# 18 Metrics

## 18.1 Finance

### 18.1.1 Return on Investment (ROI)

$ROI = \frac{\text{Net Profit}}{\text{Investment}}$

Similarly, return on marketing investment

$ROIM = \frac{IRAM - CM}{MS}$

where

• IRAM = Incremental Revenue Attributable to Marketing
• CM = Cost of the Marketing Investment
• MS = Marketing Spending

If ROMI is positive, then firm is on doing well.

• also known as economic profit.
• is a measure based on the residual wealth calculated by deducting its cost of capital from its operating profit, adjusted for taxes on a cash basis.
• measures the value a company generates from its invested funds.
• EVA relies heavily on invested capital. Hence, more suitable for asset-rich companies, whereas companies with intangible assets, such as technology businesses, may not be good candidates.

$EVA = NOPAT - (Invested Capital * WACC)$

where

• NOPAT = Net Operating profit after taxes = Operating Profit x (1-Tax Rate)

• Invested capital = Debt + capital leases + shareholders’ equity = Equity + long-term debt at the beginning of the period

• WACC = Weighted average cost of capital (average rate of return a company expects to pay its investors).

• $$WACC = \frac{Ke \times E}{E+D} + \frac{Kd \times (1-t) \times D}{E +D }$$

• Ke = required return on equity
• Kd(1-t) = after tax return on debt.
• (WACC* capital invested) is also known as a finance charge

Invested capital can also be calculated as (Total Assets - Current Liabilities). Hence, the modified version of EVA is:

$EVA = NOPAT - (\text{total assets} - \text{current liabilities}) * WACC$

• is the amount of wealth that a company is able to create for its stakeholders since its foundation.
• (the current market value of the company’s stock - the initial invested capital)

$MVA = \text{market value of shares (or enterprise value)} - \text{book value of shareholders' equity}$

### 18.1.5 Shareholder Complaints

used data from RiskMetrics

### 18.1.6 Profitability

$\text{profitability} = \frac{\text{operating income before depreciation}}{\text{total assets}}$

### 18.1.7 Firm Size

used log of total asset in million as firm size

$\text{firm size} = \log (\text{total assets})$

### 18.1.8 Sales Growth

Percentage change in gross sales = sales growth

### 18.1.10 Cash flows

Cash flows = log(operating cash flow in mil)

### 18.1.11 Financial Leverage

$\text{financial leverage} = \frac{\text{long-term debt}}{\text{book value of assets}}$

### 18.1.12 Stock return

used dummy if stock returns exceed industry averaged stock returns

### 18.1.14 Book Equity

Following Fama and French’s definition:

“BE is the book value of stockholders’ equity, plus balance sheet deferred taxes and investment tax credit (if available), minus the book value of preferred stock. Depending on availability, we use the redemption, liquidation, or par value (in that order) to estimate the book value of preferred stock. Stockholders’ equity is the value reported by Moody’s or Compustat, if it is available. If not, we measure stockholders’ equity as the book value of common equity plus the par value of preferred stock, or the book value of assets minus total liabilities (in that order)” (cite )

library(tidyverse)
seq - coalesce(pstkrv, pstkl, pstk, 0) + coalesce(txdtc, 0)

### 18.1.15 Net Contribution

Net contribution = gross margin x sales - cost of marketing

$NC = m \times S(a) -ka$

All data without CSRP come from Compustat/Fundamentals
Variable Data Item Source
Book Value on Equity PRCC_C x CSHO

PRCC_C: CRSP/Annual Update/CRSP/Compustat Merged/Fundamental Annual/Supplemental Data Items

CSHO: CRSP/Annual Update/CRSP/Compustat Merged/Fundamental Annual /Miscellaneous Items

CAPEX CAPX Cash Flow Items
Capital Intensity CAPX / AT

CAPX: Cash Flow Items

AT: Balance Sheet Items

Cash CH Balance Sheet Items
Cash and short-term Investments CHE Balance Sheet Items
Cash Flow $$\frac{IBC + DP}{AT}$$

