The first paper analyzed talks about the resource-based approach present in multibusiness firms, and it was written
by Robins and Wiersema in 1995.
The resource-based view of the firm has proven to be significant over the years in explaining differences in financial
performance inside corporations. Due to the lack of factual research linked to this view, the main purpose of this
paper is to develop an approach in order to analyze in depth the theory of the multibusiness firm and the empirical
studies between corporate portfolio and performance.
Chandlers (1962) has developed a pioneer work about the effect of combining different businesses within a single
organization. He argued that expanding into new areas of business represents a managerial problem for firms that
could produce operational inefficiency. Ten years later, in 1974, Rumelt carried out a quantitative study about the
correlation between the structure’s complexity of firms and the economic performance, distinguishing firms by type
of diversification and structure. It was found out that companies with interrelated business portfolios appeared more
profitable with respect to firms with unrelated ones. Other researchers have gone into conflict with Rumlet’s
findings: Montgomery (1985) for example, showed that industry profitability and market share are more important in
defining performance (they explain about 12% of variance in firm performance), while portfolio relatedness has no
explanatory power over this.
During the last decade, the explanation about the relationship between portfolio composition and economic
performance emerged as a concept linked to the development of the resource-based view of the firm, arguing that a
wide business portfolio has a positive effect on firm performance and in absence of strategic assets, the Rumelt’s
findings are confirmed.
In order to study the relatedness between businesses, it’s important to consider two different concepts: categorical
and continuous measures. The categorical measures, built on the work of Rumelt (1974) and Wrigley (1970), involve
the classification of a firm in terms of one of several characteristic types of diversification, while continuous
measures, that are based on the Standard Industrial Classification (SIC) system, put a firm on a scale that indicates
the degree of related or unrelated diversification. Continuous measures offer researchers a broader offering of
techniques for the analysis, employing data classified among standard categories. They use data reported applying
the SIC system (Hoskisson et al.,1993). Two important continuous measures in order to study the relatedness are the
entropy index and the concentric one. The first is an adaptation of the Herfindahl index and it has been applied for
research on corporate strategy (Montgomery and Hariharan, 1991; Montgomery and Wernerfelt, 1988).
It measures the level of diversification of a k-th firm in different industries, considering the different percentage of
sales in them, while weighting the affiliation of the industries to one SIC category rather than another.
On the SIC system, difficult assumptions should be made: the first one implies the homogeneity of industries within
category levels, the second that the “relatedness” scale should reflect quantitative distinctions between category
levels (Rumelt, 1982).
Similarly, the entropy index (Jacquemin and Berry, 1979) involves assumptions about the SIC system and it studies the
proportion of a firm’s sales in a SIC industry i. In this case, Index’s problems are linked to the inaccuracy of the
assumptions: it’s necessary adding additional sources of information in order to diminish the levels of measurement
error. In general, the SIC system is a weak source of information: it contains only limited information of the types of
strategic interrelationships (Teece, 1982) and neither continuous nor categorical measures differentiate extent of
diversification from relatedness within corporate business portfolios. This could be because some researchers (Teece,
1982; Peteraf, 1993) have argued that the interrelationships among businesses should have independent effect
regarding the firm’s diversification.
In general, additional information are necessary in the resource-based approach and recently, some works have used
the distribution of occupations across industries as a source of strategic importance.
Some scholars (Teece, 1986; Barney, 1986; Winter, 1987) developed alternatives to measure the relatedness of
industries: they identified common business factors with importance in a strategic perspective, like the managerial
and the technical knowledge. The disadvantage is that they are difficult to formalize and reproduce so, for this
reason, they can be useful as strategic assets and offer an important competitive advantage (Barney, 1991).
Farjoun (1994) has argued the possibility of developing indirect indicators based on secondary data about industrial
activity. They are not measured directly because they are forms of knowledge or capability.
One of the most useful indicators is the pattern of technology flows among industries, which underlines strategic
capabilities. In fact, critical scope economies or synergies are the result of shared know-how or capabilities (Teece,
1982), they are not a consequence of sharing inputs. These forms of shared knowledge in many cases are less well
articulated than the measurable technological inputs that these tacit capabilities help firms to utilize (Winter, 1987).
