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To complete this Assignment, respond to the following in a 3- to 4-page paper:Analyze HR executives’ outsourcing decisions. How would the HR skills identified in the Human Resource Competency Study aid an HR executive in assessing potential outsourcing partners? How would the skills identified in the Human Resource Competency Study enable an HR executive to effectively manage outsourcing agreements? How would such skills enable an HR executive to effectively advise the CEO of the organization regarding outsourcing decisions? What has been left unaddressed in the Human Resource Competency Study? Explain your concern or rationale in thinking something might be missing. The link to the Human Resource Competency Study http://hrcs.rbl.net/ All work must be original and in APA format. Please include an introduction and conclusion. I have also included some resources for your reference.
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The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0307-4358.htm
MF
36,6
Economic impact of marketing
alliances on shareholders’ wealth
Foo-Nin Ho and Allan D. Shocker
534
Department of Marketing, College of Business, San Francisco State University,
San Francisco, California, USA, and
Yewmun Yip
Department of Finance, Beacom School of Business,
University of South Dakota, Vermillion, South Dakota, USA
Abstract
Purpose – The purpose of this paper is to examine whether marketing alliances create value for
shareholders, and whether the results are robust across different business cycles.
Design/methodology/approach – Using standard event study methodology, abnormal returns
(AR) were computed for 402 firms which formed marketing alliances in a 12-month period covering
three business time periods, namely bull, bear and post 9/11 periods. ANOVA and regression
analyses were performed on cumulative abnormal returns (CAR).
Findings – Significant and positive AR were found on announcement day for firms forming
marketing alliances. When the sample is segmented by market capitalization, small cap firms were
found to stand to benefit the most, particularly when partnering with a large firm. During the bear
market period, marketing alliances tend to benefit small cap firms and firms with low profitability,
whereas during the bull market period, marketing alliances benefit firms with low asset utilization.
Research limitations/implications – Results are limited by the accuracy of the models used to
measure AR.
Practical implications – The results seem to suggest that smaller partners tend to benefit more
from marketing alliance, and the effect changes with business cycle.
Originality/value – The paper analyses how the benefits of forming a marketing alliance are shared
between partnering firms and how the different phases of business cycle influence the distribution of
benefits.
Keywords Marketing, Strategic alliances, Shareholder value analysis, Business cycles
Paper type Research paper
1. Introduction
By definition, a strategic alliance is a formal cooperative agreement between firms
designed to pursue a set of agreed upon goals so as to achieve competitive advantages
for both partners. Strategic alliances in general involve either collaborative effort (nonequity) or joint venture (equity). In a joint venture alliance, both partners share equity
control in a new organizational entity. In a collaborative alliance, neither partner has
any equity stake since no new entity is created, and the goal is to pool and leverage on
each other’s resources to achieve a common goal. Within non-equity alliances, they can
be further classified by two dimensions (Chan et al., 1997):
(1) horizontal vs non-horizontal; and
(2) technical vs non-technical.
Managerial Finance
Vol. 36 No. 6, 2010
pp. 534-546
# Emerald Group Publishing Limited
0307-4358
DOI 10.1108/03074351011043017
Horizontal alliances involve partners in the same three-digit SIC class while nonhorizontal alliances are between firms from unrelated industries. Technical alliances
involve the transfer or pooling of technological knowledge between the partners (e.g.
licensing agreements, and research and development agreements) while non-technical
alliances involve marketing and distribution agreements. An example of marketing
alliance is the common practice by airlines to engage in marketing alliances to promote
their frequent flyer programs such as the formation of star alliance. In times of tight
budgets, an underinvestment in marketing and brands may have a long-term adverse
impact on the firm. Swaminathan and Moorman (2009) find that by pooling resources
together in a marketing alliance with another firm can increase the value of the
partnering firms, particularly for marketing alliances in high-tech software industry.
Zagnoli (1987) find that non-equity alliances account for over 50 per cent of all
strategic alliances, and other researchers find that non-equity alliances offer more
advantages than equity joint ventures. For example, Jensen and Meckling (1991) argue
that non-equity alliances provide an organizational mechanism that aligns decision
authority with decision knowledge, and that benefits and costs resulting from the
decisions accrue fully to the decision maker, i.e. decisions are delegated to a level closer
to the requisite knowledge. Another advantage is the organizational flexibility of such
alliances where new links can be formed or current links disbanded in response to
market demands. On the other hand, there are costs associated with these ‘‘symbiotic’’
alliances – those that relate to searching out reliable partners, designing contracts and
other bonding mechanisms that discourage opportunism, and monitoring the
behaviour of alliance partners (Chan et al., 1997; Klein et al., 1978). In such situations,
companies have to balance between preserving key proprietary knowledge to maintain
their competitive advantage and insuring that partners will see a need to pool their
resources. This conjecture is supported by the findings of Luo et al. (2007) in that firms
must carefully balance between competition and cooperation when working with their
rivals in a cooperative alliance. Conversely, Harrigan (1984) notes that most strategic
alliances usually involve technology or knowledge that companies know they cannot
protect adequately or control.