IBC: Cash Flow Items

DP: Income Statement Items

AT: Balance Sheet Items

Cash Holdings $$\frac{CHE}{AT}$$

CHE: Balance Sheet Items

AT: Balance Sheet Items

Closing Price (annual, calendar) PRCC_C Supplement Data Items

Closing Price

(annual, fiscal)

PRCC_F Supplement Data Items
Cost of Capital $$\frac{XINT}{DLC}$$

XINT: Income Statement Items

DLC: Balance Sheet Items

Current Assets Total ACT Balance Sheet Items
Current Liabilities LCT Balance Sheet Items
Debt in Current Liabilities DLC Balance Sheet Items
Dividends (common) DVC Income Statement Items
Dividends (preferred) DVP Income Statement Items
Dividends (total) DVT Income Statement Items
Earning per Shares $$\frac{NI}{CSHO}$$

NI: Income Statement Items

CSHO: Miscellaneous Items

EBIT EBIT Income Statement Items
EBITDA EBITDA Income Statement Items
Equity Book Value BKVLPS Balance Sheet Items
Firm Size log(AT) AT: Balance Sheet Items
Income before Extraordinary Items IB Income Statement Items
Interest on long-term debt UXINTD Income Statement Items
Interest on short-term Debt UXINST Income Statement Items
Leverage $$\frac{DLTT + DLC}{SEQ}$$ Balance Sheet Items
Long-term Assets AT - ATC Balance Sheet Items
Long-term Debts DLTT Balance Sheet Items
Market to Book Ratio $$\frac{MKVALT}{BKVLPS}$$

MKVALT: Supplement Data Items

BKVLPS: Balance Sheet Items

Market Value MKVALT or CSHO x PRCC_F

MKVALT: Supplement Data Items

CSHO: Miscellaneous Items

PRCC_F: Supplement Data Items

Net income (Loss) NI Income Statement Items
Payout Ratio $$\frac{DVP + DVC + PRSTKC}{IB}$$

DVP: Income Statement Items

DVC: Income Statement Items

PRSTKC: Cash Flow Items

IB: Income Statement Items

Property, Plant, and Equipment PPENT Balance Sheet Items
Purchase of common and preferred Stocks PRSTKC Cash Flow Items
R&D Intensity $$\frac{XRD}{AT}$$

XRD: Income Statement Items

AT: Balance Sheet Items

ROA $$\frac{NI}{AT}$$

NI: Income Statement Items

AT: Balance Sheet Items

ROE NI/(CSHO x PRCC_F)

NI: Income Statement Items

CSHO: Miscellaneous Items

PRCC_F: Supplement Data Items

ROI $$\frac{NI}{ICAPT}$$

NI: Income Statement Items

ICAPT: Balance Sheet Items

Sale SALE Income Statement Items
Short-term Liabilities APC Balance Sheet Items
Stockholders Equity (total) SEQ Balance Sheet Items
Tangibility $$\frac{PPENT}{AT}$$

PPENT: Balance Sheet Items

AT: Balance Sheet Items

Tobin’s Q $AT + (CSHO x PRCC_F) - CEQ$/(AT)