These capabilities have a tacit character that makes them difficult to measure directly.
Technology flows among industries can be used in order to estimate portfolio interrelationships, and the process
involves two steps: firstly, the creation of measures of strategic similarity and then, the derivation of a firm-level
index of interconnection among businesses of specific corporate portfolios.
Some measures of strategic similarity were estimated by Scherer (1982), who constructed a matrix of R&D flows
between major groups of industries, combining information about input and output data.
Subsequently, in order to understand better the impact of similarities, it was necessary to create an index, using the
percentage of sales of a k-th firm in an industry category i, in an industry category j and the structural equivalence
similarity of both the industries. Starting from a range of -1.0 and going to a range of +1.0, it is possible to understand
if a firm has a positive interrelated portfolio of businesses or a negative one. This kind of index avoids a lot of
problems encountered before, since there are more information available and the content validity of patterns of
technology is better too.
Talking about the main topic of our interest, the resource-based approach, we know that this method was useful in
order to analyze the impact of portfolio interconnections on firm performance in a sample of manufacturing
companies. There were developed two models to address basic issues of corporate strategy, using the least squares
regressions: the first is a cross-sectional analysis of current performance as a function of industry and firm
characteristics, while the second one analyzes the performance of firms over a 6-year period, as a function of initial
industry and firm characteristics. The industry characteristics used as independent variables were industry
concentration, asset intensity and industry profitability, that measure a firm's relative sales in the different industries
in which it is active. While the first two are measures of exit and entry barriers, the third one captures the lack that
was not gained by the previous two.
At the level of the firm, there were used as independent variables the firm market share, the firm size and the firm
relatedness, that were expected to be positively related to firm performance, which was measured with the ROA.
The use of ROA as a performance measure allowed the results of the analysis to be directly compared with a
substantial body of work on related topics in strategy and helped to make the research replicable and cumulative.
The analysis was conducted on a sample of 88 firms, with an average firm size of $1,550 million and six lines of
business. The initial sample was 120, but later it was reduced because TRINET data was not available for some of
them. They adopted the least square regression using a reduced data set without outliers.
Authors estimated four separate regression equations for each of the two dependent variables.
The first model studied the regression of current firm performance on the control variables (industry profitability,
industry concentration, industry asset intensity, firm market share and firm size) and it concluded the fact that firms
operating in highly concentrated industries in 1977 usually performed less well than others.
Subsequently, they analyzed control variables and related diversification studied by the entropy measure (Model 2)
or by the concentric index (Model 3). Once firm-level variables have been taken into account, firm performance is not
affected by a firm’s level of “relatedness” on the entropy index. On the other hand, firm performance appears to be
affected by related diversification (related by the concentric index), once effects of industry and firm-level controls
are considered. The last model, the number 4, considered the control variables and the resource-based relatedness
measure, showing that interrelationship among the businesses of a firm is associated with higher financial
performance. Model 4 showed how adding a resource-based measure implies a significant effect in the explained
variance in continuing firm performance.
The research results of Robins and Wiersema show a concrete application of the resource-based approach about
interconnections through businesses into multibusiness firms.
It’s quite evident that companies with a higher level of portfolio “relatedness” perform more efficiently than others.
A core concept of the article is the reconceptualization of “relatedness” in terms of shared strategic assets, rather
than relationships between businesses based on operations or facilities. The resource-based theory gives a lot of
importance to know-how and tacit knowledge, which at the same time could create an obstacle to the empirical
research, giving the fact that they cannot be measured directly.
The second article we analyzed, called “Overcoming Local Search Through Alliances and Mobility” and written in
2003 by Lori Rosenkopf and Paul Almeida, has the main purpose to analyze existing relationships between contextual
localization and knowledge flow. Thanks to several hypotheses demonstrated during the paper, the authors applied
concretely the obtained results through an analysis of patent citation patterns in the semiconductor industry, which
is characterized by continuous innovation (Jelinek and Schoonhoven, 1993).
The willingness was to demonstrate that alliances and mobility of inventors are two important techniques that
companies should apply in order to overcome geographical and technological constraints that usually limit them
contextually in their search for new knowledge.
