The role of inventory as a knowledge transfer mechanism

George P. Ball*, Ph.D. StudentOperations and Management Science Department

Carlson School of ManagementUniversity of Minnesota

321 Nineteenth Avenue SouthMinneapolis, MN 55455-0438

Voice (612) 626-9762Fax (612) 624-8804

E-mail ball0197@umn.edu

Rachna Shah, Associate ProfessorCarlson School of Management

Tel: (612) 624-4432Fax: (612) 624-8804

E-mail: shahx024@umn.edu

Arthur V. Hill, John & Nancy Lindahl ProfessorCarlson School of Management

Tel: (612) 624-4432Fax: (612) 624-8804

E-mail: ahill@umn.edu

February 21, 2011

*Corresponding author

The role of inventory as a knowledge transfer mechanism

Empirical research has found that manufacturing involvement in the new product development process enhances new product performance. However, very little research has explored the contextual factors that enhance this involvement. We hypothesize that people in a manufacturing environment with lower levels of inventory will be more knowledgeable about their products and processes and that this state of enhanced knowledge will drive more effective cross-functional involvement. Using data from a cross-section of international plants in three industries, we find that in the presence of lower levels of manufacturing inventory, manufacturing involvement in new product development is more effective.

Keywords: Cross-functional involvement, inventory reduction, new product development, innovativeness.

1. Introduction

Previous research has shown that involving manufacturing in the product development

process results in improved new product development performance. Similarly, new product

development researchers have shown that involving product development teams in the

manufacturing process also results in developing products fast and on-time (Mishra and Shah,

2009; Trygg, 1993; Youseff, 1994). This literature has shown that improving early

communication between manufacturing and design during the new product development process

can improve the performance of the new product development product and process (Swink &

Nair, 2007). However, most proponents of cross-functional involvement (whether it is

manufacturing teams in product development process or product development teams in the

manufacturing process) have primarily focused on the resulting new product performance

benefits; they do not explain why such involvement leads to improved performance. In the

current research, we propose one such mechanism to conceptually explain how this particular

mechanism relates cross-functional team involvement to new product performance.

Specifically, we invoke the “informational” perspective associated with inventory

reduction on production lines. The key tenet of this literature stream is the idea that low levels of

buffer inventories between workstations provide workers with context specific information that

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improves process reliability and solves quality related problems (Alles et al., 2000). Other

researchers have substantiated these findings conceptually and empirically. For example, studies

have shown that as inventory decreases, costs are reduced (Schonberger, 1986), quality is

improved (Fullerton and McWatters, 2001), and workers work faster and are more productive

(Schultz et al., 1999). Similarly, other researchers have suggested that the performance

outcomes occur because lower levels of inventory allow employees to identify problems

associated with manufacturing products or processes (Lieberman and Demeester, 1999). We

extend this logic to a new product development context. Specifically, we suggest that when

inventory levels are lower, early involvement of cross-functional teams is more effective which

results in improved new product performance.

We empirically examine our logic using plant level data from multiple respondents. Our

results substantiate the core logic of our argument. Using existing measures for central

constructs, we find that when inventory levels are lower, early involvement of cross-functional

teams result in a substantive and positive impact on new product performance. Our contribution

lies in connecting two research streams in the context of a new product development process.

An additional contribution is that we formalize the underlying logic to describe the mechanism

through which early involvement of cross-functional teams result in improved new product

performance. More specifically, this paper demonstrates that when firms reduce inventory, the

early involvement of cross-functional teams will be more effective.

The remainder of this paper proceeds as follows. In Section 2, we synthesize the

pertinent literature related to inventory reduction benefits and cross-functional involvement and

present our research hypothesis. Section 3 includes a short case study to illustrate the interplay

between manufacturing inventory levels, cross-functional involvement and new product

performance from a high-tech manufacturing firm. Section 4 explains the methods and measures

used to test the hypotheses, and section 5 presents the results and the conclusions from this study.