AT: Balance Sheet Items

CSHO: Miscellaneous Items

PRCC_F Supplement Data Items

CEQ: Balance Sheet Items

Total Assets AT Balance Sheet Items
Total Equity PSTKC + CSHO

PSTKC: Balance Sheet Items

CSHO: Miscellaneous Items

Total Liabilities LT Balance Sheet Items

Information is taken from WRDS Data Items

Variable Data Item Data Source
Age CRSP
Valuation
Market Cap/ GDP series GDPA (U.S. Bureau of Economic Analysis)
Tobin’s q (AT (CSHO * PRCC_F) - CEQ)/(AT)
Market cap (000s) $$prc \times shrout$$ CRSP
Small firm Market cap < $100M Revenue Herfindahl (for each 3-digit NAICS in each year) $$\frac{revt^2_i}{\sum_1^n revt}$$ where $$i$$ = the firm, $$n$$ = firms in the same industry Investment Capital Expenditures / Assets $$\frac{capx}{lag(at)}$$ R&D/Assets $$\frac{xrd}{lag(at)}$$ If R&D is missing, set to 0 Fixed Assets/ Assets $$\frac{ppent}{at}$$ Inventory / Assets $$\frac{invt}{at}$$ Cash / Assets $$\frac{che}{at}$$ Profitability Operating cashflow / assets $$\frac{oidbp - xint - txt}{lag(at)}$$ Operating income before depreciation (oibdp) minus interest (xint) minus taxes (txt), divided by lagged assets Loss firms % of firm with net income (ni) < 0 R&D-adjusted operating cash flow/ assets $$CF/at + RD/ at$$ ROA $$\frac{ib}{at}$$ Financing Book Leverage $$\frac{dltt + dlc}{at}$$ Market leverage (dltt + dlc)/(at - ceq + (chso x prcc_f) Net Leverage $$\frac{dltt + dlc - che}{aat}$$ Negative net leverage firms % of firms with Net Leverage < 0 Interest/ Assets $$\frac{xint}{lag(at)}$$ No debt firms % with no dltt or dlc Net equity issuance $$\frac{sstk - prstkc}{lag(at)}$$ Ownership Institutional ownership % of shares outstanding held by institution Thomson Financial 13f data Blockholder % of firms with an institutional owner who holds 10 percent or more of outstanding shares Thomson Financial 13f data Payout Policy Dividend paying firms % of firms with dvc > 0 Dividends / Assets $$\frac{dvc}{lag(at)}$$ Repurchase / Assets $$\frac{prstkc - pstk}{lag(at)}$$ Total payout/ assets $$\frac{dvc + prstkc}{lag(at)}$$ Total payout/ Net income $$\frac{dvc+ prstkc}{ni}$$ Info from Appendix ### 18.1.16 Diversity Simpson Diversity index measures the market share of each sector • Equivalent to the Herfindahl index (in economics) ## 18.2 Marketing ### 18.2.1 Trust Measuring trust algorithmically using social media data ### 18.2.2 Sentiment Accuracy and Application of Sentiment Analysis • Sentiment is core to human communication. • Marketing uses sentiment analysis for: • Social media. • News articles. • Customer feedback. • Corporate communication. • Available sentiment analysis methods: • Lexicons: link words/expressions to sentiment scores. • Machine learning: complex but potentially more accurate. • Study introduces an empirical framework to: • Evaluate method suitability based on research questions, data, and resources. • Meta-analysis conducted on: • 272 datasets. • 12 million sentiment-labeled documents. • Findings: • Transfer learning models top performance. • These models may not always meet leaderboard benchmarks. • Transfer learning models are, on average, >20% more accurate than lexicons. • Performance influenced by: • Number of sentiment classes. • Text length. • Study offers: • SiEBERT - a pre-trained sentiment analysis model. • Open-source scripts for easy application. ### 18.2.3 Purchase Intention The Power of Brand Selfies • RoBERTa-based model: https://huggingface.co/j-hartmann/purchase-intention-english-roberta-large • Smartphones simplify sharing branded imagery. • Study categorizes social media brand imagery. • Identified image types: • Packshots (just the product). • Consumer selfies (consumer’s face with brand). • Brand selfies (product held, no visible consumer). • Convolutional neural networks used to recognize image types. • Language models analyze social media responses to 250,000+ brand-image posts from 185 brands on Twitter & Instagram. • Findings: • Consumer selfies lead to more likes and comments. • Brand selfies induce higher purchase intentions. • Traditional social media metrics may not fully capture brand engagement. • Display ad results: • Higher click-through rates for brand selfies than consumer selfies. • Lab experiment indicates self-reference affects image responses. • Machine learning can decipher marketing insights from multimedia content. • Image perspective impacts actual brand engagement. ### 18.2.4 Brand Reputation • Measuring brand reputation from Twitter data ### 18.2.5 Capabilities Marketing capability is critical for high-technology markets Insights • A company’s sales are increased by a solid foundation of innovative technologies that positively affect consumers’ perceptions of the benefits of its product’s externalities. • For businesses with a strong technological foundation, marketing capability has the biggest impact on the production of innovation that has been quality-adjusted. The companies that stand to benefit the most from great marketing capabilities are those with a solid R&D foundation. • because a company may have tremendous R&D capabilities but be unable to translate them into commercially viable items due to weak marketing skills • The interaction of marketing and R&D capabilities is the most significant factor in determining a firm’s performance. • High tech firm need to have both the ability to come up with innovation constantly and the ability to commercialize these innovations. • A firm’s capability is defined as “its ability to deploy the resources (inputs) available to it to achieve