The first and the second hypothesis focus on the general assumption that the starting point of the search for new
knowledge is linked to the yet existing external knowledge that is near to that specific firm (Cohen and Levinthal,
1990). Firstly, authors assume that technological similarity and the knowledge flow are linked, as demonstrated by
Stuart and Podolny (1996), through a research showing how all the major Japanese semiconductor firms maintained
a position in the technology field that was practically the same during a horizontal period of about 10 years.
Secondly, the hypothesis about the geographic proximity implies that the presence in a same area of different players
helps building contacts and social relationships between them, because as it’s possible to notice, the costs of
personal contact are extremely reduced, the frequency is increased and the knowledge flows better.
The literature behind this is given by Jaffe et al. (1993), who demonstrated that firms and universities acquire
knowledge from entities nearly located, and by Saxenian (1990), who showed again the interfirm linkages between
firms in the same region.
As regards semiconductor companies, Sorenson and Stuart (2000) highlighted this concept.
In this sense, it’s clear that both technological and geographical context are stable over time and difficult to change.
Although these two constraints could bring some benefits to the companies (as for example a more manageable and
recognizable knowledge), a firm will not compete in the long-term period with competitors using a local search.
The semiconductor industry started to be varied when companies noticed they had to beat competitors through
diversification across technological subfields in order to maintain their competitive position (Kim and Kogut, 1996).
The third and the fourth hypothesis rely on the core concepts of the article: alliances and mobility.
Alliances can be constructed as learning processes, which will grow across time.
Stuart and Podonly’s (1996) analyzed the major Japanese semiconductor players and showed how certain alliances
were followed by cross-citation and common-citation patterns between firms, suggesting a transfer of knowledge
between them. Regarding that specific field, the sources of the knowledge transfer are qualified engineers who move
from a firm to another one.
The tendency is to cite prior patents of the newly employed (Almeida and Kogut, 1999).
Starting from the general assumption that both alliances and mobility are related with contextual localization, it’s
also important to consider what both a convergent or a divergent context could imply.
Regarding the first one, it’s clear that common culture and similar practices may help the process of knowledge flow
and the absorption of information becomes easier. Porter (1990) showed the fact that a common context can
facilitate the creation of a more trustable and friendly environment in which firms will operate.
A divergent context allows a new way of transferring knowledge associated with the relative interpretation.
Furthermore, the mobility of individuals is more valuable because their knowledge is interpreted in a new context.
Mechanisms like alliances and mobility are minimally affected by the distance present in a divergent context and this
is possible also thanks to new kinds of media (Daft and Lengl, 1986).
The added value that divergent context has is linked to the factor of novelty present in this type of situations, that in
a convergent one does not exist: the value given by alliances and mobility is more important if coming by two firms
that are situated in two different frameworks (Granovetter,1973 ; Burt, 1992).
After all these theories and examinations, we get to the last four hypotheses, that are the more complete and
elaborated ones: they point out a positive correlation between the increase in geographical and technological
distance and the increase in effectiveness of alliances and mobility.
Regarding the methods, the study has been conducted based on patent data that show the ways through which
knowledge is acquired in semiconductor firms. Patent data were chosen because they are useful in understanding the
innovation process, since they contain detailed information and they are available across a longer period of time.
The sample of study in the semiconductor industry is a representative one of the general population: Rosenkopf and
Almeida took into considerations all the firms born in the decade 1980-1989, located in different regions of the U.S.
and abroad, which shared similar technological and industrial conditions (Stinchcombe, 1965).
Considering only firms that designed or fabricated semiconductor devices through databases from ICE and
Dataquest, the final sample consisted of 74 firms.
They discovered that the sources of external knowledge were not only the other focal firms, but also companies
founded before the range of time studied (the so-called “incumbents”, which were 116).
For this reason each variable was seen as a result between the receiver of knowledge (the focal firm) and the
possible sources of knowledge (companies including both focal firms and incumbents): this unit of analysis is called
“dyad”. The study was based on the prediction of knowledge flows from 1990-1995 as a function of mechanisms
measured from 1980-1989. Through a deepening study, researchers showed that the firms in the sample had a total
of 992 patents during the period 1990-1995.