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2. Inventory reduction and cross-functional involvement

The rich literature on inventory reduction demonstrates multiple performance benefits for

the manufacturing function. Womack et al. (1999) document several case study examples of the

operational benefits of reducing inventory in manufacturing. These operational benefits include

deeper process and product understanding, cost reductions, and improvements in quality.

Schonberger (1986) develops many of the details behind Just-in-Time (JIT) manufacturing,

which have their foundation in reducing inventory levels to improve cycle time, repeatability and

quality. These two seminal bodies of work establish the logic of knowledge gained through

inventory reduction, and the benefits that this knowledge contains for the manufacturing

environment.

Lieberman and Demeester (1999) use empirical data to demonstrate that manufacturing

systems that reduce inventory allow problems to be quickly resolved, knowledge to be acquired,

and best practices to be identified. Manufacturing operators and engineers develop higher skills,

develop knowledge about their current processes, and apply this new knowledge internally in

manufacturing to drive improved operational performance. Alles et al. (2000) show how

reduced inventory levels drive creativity in the manufacturing environment. Their paper

analyzed data from 116 plants that showed that performance improved as inventory was reduced.

This performance resulted from new knowledge on the manufacturing line about products and

processes that developed as inventory was reduced and problems that were hiding under the

inventory were subsequently solved.

Flynn et al. (1999) found similar results in studying “World Class Manufacturing”

companies. Firms that embrace a Lean culture of lower inventory and waste reduction were able

to generate improved operational performance. Sakakibara et al. (1997) demonstrated that as

manufacturing inventory is reduced, several aspects of performance are improved, specifically:

flexibility, delivery, quality and cost. De Treville and Antonakis (2006) demonstrated that

intrinsic benefits arise from a manufacturing environment that stresses lower levels of inventory,

and these benefits result in operators who are more creative and more satisfied. This increased

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creativity develops into knowledge that is gained about the manufacturing products and

processes.

Fullerton and McWatters (2001) demonstrated empirically that as the level of inventory is

reduced, the level of employee flexibility goes up. As employees are more flexible, they are able

to perform more varied tasks. This amount of variation in their abilities allows them to have a

deeper understanding of the entire manufacturing process, not just their individual operations.

This deeper perspective translates into new knowledge that increases operational performance in

multiple ways. Fullerton and McWatters (2001) also see improvement in quality, inventory,

leadtime, equipment downtime, and customer response time as the level of inventory decreases.

Flynn, Sakakibara, and Schroeder (1995) explain the relationship between JIT and TQM, which

is a critical facet of understanding the knowledge creation aspect of lower levels of inventory.

They describe how JIT can lead to a more effective TQM program by eliminating excess

inventory and elevating problems that were hiding under the inventory. Problem elevation and

resolution creates knowledge in the manufacturing area that can lead to a more effective TQM

implementation in manufacturing. While this study shows that lower inventory levels create

knowledge that can assist other projects in the same functional area, it does not show that this

knowledge can actually travel outside of manufacturing.

In summary, past research has found significant benefits in reducing inventory for the

manufacturing organization, including quality, cost, speed, and even worker satisfaction.

However, we observe that this stream of research has only considered the benefits to the

manufacturing organization alone. This research will consider the benefits of reducing inventory

to product development as well.

A mechanism is needed for the knowledge gained through inventory reduction to expand

outside of the manufacturing environment to impact other functional areas. We propose that

cross-functional involvement is the mechanism that can promote knowledge between

manufacturing and new product development and therefore allow inventory reduction knowledge

to travel.

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Cross-functional involvement has been shown to be a critical aspect of an effective

manufacturing strategy (Swamidass and Newell, 1987) as it can serve as an effective mechanism

for driving improved performance in the new product development process (Adler, 1995). Adler

(1995) identifies taxonomies of manufacturing and new product development coordination,

ranging from least coordinated to most coordinated. In his most coordinated taxon, he describes

an environment in which manufacturing and new product development engineers work jointly to

design the products and processes. This environment has frequent interaction between

manufacturing and new product development at early stages of the design. The purpose of this

interaction is to design a product that is both marketable and manufacturable.