the desired objective(s) (output).” • Logically, the higher the functional capability a firm has, the more efficiently it is able to deploy its productive inputs to achieve its functional objectives. • Equivalently, the lower the functional inefficiency, the higher the functional capability of the firm Operationalization Marketing Capability Sales = f(technological base, advertising stock, stock of marketing expenditure, investment in customer relationships, installed base) Weight for marketing expenditure = 0.5 Advertising stock weight = 0.4 or 0.5 R&D Capability 2 dimensions of the quality of technological output • innovativeness • width of applicability Quality-adjusted technological output = f(technological base, cumulative R&D expenditure, marketing capability) R&D expenditure weight = 0.4 Operations Capability Cost of production = f(output, cost of capital, labor cost, technological base, marketing capability) Variables Label Variable Sales Did not use raw patent count because quality matters. Innovative-adjusted technological output The number of times the patents of a firm have been cited (citation-weighted patent count) Replication As the time of this writing, the US Patents by WRDS dataset only covers from 2011 to 2019. Hence, I can only replicate the results in this period but not the years in the study (1985 - 1994). Alternatively, you can visit USPTO to download the raw dataset and create your own matching algorithm based on company names. wrdsapps_patents_link is from US Patents by WRDS / Compustat Link wrdsapp_patents is from US Patents by WRDS / Patents • forward citations counts as of Dec 31st 2019 wrdsapp_patents_citations is from US Patents by WRDS / Citations library(tidyverse) library(lubridate) totalq <- read.csv(file.path("data/totalq.csv.gz")) capability <- read.csv(file.path("data/capability.csv.gz")) %>% # fix the length of numeric variables mutate(gvkey = str_pad(as.character(gvkey),width = 6, pad = "0", side = "left"), sic = str_pad(as.character(sic), width = 4, pad = "0", side = "left")) %>% # need to have data on advertising, r&d, sga, cogs filter(!is.na(xad) & !is.na(xrd) & !is.na(xsga) & !is.na(cogs)) wrdsapps_patents_link <- read.csv("data/patents/wrdsapps_patents_link.csv.gz") %>% mutate(gvkey = str_pad( as.character(gvkey), width = 6, pad = "0", side = "left" )) %>% # get industry data inner_join(capability %>% select(gvkey, sic) %>% distinct(), by = "gvkey") # view(head(wrdsapps_patents_link)) wrdsapps_patents <- read.csv("data/patents/wrdsapps_patents.gz") # view(head(wrdsapps_patents)) wrdsapps_patents_citations <- read.csv("data/patents/wrdsapps_patents_citations.csv.gz") %>% # correct dates mutate(grantdate = as.Date(as.character(grantdate), "%Y%m%d"), cited_pat_gdate = as.Date(as.character(cited_pat_gdate), "%Y%m%d")) %>% # get year # grant year here is the year that the new patent that cite the old one was granted. mutate(grantyear = year(grantdate), cited_pat_gyear = year(cited_pat_gdate)) %>% # get only those patents belong to firms in the Compustat database left_join( wrdsapps_patents_link %>% select(patnum, sic) %>% rename(cited_patnum_sic = sic), by = c("cited_patnum" = "patnum") ) %>% left_join( wrdsapps_patents_link %>% select(patnum, sic) %>% rename(patnum_sic = sic), by = c("patnum" = "patnum") ) # view(head(wrdsapps_patents_citations, 100)) wrdsapps_patents_citations_by_year <- wrdsapps_patents_citations %>% select(patnum, grantyear, cited_patnum) %>% count(cited_patnum, grantyear) %>% rename(citation_count = n) # check # wrdsapps_patents_citations_by_year %>% # filter(cited_patnum == "01044494") %>% # view() # wrdsapps_patents_citations %>% # filter(cited_patnum == "01044494") %>% # view() Innovativeness-adjusted technological output: 1. Calculate the average number of citations receive by all patents belonging the firms in the sample within one industry (defined by sic1, sic2, sic3, sic4) 2. The weight assigned to a firm’s patents = # of citations / industry sample average 3. Tech_innv = sum of citation-weighted patents within a firm in a year. # calculate industry average citation based on # sic1 tech_innv1 <- aggregate(x = tech_innv$citation_count,
by = list(tech_innv$sic1), FUN = mean) %>% rename(sic1 = 1, mean_cite_ind1 = 2) # sic2 tech_innv2 <- aggregate(x = tech_innv$citation_count,
by = list(tech_innv$sic2), FUN = mean) %>% rename(sic2 = 1, mean_cite_ind2 = 2) # sic3 tech_innv3 <- aggregate(x = tech_innv$citation_count,
by = list(tech_innv$sic3), FUN = mean) %>% rename(sic3 = 1, mean_cite_ind3 = 2) # sic4 tech_innv4 <- aggregate(x = tech_innv$citation_count,
by = list(tech_innv$sic), FUN = mean) %>% rename(sic = 1, mean_cite_ind4 = 2) # merge all four sic types together tech_innv_sic <- tech_innv4 %>% mutate( sic3 = substr(sic, 1, 3), sic2 = substr(sic, 1, 2), sic1 = substr(sic, 1, 1) ) %>% full_join(tech_innv3, by = "sic3") %>% full_join(tech_innv2, by = "sic2") %>% full_join(tech_innv1, by = "sic1") %>% select(-c(sic1, sic2, sic3)) # note that a modification from Dutta's paper is that I use the average number of citations received by all the patents belonging the firms in the sample within one industry (because industry average citations might differ) # Dutta's paper did not do this because they consider only one industry # calculate the Tech_innv tech_innv <- wrdsapps_patents_link %>% select(gvkey, patnum, sic) %>% inner_join(wrdsapps_patents_citations_by_year, by = c("patnum" = "cited_patnum")) %>% # match with the firm and year data filter(gvkey %in% capability$gvkey) %>%