Analyzing the citations made to other patents for each patent (and linking the patents to the firms that owned them),
independently of the fact that the firm was a focal or an incumbent one, the result was a total of 4,560 citations and
an average of 1,200 dyads were recognized.
Regarding the mobility, they examined the set of semiconductor patents for each firm in the sample for the range
1980-1995 and they tracked each inventor listed on the patents through that period.
Since the period of interest was the years between 1980 and 1989, the dates were interpolated to the nearest month
and, thanks to this, 121 instances of mobility were identified.
Due to the lack of comprehensive databases, it would have been easy to incur in some errors, underestimating or
overestimating the effect of mobility. To overcome this problem, it’s useful to simulate additional random instances
of mobility, in order to analyze whether the results would have remained the same or not, even if the method would
have identified only a small portion of the mobility phenomenon.
Regarding the alliances, authors analyzed every relationship between firms in the range 1980 and 1989, listed in the
weekly publication Electronic News: a total of 160 were identified.
Talking about the technological and the geographical similarity, Rosenkolf and Almeida developed two different
approaches. In order to capture technological overlap, they calculated the Euclidean distances between patent class
vectors for each pair of firms, creating a new measure of technological distance between firms of the sample, with a
range that goes from 0 to 1.4.
In order to capture geographic similarity, it was created a binary variable with the aim to determine if the inventors of
each patent belonged to the same geographic region or not at the time of patenting: if inventors were in the same
region, the geographic similarity was set to one, otherwise to zero.
It’s important to include other important factors that must be considered in the research (called “controls”), such as
the firm age and size, the number of semiconductor patents the focal firm has received during the 1990-1995 period,
the firm’s propensity to cite and absorptive capacity, or the likelihood of the firm to receive citations.
The analysis has been conducted through a negative binomial regression (Hausman et al. 1984) and several different
models. Model 1 suggests two different behaviors of technological distance and geographic proximity: the first
variable seems to have a strong negative effect on the dependent variable, whereas the second one has significant
positive effect. This supports Hypotheses 1 and 2.
The results of Model 2 support Hypotheses 4, since an improvement of the overall fit of the model, due to the
addition of the variables of mobility and alliance, is demonstrated.
Model 2 confirms the presence of a positive effect with mobility and a less relevant effect with alliances.
Model 3-6 do not demonstrate Hypotheses 5 and 6, since it’s quite difficult to demonstrate the higher effectiveness
of alliances and mobility over geographic context. Hypotheses 7 and 8 are supported by the fact that the effects of
alliances and mobility seems to be higher with technological distance.
Talking about the other control variables, some aspects (like the number of patents by the focal firm, the cited firm
and the citability of the cited firm) all have a positive effect with knowledge flows, as well as with size, but there is a
negative correlation with age.
The last step consists in controlling the robustness of the results through the simulated mobility data.
The researchers observed two different scenarios: with 80% simulated mobility, the previous effect of mobility is
confirmed to be positive and significant (with a decrease of the coefficient’s magnitude), and the same result is
obtained also for the interaction of simulated mobility with technological distance (demonstrated by Models 9 and
10); with 90% simulated mobility, even partial models begin to prove weak evidence of the effects listed before.
The overall results seem to demonstrate the expected statement that both technological and geographic contexts are
significant for interfirm knowledge flows. Talking about the effect of mobility and alliances, researchers discovered
that mobility facilitates knowledge flows regardless of context, whereas alliances do not have the same tendency. It’s
important to make a distinction between the two phenomena: mobility is an individual-level phenomenon, whereas
alliances are an organizational-level phenomenon that requires commitment and collaboration between firms over a
certain period.
There is also evidence that technological distance multiplies the effects of the two variables on knowledge flows.
Talking about the semiconductor industry, the second wave of entrants has been important for the development of
technology and innovation and has helped the study of mechanisms they use to acquire knowledge.
In practical terms, we are able to highlight the fact that managers may have a certain level of discretion in deciding
which mechanisms to implement in order to foster new knowledge and fill the gaps of their existing context.