When the integration between manufacturing and design is in a tight, highly coordinated

state, new product development complexities can be overcome (Swink and Nair, 2007). When

these complexities are overcome, the new product development team is able to design a product

with more innovative and creative features, which can better serve the customer. In addition to

enabling more innovative designs, cross-functional involvement also improves the quality and

reliability of the new product (Ha and Porteus, 1995; Trygg, 1993; Youseff, 1994). This quality

control benefit finds its source in a more manufacturable design of products and processes that

will perform better in manufacturing.

Kumar and Motwani (1995) describe concurrent engineering, a form of cross-functional

involvement, as a tool to eliminate rework in the design process and therefore increase the speed

to market. Concurrent engineering is defined as not just concurrent work flows, but also

includes a required element of early cross-functional involvement of constituents (Koufteros,

Vonderembse, and Doll, 2001). Koufteros, Vonderembse, and Doll (2001) define three aspects

of concurrent engineering: 1) Early involvement of participants, 2) Team approach, and 3)

Simultaneous work on different aspects of the project. Mishra and Shah (2009) also demonstrate

the value of cross-functional involvement on project performance and as a critical aspect of

collaborative competence.

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We intend to couple these two bodies of research of inventory reduction and cross-

functional involvement and demonstrate that they have a critical linkage that has been previously

unrecognized. The knowledge gained through inventory reduction has been shown to drive

operational performance in cost, quality and time (Sakakibara et al., 1997; Flynn et al, 1999; De

Treville and Antonakis, 2006; Fullerton and McWatters, 2001; Lieberman and Demeester, 1999).

The cross-functional involvement literature has shown that early manufacturing involvement in

the new product development process can lead to improved product cost, quality, reliability, and

quicker time to market (Adler, 1997; Swink and Nair, 2007; Ha and Porteus, 1995; Swamidass

and Newell, 1987).

With lower inventories, manufacturing involvement in the new product development

process is “activated” with more knowledge about the limitations of the process and the

products. In the presence of lower inventories, problems that were hiding are resolved. These

problems are opportunities for new knowledge. The problems may revolve around process limits

that were previously misunderstood or product characteristics that exhibit unique attributes under

certain conditions. Both of these examples, when solved by the manufacturing team, are the

sources of new knowledge that have been empirically shown to benefit the manufacturing

function. This knowledge however, we argue, can become a significant contributor to the

effectiveness of manufacturing cross-functional involvement in the new product development

process. As shown in Figure 1, we propose the following hypothesis:

H1: Manufacturing cross-functional involvement in the new product development process is associated with higher new product performance in the presence of lower inventory than when inventory levels are higher.

3. Case study-Midwest Telemetry Solutions

We see in the above sections that inventory reduction benefits many aspects of

manufacturing performance, and that cross-functional involvement benefits many aspects of new

product development performance. The question is “why and how are these two phenomena

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related?” Because the linkage between these two concepts is not well established in the

literature, we chose to start our theory development with a specific case study of a high-tech

manufacturing firm headquartered in the USA; Midwest Telemetry Solutions (MTS). This firm

was transitioning from a small start-up to a high-volume manufacturing firm. A critical aspect of

this transition was lowering inventories to reduce product leadtime and improve quality.

The manufacturing process of MTS was broken down into five distinct processes. The

third process was a form of high-tech welding. In a start-up mode, the firm did not possess the

equipment or technical abilities to manage welding in-house. The leadtime for their devices was

more than four weeks. A large portion of this lead time included sending the product to a vendor

for the welding process, which added one week to inventory and leadtime.