inner_join(tech_innv_sic, by = "sic") %>%

# create the weight assigned to a firm's patent
mutate(weight1 = citation_count / mean_cite_ind1,
weight2 = citation_count / mean_cite_ind2,
weight3 = citation_count / mean_cite_ind3,
weight4 = citation_count / mean_cite_ind4) %>%

group_by(gvkey, grantyear) %>%
# create the citation-weighted patent count
summarize(tech_innv1 = sum(weight1),
tech_innv2 = sum(weight2),
tech_innv3 = sum(weight3),
tech_innv4 = sum(weight4)) %>%
ungroup()

rm(tech_innv_sic,
tech_innv1,
tech_innv2,
tech_innv3,
tech_innv4)

Width-of-applicability-technological output 1. Calculate the proportion of citations received by a patent from firms belonging outside of focal SIC code (sic1, sic2, sic3, sic4) = # of citations received by a patent from firms outside the focal SIC code / the total number of citation the patent received. 2. The weight assigned to a firm’s patent = the proportion of outside citations for the patent/ the industry sample average proportion 3. Tech_width = The sum of the “proportion-of-outside citation”-weighted patent in a year for a firm

wrdsapps_patents_citations_out_ind <- wrdsapps_patents_citations %>%
# select patent number with data on industry
filter(!is.na(cited_patnum_sic) & !is.na(patnum_sic)) %>%