Over the years, various scholars have studied strategic alliances. For example,
researchers have looked at the theoretical and conceptual foundations, motives for, and
framework of strategic alliances (Varadarajan and Cunningham, 1995); economic
outcome of strategic alliances (Chan et al., 1997); choice between equity and non-equity
modes of alliance (Pisano, 1989); the management and structuring of alliances (Parkhe,
1993). Marketing scholars have also looked at strategic alliances such as intraorganizational cooperation between marketing and other functional areas or other
business units (Ruekert and Walker, 1987) and inter-organizational relationships
between firms (Adler, 1966; Swaminathan and Moorman, 2009; Luo et al., 2007). Some
researchers have also identified the importance of marketing alliances in the overall
realm of strategic alliances. Varadarajan and Cunningham (1995) view marketing
activities as critical factors in the success of strategic alliances especially in a rapidly
changing business and market environments. Other researchers have also echoed this
sentiment in recognizing the importance of integrating marketing in strategic alliances
(Webster, 1992; Day, 1992).
2. Economic value of strategic marketing alliances
Das et al. (1998) and Chan et al. (1997) have found that while strategic alliances create
value for their shareholders especially when there is sharing of technological knowhow, but that is not necessary true for marketing alliances. For technological alliances
involving firms in the same industry, Chan et al. (1997) report a significant positive
returns, and whereas for marketing alliances, a significant positive return is observed
only when the partners are from unrelated industries. In their study on marketing
alliances by high-tech software firms, Swaminathan and Moorman (2009) also find a
Impact of
marketing
alliances
535
MF
36,6
536
significant positive return for both partnering firms. On the other hand, Das et al. (1998)
do not find a significant return to shareholders for the formation of marketing alliances.
Das et al. (1998) find that although investors view alliances formed by more profitable
firms as detrimental to their value, marketing alliances are viewed as more detrimental
than technological alliances. On the hand, Chan et al. (1997) report that firms entering
into strategic alliances tend to outperform their industry counterparts in the period prior
to the formation of the alliances, and therefore, they argue that the formation of an
alliance is not in response to poor performance. Larger firms, especially in technological
alliances, depend critically on their smaller partners for resources (e.g. technological
know-how). This asymmetric dependence enhances the bargaining power of the smaller
partners. This conjecture is supported by the empirical evidence provided by Das et al.
(1998) in that the market reaction to smaller firms’ alliances is greater than the reaction to
larger firms’ alliances. This effect is more prominent for technological alliances, where
smaller partners earn significantly higher returns than their larger partners. However,
there are no discernible differences in the returns earned by the small and large partners
in marketing alliances. Chan et al. (1997) also report similar findings in that smaller
partners tend to benefit the most from forming a strategic alliance, but their larger
counterparts do not suffer a decline in value.
The existing literature seems to suggest that marketing alliances do not create value
unless it is forged between firms in unrelated industries. Why do we continue to observe
the formation of so many marketing alliances? The apparent incongruity between
marketing practices and existing empirical results lead us to investigate further the issue
of whether marketing alliances create value for shareholders. The seemingly conflicting
results on which types of firms benefited the most from forming a strategic alliance as
reported by Chan et al. (1997) and Das et al. (1998) are perhaps due to using different
sampling periods. Essentially, Das’s 1998 study covers the bear market period (i.e. from
1987 to 1991) while Chan’s 1998 study covers a longer period (i.e. from 1983 to 1992)
which includes both the bear and the bull market. In this study, we hypothesize that the
market reacts differently to the different characteristics of firms forming marketing
alliances during different phases of the business cycle. During a bear market, the market
may view the formation of an alliance by a poor performing firm as a positive move
towards profitability and reward the firm for doing that. On the other hand, during a bull
market, since most firms are profitable, profitability may not play as critical a role in
deciding on the formation of a marketing alliance.
Given the limited empirical studies on marketing alliances and their economic
impact, our research objective is, therefore, to re-examine whether non-equity
marketing alliances create economic value for the shareholders. In this study, we look
at non-equity, non-technical marketing alliances (henceforth referred simply as
marketing alliances) and their economic impact on shareholders’ value. Specifically, the
marketing alliances we examined include cross-licensing, co-branding, co-marketing,
and joint marketing. In addition to investigating the economic value of marketing
alliances, we also examine whether there is a redistribution of wealth between
partners. Third, we also examine the influence of business cycle on the economic value
of marketing alliances. Therefore, our research questions about marketing alliances
and their economic impact are stated as follows:
RQ1.
What is the economic return to shareholders from forming a marketing
alliance?
RQ2.
Who benefits the most from such alliances?
RQ3.
Whether business cycle plays a role in influencing the type of firms entering
into a marketing alliance?
The rest of the paper is organized as follows. Section 3 presents data description and
methodology. Section 4 discusses the empirical results. Finally, section 5 offers our
concluding comments.
3. Research design
3.1 Data collection
An event such as marketing alliance is usually well publicized in the media in the form
of business wires. To obtain a sample of firms announcing strategic marketing
alliances, we conduct a search of the Lexis/Nexis database including all business wires
covering the following time periods:
.
bull market period from 1 November 1999 to 28 February 2000 (also known as
the internet bubble period);
.
bear market period from 1 March 2001 to 10 September 10 2001; and
.
post 11 September from 11 September 2001 to 31 October 2001.
Since marketing alliances are commonly announced in the media, within this 12-month
period, our search resulted in close to 10,000 initial hits, and therefore, provided us with
a large enough sample size for the study.
Data collection for the study is a multi-stage process. In stage one, we search the
Lexis/Nexis database using the keywords ‘‘strategic alliance’’. In stage two, we narrow
the search by using a combination of four keywords; specifically, co-marketing, cobranding, joint marketing, and marketing alliance as these represent non-equity
marketing alliances. From this search, there are 9,847 hits. Every announcement is then
reviewed to see if it meets the criteria of a marketing alliance, i.e. a non-equity
agreement that is non-technical. We exclude all alliances that involve the transfer or
pooling of technological knowledge. Cases, whereby the announcement has both
technical and non-technical components, are also excluded from the final sample.
The filtering process identifies 311 qualified announcements of marketing alliances
involving 402 firms in which at least one partner’s common stock is publicly traded,
and data on daily stock returns and market capitalization are available. Stock price
data are obtained from Commodity Systems Inc and Exchanges hosted on the Yahoo!
Finance web site and the previous year annual financial data for each firm are obtained
from the COMPUSTAT database. The Security Exchange Commission requires
companies to report their financial statements no later than three months after the
fiscal year ending date. To ensure that the firm’s financial data are available on the
event day, we restrict the fiscal year ending date of the financial statements to be at
least three months before the event date.
For comparisons, firms in the final sample (Table I) are further classified by their
market capitalization, which is defined as the price of a share of common stock
multiplied by the number of shares outstanding. Small cap firms are defined as having
a market value of less than $1 billion; mid cap firms have value of between $1 and $5
billion; and, large cap firms with market value greater than $5 billion.
3.2 Measuring abnormal returns
We use an event-study methodology similar to that described in Brown and Warner
(1985) to measure the stock market’s reaction to the announcements of marketing
Impact of
marketing
alliances
537
Table I.
Descriptive statistics of
firm characteristics
274.26
0.80
0.05
0.76
3.99
0.24
3.50
0.25
2.73
27.18
2.45
144.38
0.89
0.05
0.00
0.00
0.02
0.03
1.03
0.89
5.21
307.54
0.00
1.12
Small cap firms
SD
Min
Mean
1,078.65
0.19
0.00
0.03
0.25
0.02
1.02
0.29
5.77
6.04
0.04
0.67
Mid cap firms
SD
Min
57
934.49 2,789.69 1,104.38
3.48
1.23
0.95
0.21
0.07
0.07
6.12
0.48
0.45
37.33
1.81
1.51
0.96
0.58
0.24
25.99
4.15
6.55
0.33
0.02
0.10
26.37
0.04
0.85
5.63
0.15
0.89
15.27
1.23
1.03
778.58
298.46 1325.08
Max
4,851.00
3.40
0.31
2.54
7.64
0.98
47.09
0.21
1.11
0.25
4.65
6373.00
Max
207
85,672.57
1.57
0.03
0.20
1.94
0.53
3.18
0.08
0.16
0.11
0.89
58.04
Mean
101,367.01
0.86
0.03
0.21
1.44
0.22
2.90
0.10
0.32
0.40
0.63
271.44
5,049.28
0.01
0.00
0.01
0.26
0.08
1.09
0.66
2.70
1.81
0.05
0.95
Large cap firms
SD
Min
Notes: The reported F-statistic is for testing if the means of three independent samples are equal; *p-value < 0.10; **p-value < 0.05 n 138 Market cap 278.66 Beta 1.21 Earnings-price ratio 0.06 Book-to-market 0.59 Current ratio 3.16 Debt ratio 0.39 Equity multiplier 2.69 Return on assets 0.11 Return on equity 0.01 Profit margin 3.73 Asset turnover 1.51 Time interest earned 64.54 Mean 538 Firm characteristics 460,770.51 4.39 0.19 1.59 10.48 0.93 15.12 0.28 1.46 2.71 3.98 3362.09 Max 67.85** 8.35** 24.76** 26.41** 9.56** 18.98** 2.78* 51.11** 0.59 2.55* 6.63** 2.95* F-stat MF 36,6 alliances. The day when a press release is issued on the formation of a marketing alliance is defined as the event day (i.e. t ¼ 0). We then examine the behaviour of stock returns 60 days before the event day, and 60 days after the event day (i.e. t ¼ 60, þ60). For each of the 402 previously identified stocks, the daily risk-adjusted returns are estimated using both the market model and the market-adjusted returns (see, Brown and Warner, 1985). For market-model risk-adjusted return, the parameters of the single index market model are estimated over a 200-day period (t ¼ 260, . . ., 61) using the S&P 500 index as the market index. The parameter estimates are then used to calculate the abnormal returns (ARt) for the period from 60 days before to 60 days after the announcement day. However, the results based on a single-index model can be biased due to model misspecification, as pointed out by Roll (1977). To avoid specification bias, we also compute the market-adjusted returns for each of the 402 stocks, and again the S&P 500 index is used as the market index. The cumulative abnormal return CARj (–m, þn) for event window from Day m to Day þn is then computed for each stock using the following equation: CARj ð m; þnÞ ¼ tþn X ARj;k k ¼ t m 4. Empirical results 4.1 Sample characteristics Table I presents the characteristics of firms in the sample. As shown by their profitability measures, such as returns on assets and profit margin, small cap firms have lower profit margin as compared to their larger counterparts. In fact, more than half of the small cap firms are unprofitable prior to their entering into a marketing alliance, compared to only 5 per cent for the large cap firms. In addition, the common stocks of small cap firms are not as highly priced by investors as indicated by their high earnings-price and book-to-market ratios. Perhaps, because of their low profitability, these small cap firms are not as highly leveraged as indicated by their average debt ratio of 39 per cent as compared to 53 per cent for large cap firms. They also have higher liquidity as measured by a high average current ratio of 3.16. Surprisingly, the small cap firms are also more efficient in the use of asset as shown by their higher asset turnover ratio. On the other hand, the lower earnings-price ratios for the large cap firms may indicate that investors expect these firms to have a higher potential growth rate. The stock returns of these large companies are also more risky as shown by their higher betas. 4.2 Stock price response to announcement of marketing alliances For compatibility reasons, we adopt the same event windows as those used by Das et al. (1998). Since the results using market-adjusted AR are very similar to those using market-model-adjusted AR, we report only the market-model-adjusted CAR. Table II presents the average CAR calculated for various event windows. For all firms in the entire sample period, our results show that, on average, the market reacts positively to the announcements of marketing alliances for all event windows. On the event day (Day 0), firms entering into marketing alliances earn, on average, a significant abnormal return of 0.66 per cent. When we partition the firms in the sample into three Impact of marketing alliances 539 MF 36,6 540 Table II. Average cumulative AR over different event windows All firms Small cap Mid cap Large cap Event window Mean t-statistic Mean t-statistic Mean t-statistic Mean t-statistic F-statistic Full sample n Days 3 to 3 Days 2 to 2 Days 1 to 1 Days 1 to 0 Days 0 Days 0 to 1 Days 1 to 2 Days 1 to 3 Days 2 to 1 Days 3 to 1 Bull market n Days 3 to 3 Days 2 to 2 Days 1 to 1 Days 1 to 0 Days 0 Days 0 to 1 Days 1 to 2 Days 1 to 3 Days 2 to 1 Days 3 to 1 Bear market n Days 3 to 3 Days 2 to 2 Days 1 to 1 Days 1 to 0 Days 0 Days 0 to 1 Days 1 to 2 Days 1 to 3 Days 2 to 1 Days 3 to 1 Post 9/11 n ... Purchase answer to see full attachment

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