The firm used two approaches to reduce inventory. First, it dramatically reduced lot

sizes. In the start-up mode, lot sizes were over 200 units. This created large work-in-process

inventories and contributed significantly to a multi-week product leadtime. Second, it

redesigned the manufacturing layout for quicker handoffs between processes, tighter feedback

between processes, and shorter travel distances for both products and people. These efforts to

reduce inventory became a catalyst for dramatic improvements in manufacturing cycle time and

performance. Surprisingly, these efforts also contributed to MTS’s new product innovativeness.

As the lot sizes were reduced and equipment was brought closer together, the fact that the

welding process was performed off-site came became an obvious issue. With smaller lot sizes,

more lots had to be sent to the off-site vendor, which increased handling and transactional costs.

Additionally, the drive to reduce inventories was hindered by the several days of product being

kept in transit and off-site at the vendor’s facility. Therefore, the firm made the strategic

decision to in-source the welding process. In-sourcing a high-tech device welding process

requires significant investment in equipment and training; however, the goal of a faster, more

responsive manufacturing environment with low lead times and low inventories was of strategic

importance to the firm. As the equipment and technological expertise were brought in-house, the

manufacturing engineering organization developed deep expertise on this welding process. What

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had at one time been a “black box” for both the R&D and manufacturing organizations became a

well-understood and controllable critical process on the manufacturing line.

A major result of this in-sourcing was dramatically reduced inventory and leadtime. The

firm’s manufacturing leadtime decreased from over four weeks to less than two days. However,

an additional benefit was the technical expertise gained in the welding process. One result of

this new insight was the ability to weld smaller devices. As the technical skills improved,

manufacturing’s ability to create very small welds had a ripple effect in the firm’s new product

development organization.

MTS espoused significant cross-functional involvement of the manufacturing personnel

in the new product development process. For example, manufacturing engineers were assigned

to every new product development team. There were both generalists and process specialists

from manufacturing that partnered with new product development. These process specialists

were tasked with ensuring a tight linkage between new product development and

manufacturing’s capabilities. With the in-sourcing of welding, the manufacturing team members

on the new product development teams were able to influence new designs by helping to create

products with previously unimaginable weld sizes. The smaller device sizes translated into

product innovativeness and competitiveness.

In this example, we see how a manufacturing line that operates in an environment of

lower inventories can create a more powerful cross-functional involvement with new product

development that leads to improved new product innovativeness. This example also illustrates

common Lean approaches to reducing inventories with both lot size reduction and equipment

layout.

4. Measures and methods

The data used for this analysis were collected as a part of the third round of High

Performance Manufacturing (HPM) project. The HPM survey is a multi-country and multi-

industry project conducted by a team of international researchers. HPM was designed to assess a

manufacturing plant’s operation from multiple facets. In round 3, data were collected from nine

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countries in three industries. These countries broadly represent the industrialized world. The

sample included both traditional and high performing manufacturing plants. For more details on

data collection during rounds 1 and 2, see Flynn et al. (1995) and Sakakibara et al. (1997).

Because participation in the study involved extensive effort for the participating firms, an

assigned research team member solicited plant participation by contacting their assigned plant

manager to describe the study and relay the benefits of participation. Plants that agreed to

participate were asked to assign a survey coordinator who served as the liaison with the research

team on all matters. A packet containing 21 surveys was mailed to each plant research

coordinator. This person then distributed the surveys to the appropriate individual in the plant

and mailed the response back to the research team. For more details on the protocol followed for

round 3, please see Peng et al. (2007). The final data set consists of 266 responses which amount

to a 65% response rate. We addressed non-response bias by comparing the plant size and annual

revenue of the responding and non-responding plants. We did not find any evidence of a

systematic non-response bias.

Conceptually, lower levels of work in process inventory are closely associated with

equipment that is laid out in close proximity of each other and small production lot sizes. We

argue that a production line laid out in a manner that allows quick handoff between processes

and minimal movement waste allows managers to reduce work-in-process inventory levels.