# create variables indicating whether the new and old patents belong in the same industry
mutate(sim4 = if_else(patnum_sic == cited_patnum_sic, 1, 0),
sim3 = if_else(substr(patnum_sic,0,3) == substr(cited_patnum_sic,0,3), 1, 0),
sim2 = if_else(substr(patnum_sic,0,2) == substr(cited_patnum_sic,0,2), 1, 0),
sim1 = if_else(substr(patnum_sic,0,1) == substr(cited_patnum_sic,0,1), 1, 0)) %>%

select(
patnum,
cited_patnum,
grantyear,
cited_pat_gyear,
cited_patnum_sic,
patnum_sic,
contains("sim")
)

total_citation <- wrdsapps_patents_citations_out_ind %>%
group_by(cited_patnum, cited_pat_gyear) %>%
count()

# get per patent, per year, the number of outside-industry citations
# sic1
outside_citation_sic1 <- wrdsapps_patents_citations_out_ind %>%
group_by(cited_patnum, cited_pat_gyear, sim1) %>%
count() %>%
ungroup() %>%
# get only citations outside of the patent's industry
filter(sim1 == 0) %>%
select(-c(sim1)) %>%
rename(n_sim1 = n)

# sic2
outside_citation_sic2 <- wrdsapps_patents_citations_out_ind %>%
group_by(cited_patnum, cited_pat_gyear, sim2) %>%
count() %>%
ungroup() %>%
# get only citations outside of the patent's industry
filter(sim2 == 0) %>%
select(-c(sim2)) %>%
rename(n_sim2 = n)

# sic3
outside_citation_sic3 <- wrdsapps_patents_citations_out_ind %>%
group_by(cited_patnum, cited_pat_gyear, sim3) %>%
count() %>%
ungroup() %>%
# get only citations outside of the patent's industry
filter(sim3 == 0) %>%
select(-c(sim3)) %>%
rename(n_sim3 = n)

# sic4
outside_citation_sic4 <- wrdsapps_patents_citations_out_ind %>%
group_by(cited_patnum, cited_pat_gyear, sim4) %>%
count() %>%
ungroup() %>%
# get only citations outside of the patent's industry
filter(sim4 == 0) %>%
select(-c(sim4)) %>%
rename(n_sim4 = n)

tech_width <- total_citation %>%

full_join(outside_citation_sic1, by = c("cited_patnum", "cited_pat_gyear")) %>%
full_join(outside_citation_sic2, by = c("cited_patnum", "cited_pat_gyear")) %>%
full_join(outside_citation_sic3, by = c("cited_patnum", "cited_pat_gyear")) %>%
full_join(outside_citation_sic4, by = c("cited_patnum", "cited_pat_gyear")) %>%

# fill in 0 (those patents without any citations outside of their industry)
replace(is.na(.), 0) %>%

mutate(weight1 = n_sim1 / n,
weight2 = n_sim2 / n,
weight3 = n_sim3 / n,
weight4 = n_sim4 / n) %>%

# get company gvkey for each patent
select(gvkey, patnum, sic),
by = c("cited_patnum" = "patnum")) %>%

# step 3: get proportion-of-outside citation-weighted patent
group_by(gvkey, cited_pat_gyear) %>%
summarize(tech_width1 = sum(weight1),
tech_width2 = sum(weight2),
tech_width3 = sum(weight3),
tech_width4 = sum(weight4)) %>%
ungroup()

rm(outside_citation_sic1,
outside_citation_sic2,
outside_citation_sic3,
outside_citation_sic4,
total_citation)

rm(wrdsapps_patents,
wrdsapps_patents_citations,
wrdsapps_patents_citations_by_year,
wrdsapps_patents_citations_out_ind,
wrdsapps_patents_link)

#### 18.2.5.1 Marketing Capabilities

• Marketing Capability:

• