**	0.624^**	0.476^**	0.555^**	0.519^**	0.643^**	0.656^**	0.642^**	0.520^**	0.551^**	0.582^**	0.591^**	0.610^**	1
PK2	0.441^**	0.376^**	0.773^**	0.600^**	0.665^**	0.617^**	0.757^**	0.831^**	0.750^**	0.580^**	0.673^**	0.732^**	0.769^**	0.684^**	0.712^**	1
PK3	0.491^**	0.418^**	0.848^**	0.674^**	0.684^**	0.698^**	0.707^**	0.743^**	0.670^**	0.639^**	0.741^**	0.811^**	0.755^**	0.729^**	0.590^**	0.746^**	1
PK4	0.540^**	0.445^**	0.824^**	0.754^**	0.804^**	0.764^**	0.807^**	0.772^**	0.717^**	0.707^**	0.837^**	0.759^**	0.846^**	0.829^**	0.566^**	0.717^**	0.763^**	1
PD1	0.542^**	0.447^**	0.833^**	0.725^**	0.771^**	0.736^**	0.781^**	0.818^**	0.803^**	0.670^**	0.797^**	0.850^**	0.809^**	0.699^**	0.581^**	0.752^**	0.775^**	0.790^**	1
PD2	0.386^**	0.349^**	0.762^**	0.585^**	0.628^**	0.615^**	0.623^**	0.712^**	0.620^**	0.570^**	0.672^**	0.738^**	0.762^**	0.649^**	0.538^**	0.674^**	0.777^**	0.704^**	0.717^**	1
PD3	0.501^**	0.377^**	0.636^**	0.554^**	0.650^**	0.617^**	0.627^**	0.662^**	0.630^**	0.643^**	0.634^**	0.688^**	0.662^**	0.686^**	0.668^**	0.668^**	0.619^**	0.644^**	0.578^**	0.580^**	1
PD4	0.535^**	0.421^**	0.791^**	0.673^**	0.740^**	0.708^**	0.756^**	0.861^**	0.732^**	0.694^**	0.744^**	0.793^**	0.793^**	0.795^**	0.731^**	0.811^**	0.780^**	0.768^**	0.739^**	0.713^**	0.836^**	1

^**Correlation is significant at the 0.01 level (2-tailed).

The previous data are valid for statistical analysis. Table 5 indicates that all the mean of research (i.e., agree) agrees with the research objectives. Thus, the researched companies have an acceptable level of application of research variables [53].

Table 5

Analysis of respondents’ responses according to the scale, the arithmetic mean, standard deviation, degree of agreement, and the strength of the answer to the variables of study

Items		1	2	3	4	5	Std.	Mean item	The direction of the sample	The direction and Mean of variable
CIT1	frequency	18	82	83	34	3	0.887	2.645	agree	3.447 AGREE
	percent	8.2	37.3	37.7	15.5	1.4
CIT2	frequency		17	67	106	30	0.805	3.677	agree
	percent		7.7	30.5	48.2	13.6
CIT3	frequency			52	112	56	0.702	4.018	agree
	percent			23.6	50.9	25.5
MK1	frequency			84	101	35	0.705	3.75	agree	3.732 AGREE
	percent			38.2	45.9	15.9
MK2	frequency			93	85	42	0.750	3.77	agree
	percent			42.3	38.6	19.1
MK3	frequency			100	91	29	0.696	3.68	agree
	percent			45.5	41.4	13.2
MK4	frequency		16	54	129	21	0.740	3.70	agree
	percent		7.3	24.5	58.6	9.5
HK1	frequency			35	141	44	0.599	4.04	agree	3.817 AGREE
	percent			15.9	64.1	20.0
HK2	frequency		14	42	123	41	0.786	3.87	agree
	percent		6.4	19.1	55.9	18.6
HK3	frequency			116	79	25	0.687	3.59	agree
	percent			52.7	35.9	11.4
HK4	frequency			83	104	33	0.691	3.77	agree
	percent			37.7	47.3	15.0
TK1	frequency			54	102	64	0.733	4.05	strongly agree	4.000 AGREE
	percent			24.5	46.4	29.1
TK2	frequency			62	95	63	0.755	4.00	agree
	percent			28.2	43.2	28.6
TK3	frequency			73	84	63	0.787	3.95	agree
	percent			33.2	38.2	28.6
PK1	frequency		17	3	126	74	0.796	4.17	agree	4.039 AGREE
	percent		7.7	1.4	57.3	33.6
PK2	frequency		17	23	119	61	0.833	4.02	agree
	percent		7.7	10.5	54.1	27.7
PK3	frequency			50	111	59	0.704	4.04	agree
	percent			22.7	50.5	26.8
PK4	frequency			68	100	52	0.737	3.93	agree
	percent			30.9	45.5	23.6
PD1	frequency			56	124	40	0.658	3.93	agree	4.086 AGREE
	percent			25.5	56.4	18.2
PD2	frequency			48	118	54	0.682	4.03	agree
	percent			21.8	53.6	24.5
PD3	frequency			15	129	76	0.582	4.28	strongly agree
	percent			6.8	58.6	34.5
PD4	frequency			35	100	65	0.693	4.15	agree
	percent			17.5	50.0	32.5
	percent			20.5	51.5	28.0

5 Results

We can explain the validity of hypotheses through the following sequence:

Table 6
Hypothesis 1, Summary regression model

Dependent independent Knowledge management

R R2 F tabular F Calculated sig β T tabular T Calculated Decision

Capability o IT 0.784 0.615 6.63 348.164 0.000 0.815 2.581 18.656 Accept

Dependent independent	Knowledge management
Capability o IT	0.784	0.615	6.63	348.164	0.000	0.815	2.581	18.656	Accept

Table 7

Hypothesis 2, Summary regression model

Dependent independent	Product design
	R	R2	F tabular	F Calculated	sig	β	T tabular	T Calculated	Decision
Knowledge Management	0.939	0.882	6.63	1623.994	0.000	0.853	2.581	40.229	Accept

Table 8

Hypothesis 2a, Summary regression model

Dependent independent	Product design
	R	R2	F tabular	F Calculated	sig	β	T tabular	T Calculated	Decision
Market Knowledge	0.847	0.718	6.63	554.184	0.000	0.739	2.581	23.541	Accept

Table 9

Hypothesis 2b, Summary regression model

Dependent independent	Product design
	R	R2	F tabular	F Calculated	sig	β	T tabular	T Calculated	Decision
Human Knowledge	0.904	0.817	6.63	973.136	0.000	0.846	2.581	31.195	Accept

Table 10

Hypothesis 2c, Summary regression model

Dependent independent	Product design
	R	R2	F tabular	F Calculated	sig	β	T tabular	T Calculated	Decision
Technological Knowledge	0.918	0.842	6.63	1164.925	0.000	0.750	2.581	34.131	Accept

Table 11

Hypothesis 2d, Summary regression model

Dependent independent	Product design
	R	R2	PD	F tabular	F Calculated	sig	β	T tabular	T Calculated	Decision
Procedural Knowledge	0.920	0.846	(1,218)	6.63	1201.359	0.000	0.786	2.581	34.661	Accept

H1: There is a positive relationship between capability of using IT and KM

In the same way we prove validity of the hypothesis: capability o IT has a statistically significant positive effect on product design, as Table 8, show R = 0.784 and R2 = 0.615 means 62% of change product design back to capability o IT, which prove the effect of capability o IT on knowledge management and confirms the validity of the regression mode is F calculated >F tabular at α= 0.01, in addition to, T calculated >T tabular, which emphasizes the significant regression coefficient of Hardware β= 0.815 at α= 0.01 and the degree of confidence = 0.99, The results confirms the true of acceptance of the third hypothesis (H1).

H2: There is a positive relationship between capability of using KM and Product Design

While Table 7, presents the results of the hypothesis with respect to Knowledge Management (independent variable) and product design (dependent variable). Results show that R = 0.939, R2 = 0.882, F(1,218) = 1623.994, T = 40.229, and β= 0.853, when α= 0.01, and the degree of confidence = 0.99. All acceptable results indicate the presence of a strong positive statistical correlation, and software change in knowledge management (88%) indicates the presence of the effect relationship and proves the validity of this effect that F Calculated >F tabular. Moreover, coefficient regression Knowledge Management β is proven significant, where T calculated >T tabular. Thus, we validate the hypothesis 2.

H2a: There is a positive relationship between Market knowledge and product design

The Table 11, shows results the effect independent variable (Market Knowledge), and dependent variable (Product design), R = 0.847, which indicates a strong positive correlation that is statistically significant at α= 1% and degree of confidence = 99%. R2 = 0.718, represents the variation ratio that moves Product design back to Market Knowledge. That is, Market Knowledge affects the Product design and proves the validity of the regression model, F(1,218) calculated >F tabular. However, it also proves a significant regression coefficient of Market Knowledge β= 0.739, which supports T calculated >T tabular by showing that we can only accept the hypothesis (H2a).

H2b: There is a positive relationship between Human knowledge and product design

The Table 11, shows results the effect independent variable (Human Knowledge), and dependent variable (Product design), R = 0.904, which indicates a strong positive correlation that is statistically significant at α= 1% and degree of confidence = 99%. R2 = 0.817, represents the variation ratio that moves Product design back to Human Knowledge. That is, Human Knowledge affects the Product design and proves the validity of the regression model, F(1,218) calculated >F tabular. However, it also proves a significant regression coefficient of Human Knowledge β= 0.846, which supports T calculated >T tabular by showing that we can only accept the hypothesis (H2b).

H2c: There is a positive relationship between Technology knowledge and product design

While Table 13, shows the results of the simple linear regression between Technological Knowledge (independent variable) and product design (dependent variable). A significant positive correlation exists at α= 0.01 wherein R = 0.918, The variance to product design by 78% back to Technological Knowledge, in accordance with R2 = 0.842, indicates that the Technological Knowledge affects product design and proves a significant effect that F(1,219) >F tabular. The significant regression coefficient of Network β= 0.750, and T calculated >T tabular at α= 0.01 and 0.99 = degree of confidence. These results validate the hypothesis (H2c).

H2d: There is a positive relationship between procedural knowledge and product design.

Whereas the Table 14, shows the power of significant positive relationship between the independent variable (Procedural Knowledge), and dependent variable (Product design), through R = 0.920, as it shows R2 = 0.846, that Product design affected ratio (85%), through Procedural Knowledge and the remainder of the effect is due to other factors, which confirms the validity of the model of Regression that F calculated >F scheduled at the α= 0.01, as well as significant regression coefficient of Procedural Knowledge β= 0.786, where T calculated >T tabular at α= 0.1 and the degree of confidence = 0.99, it is through the above results validate the hypothesis (H2d).

6 Epilogue

IT capability plays an active role in databases and promotes the exchange and re-use of knowledge. IT facilitates the sharing and use of knowledge in product design as shown in (Fig. 1). IT can support KM in accomplishing tasks efficiently, as indicated by the results on the capability of IT incentive toward efficient knowledge base management, and facilitate KM. This result supports the outcomes of other studies, thereby proving the support for KM [54].

IT support refers to the degree of KM support by IT capability [55]. Many researchers have reported that IT is a critical component of knowledge creation [56, 57]. IT capability affects knowledge in a variety of methods. IT facilitated rapid aggregation, storage, and knowledge sharing on an impractical scale in the past, and helps create knowledge [58]. Advanced technology integrates fragmented knowledge flows [55] and this integration can eliminate barriers to communication between departments in the organization. In addition, IT promotes all methods of knowledge creation and is not limited to the transfer of explicit knowledge [59].

This study is among the first attempts to integrate KM classifications and product design theoretically and practically in various industrial companies in China. KM classifications have an important role to play in product design and manufacture (Fig. 2).

Fig.2

Knowledge management classifications in the field of product design [24, 25].

Lastly, IT capability has positive effects on KM. IT and KM have significant effects on product design, Where it is one of the first studies that dealt with variables as in the (Fig. 1), and thus we draw the following conclusions:

The capability of using IT has a statistically significant positive effect on KM.

KM has a statistically significant positive effect on product design.

KM classifications have a positive impact on product design and important roles in providing products that satisfy the needs, requirements, and desires of customers.

IT capability has a significant effect on KM and product design.

KM significantly affects product design. Product design cannot be distinguished without sharing KM with production management and operations, and other management teams. Production and operation management cannot singularly operate. Product design is not the only specialization in production and operation management.

Companies need IT and KM in product design and development, and industrial companies follow principles of addressing customer requirements.

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Main variables	Main variables	Number of items	Code of items	Reference
Capability of IT	CIT	3	CIT1	[43, 44]
			CIT2
			CIT3
Market Knowledge	MK	4	MK1	[24 , 45]
			MK2
			MK3
			MK4
Human Knowledge	HK	4	HK 1	[24 , 46]
			HK2
			HK3
			HK 4
Technological Knowledge	TK	3	TK 1	[24 , 48]
			TK 2
			TK 3
Procedural Knowledge	PK	4	PK1	[24 , 49]
			PK2
			PK3
			PK4
Product Design	PD	4	PD1	[24 , 51].
			PD2
			PD3
			PD4

IT capability,knowledge management,and product design

Abstract

Keywords

1 Introduction

2 Research model and hypotheses

2.2 Product Design

2.3 Knowledge management

2.3.1 Market knowledge

2.3.2 Human knowledge

2.3.3 Technology knowledge

2.3.4 Procedural knowledge

3 Data and sample

Table 6 Hypothesis 1, Summary regression model Dependent independent Knowledge management R R2 F tabular F Calculated sig β T tabular T Calculated Decision Capability o IT 0.784 0.615 6.63 348.164 0.000 0.815 2.581 18.656 Accept

References

Table 6
Hypothesis 1, Summary regression model

Dependent independent Knowledge management

R R2 F tabular F Calculated sig β T tabular T Calculated Decision

Capability o IT 0.784 0.615 6.63 348.164 0.000 0.815 2.581 18.656 Accept