Similarly, a plant that continually drives down its lot size will inherently have lower levels of

inventories. We take advantage of this association and develop two different scales to measure

inventory. We measure equipment layout using five items measured on a seven-point scale. The

scale items measure how machines in the process are located on the shop floor. For instance,

E_layout01 (we have laid out the shop floor so that processes and machines are in close

proximity to each other) and E_Layout03 (our processes are located close together, so that

material handling and part storage are minimized) directly address machine layout on the shop

floor. We use a three item scale to measure lot size. Each of the three items assess whether the

plant uses small lot sizes during production in the plant. See for example, Lot_S01 (we have

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large lot sizes in our plants) and Lot_S03 (we make our products in the smallest lot size

possible). Both of these scales have been used in previous research to characterize the

production process in the plant (Flynn et al., 1995; Sakakibara et al., 1997). Although both of

these scales are indirect measures of inventory, conducting our analysis with two different scales,

enables us to test the robustness of our conceptual model.

We use an existing scale to measure early involvement of new product development

teams in manufacturing (Swink and Nair, 2007; Mishra and Shah, 2009). The scale includes four

items measured on a seven-point scale.

Product development performance is a broad concept with many underlying dimensions.

For instance, product development performance can be assessed as cost, quality, time to market

and innovativeness, both as an absolute and comparative measure (e.g., relative to the target goal

or relative to the industry leader). In this study, we measure product development performance

as innovativeness of new products relative to the competition.

We use four control variables in our study. Previous NPD research has noted the impact

of industry membership and country location on a firm’s approach to product development

(Hoskisson and Hitt, 1990; Ettlie, 1995). Industry membership is an important control variable

because different industries are characterized by different rates of new product introductions and

technological sophistication (Atuahene-Gima and Li, 2004).

Firm size is frequently associated with resource availability and bureaucratic structures.

Larger firms have more resources to carry out product development tasks but are also more

likely to have longer development periods because of complex and formal structures (Ettlie,

1995). Therefore, firm size is used as a control variable and is measured as the natural logarithm

of the total number of employees (number of hourly and salaried employees).

Another source of variation for product development innovativeness comes from average

life cycle of the product. Longer life cycle products are typically associated with fewer

innovations. Thus, we control for the effect of average life cycle of the product on product

performance and measure it using average life cycle of the product, in years. All the items used

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in the study are listed in the Appendix. Descriptive statistics for the items and sample

distribution by country and industry are provided in Tables 1 and 2 respectively.

We used Principal Components Analysis for dimension reduction and to determine

unidimensionality of each of the scales. We conducted Principal Components Analysis for each

scale separately, i.e. we entered only the items used to measure one concept in conducting

Principal Components Analysis. If all the items loaded on only one major component, we

deemed it to be unidimensional. All three of our multi-item scales loaded on one major

component, with eigenvalue exceeding one and variance explained ranging from 53 percent to 71

percent (Table 4). We created a single component score for each of our scales using the Bartlett

factor score in SPSS 17.0. These scores are subsequently used in conducting regression analysis.

Scale reliability for each of the three scales was assessed using Cronbach’s alpha (1951); it

exceeded a 0.7 cutoff for each of the three scales (Table 4).

We test our hypothesis using sub-group analysis. Our hypothesis can be classified into a

general class commonly referred to as “fit as moderation” and can be further specified as

“moderation as strength,” which can be formally stated as “the relationship between two

variables differs depending on the level of the third variable” (Venkatraman, 1989). Moderation

as strength is conceptually and empirically distinct from moderation as “the joint effects of two

variables on the third variable” (Venkatraman, 1989). To test our hypothesis, we created two

mutually exclusive subgroups using equipment layout and lot size component scores, our

operational measures of inventory reduction. Specifically, we divided the data into two groups

using the average equipment layout and lot size component scores. We label the equipment

layout sub-group with lower than the average value “distant layout” and the sub-group with

above average values “proximal layout.” Similarly, we label the lot size sub-group with lower

than average value “larger lot sizes” and sub-group with above average values “smaller lot

sizes.”

We conducted regression analyses in each of the sub groups separately to examine the

effect of cross-functional involvement on product innovativeness. In each of the regression

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models, we first entered our control variables (Model 1) and then added cross-functional

involvement, our substantive variable of interest (Model 2). We can assess the association

between cross-functional involvement and product innovativeness by examining the significance

of the cross-functional coefficient in Model 2 of each of the regression results. To compare

whether the relationship between cross-functional involvement and product innovativeness

differs across the levels of equipment layout (lot size), we can compare the cross-functional

coefficient in Model 2 of the distant and proximal layout (larger and smaller lot size) regression

results. Statistically, the former constitutes a within sub-group comparison where as the latter

constitutes an across sub-group comparison in each of the regressions. The results of the

equipment layout and lot size analyses are reported in Tables 6 and 7 respectively.

The results show that in the distant layout sub-sample, neither Model 1 nor Model 2 is

statistically significant, hence obviating the need to interpret the cross-functional involvement

coefficient (Table 5). However, in the proximal layout sub-sample both Models 1 and 2 explain

a significant amount of variance in product innovativeness (R2 = 0.303 and 0.371 respectively)

and are highly significant (p ≤ 0.05). Additionally, Model 2 explains a significant amount of

incremental variance (R2 change = 0.068 percent, p ≤ 0.05) and the cross-functional involvement

coefficient is positive and significant (coeff = 0.247, p ≤ 0.05). The results imply that in an

environment where the equipment layout is spread out and not conducive to lower inventories,

cross-functional involvement does not significantly contribute to product innovativeness where

as when equipment is closely laid together, cross-functional involvement has a significant,

positive impact on product innovativeness.

We find similar results with lot size. In the larger lot size sub-sample, while Model 1 is

statistically significant (R2 = 0.259 percent, p ≤ 0.05), Model 2 and cross-functional involvement

coefficient is not (Table 6). In the smaller lot size sub-sample, Model 2 explains a significant

amount of variance in product innovativeness (R2 = 0.244, p ≤ 0.10) and the cross-functional

involvement coefficient is positive and significant (coeff = 0.173, p ≤ 0.10). While the results

are weaker than the equipment layout model, the direction and significance of the results are

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supportive of the trend. The results imply that in an environment with smaller lot sizes, cross-

functional involvement has a significant, positive impact on product innovativeness.

5. Discussion and conclusions

This research has generated new insights related to the important relationship between

inventory reduction, cross-functional involvement and new product development. These are not

often thought of in the same context and have not been researched in this manner in the past.

What we see through this analysis is that the knowledge gained through lowering inventory

levels in manufacturing provides a critical input into the cross-functional interaction between

manufacturing and new product development.

Our research supports the many past empirical pieces that argue that cross-functional

involvement significantly contributes to new product performance. However, our divergence

lies in the context of this involvement. We ask and answer the question: “when does the

involvement help more?” In hypothesizing the circumstances that would lead to a more

informed manufacturing participation, we turned to the literature that demonstrates the value of

lower levels of inventory in manufacturing. We add to this literature stream by demonstrating

that knowledge gained through reducing inventory can benefit new product development as well

as manufacturing. This knowledge can provide useful insights to the cross-functional

relationships between manufacturing and new product development. We therefore see

contributions to both streams of literature in this research. The inventory reduction literature is

more complete, as we demonstrate a new use for the knowledge gained in that environment. The

cross-functional involvement literature is richer due to a context that is provided through our

research.

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Appendix: Measures

Responses to Equipment Layout, Lot size, and Cross-functional Involvement are given on a 7-point Likert scale (1=Strong disagreement, 7=Strong agreement)

Equipment Layout Variables (α = 0.81)E_Layout01 We have laid out the shop floor so that processes and machines are in close

proximity to each other.E_Layout02 The layout of our shop floor facilitates low inventories and fast throughput. E_Layout03 Our processes are located close together, so that material handling and part

storage are minimized. E_Layout04 We have located our machines to support JIT production flow. E_Layout05 We consistently monitor work-in-process inventory in front of each process to

identify the bottleneck (constraint) in the system.

Lot size Variables (α = 0.71)Lot_S01 We have large lots sizes in our plantLot_S02 We emphasizes small lot sizes, to increase manufacturing flexibilityLot_S03 We make our products in the smallest lot sizes possible

Cross-functional Involvement Variables (α = 0.70)Xfunc01 New product design teams have frequent interaction with the manufacturing

function. Xfunc02 Manufacturing is involved at the early stages of new product development. Xfunc03 The manufacturing function is key in improving new product concepts. Xfunc04 Manufacturing is given challenging tasks in the development of new product

concepts.

NPD VariablePlease rate how your plant compares to its competition on a global basis (1=Poor, low end of industry, 2=Equivalent to competition, 3=Average, 4=Better than average, 5=Superior)

NPD Rate your firm’s new product innovativeness

Control VariablesLifecycle (LIFE) and number of employees (Emp) are measured objectively as follows:

LIFE What is the average lifecycle of your products in years?Employees What is the number of hourly and salaried employees at your plant?

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Manufacturing involvement in new product development New product innovativeness

Work-in-process inventory

Figure 1. The conceptual model

Table 1. Sample profile

Number of plants by industry

Country Electronics MachineryAuto

suppliers TotalAustria 10 7 4 21

Finland 14 6 10 30

Germany 9 13 19 41

Italy 10 10 7 27

Japan 10 12 13 35

South Korea 10 10 11 31

Spain 9 9 10 28

Sweden 7 10 7 24

United States 9 11 9 29

Totals 88 88 90 266

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Table 2. Descriptive statistics

N Min Max Mean Std. Dev Var.NPD 239 1.00 5.00 3.6151 0.92715 0.860

# of Employees 230 51.00 41589 817.11 2840.04 8065850.04LIFE 206 0.00 40.00 8.8653 6.64099 44.103

E_Layout01 266 2.67 7.00 5.3588 0.83025 0.689E_Layout02 266 1.83 7.00 4.9666 0.97434 0.949E_Layout03 266 2.17 7.00 5.0807 0.92469 0.855E_Layout04 266 2.00 6.67 4.7048 0.99715 0.994E_Layout04 266 2.00 7.00 5.0095 0.97297 0.947

Xfunc01 238 2.00 7.00 5.5126 1.24518 1.550Xfunc02 236 1.00 7.00 5.2924 1.31574 1.731Xfunc03 237 1.00 7.00 5.0380 1.30621 1.706Xfunc04 236 1.00 7.00 4.6017 1.34411 1.807Lot_S01 266 1.56 6.89 4.6646 1.19677 1.432Lot_S02 266 2.00 7.00 4.7668 1.04032 1.082Lot_S03 266 1.50 6.89 4.3520 1.18291 1.399

Table 3. Pearson correlation matrix

Variable name NPD LIFE E_Layout Xfunc Lot_S

NPD 1

LIFE 0.159* 1

E_Layout 0.203** – 0.170* 1

Xfuncl Involve 0.156* 0.015 0.167** 1

Lot_S 0.062 0.121 0.357** –0.005 1* Listwise deletion method used

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Table 4. Principal component analysis results

Equip_Layout X_Funcl_Involve Lot sizeLoading on the first component

Loading on the first component

Loading on the first component

E_Layout01 0.809 Xfunc01 0.757 Lot_S01 0.797E_Layout02 0.839 Xfunc02 0.850 Lot_S02 0.861E_Layout03 0.841 Xfunc03 0.588 Lot_S03 0.867E_Layout04 0.702 Xfunc04 0.690E_Layout05 0.594

Eigenvalue 2.91 2.12 2.13% of Var

Explained 58.23 52.95 70.97Cronbach Alpha

(sample N)

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Table 5. Regression analysis results on subgroups based on equipment layout

Distant layout Proximal layoutModel 1 Model 2 Model 1 Model 2

Unstdized Coeffs S.E. Unstdized

Coeffs S.E. Unstdized Coeffs S.E. Unstdized

Coeffs S.E.

Indy dummy1 –0.149 0.243 –0.149 0.242 0.294 0.234 0.227 0.225Indy dummy 2 –0.096 0.248 –0.091 0.246 0.274 0.246 0.298 0.236

Austria dummy 0.582 0.468 0.601 0.466 –0.549 0.444 –0.560 0.425Finland dummy –0.005 0.424 –0.058 0.424 –0.709* 0.402 –0.624 0.386

Germany dummy 0.497 0.335 0.436 0.336 –0.408 0.398 –0.296 0.383Italy dummy 0.346 0.380 0.169 0.400 –0.738* 0.411 –0.560 0.398

Japan dummy 0.032 0.372 –0.041 0.374 –0.870** 0.422 –0.780* 0.404Spain dummy 0.394 0.388 0.373 0.387 –0.311 0.394 –0.195 0.379U.S. dummy 0.034 0.606 –0.071 0.608 –1.432** 0.555 –1.390** 0.531

Ln(employees) 0.058 0.129 0.063 0.128 0.341*** 0.099 0.286** 0.096LIFE 0.039** 0.015 0.037** 0.015 –0.012 0.015 –0.017 0.015

X-Funcl Involve –0.158 0.117 0.247** 0.089

Overall R² 0.147 0.166 0.303** 0.371**ΔR² 0.020 0.068**

N 91 91 84 84

Dependent variable: New product development innovativeness (NPD)*Significance at 0.10 in a two–tailed test, **at 0.05, *** at 0.001.

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Table 6. Regression analysis results on subgroups based on lotsize

Larger lotsizes Smaller lotsizesModel 1 Model 2 Model 1 Model 2

Unstdized Coeffs S.E. Unstdized

Coeffs S.E. Unstdized Coeffs S.E. Unstdized

Coeffs S.E.

Indy dummy1 0.061 0.261 0.076 0.265 0.216 0.228 0.241 0.225Indy dummy 2 0.302 0.242 0.308 0.244 –0.076 0.288 –0.020 0.286

Austria dummy –0.719 0.496 –0.724 0.499 0.965** 0.417 0.927** 0.412Finland dummy –0.362 0.391 –0.360 0.393 0.185 0.419 0.318 0.421

Germany dummy –0.044 0.375 –0.068 0.383 0.386 0.347 0.434 0.343Italy dummy –0.527 0.384 –0.550 0.391 0.327 0.387 0.571 0.407

Japan dummy –0.525 0.370 –0.537 0.373 –0.258 0.466 –0.152 0.463Korea dummy –1.192* 0.623 –1.211* 0.628 –0.274 0.716 –0.007 0.722Spain dummy 0.518 0.419 0.523 0.421 0.081 0.362 0.188 0.363U.S. dummy –0.958 0.631 –0.969 0.636 –0.224 0.518 –0.135 0.514

Ln(employees) 0.289** 0.110 0.294** 0.112 0.133 0.114 0.099 0.114LIFE 0.015 0.014 0.015 0.015 0.035** 0.017 0.034** 0.017

X-Funcl Involve –0.040 0.112 0.173* 0.101

Overall R² 0.259** 0.260** 0.211 0.244*ΔR² 0.001 0.033*

N 93 93 82 82

Dependent Variable: New product development innovativeness, NPD*Significance at 0.10 in a two-tailed test, **at .05, *** at .001

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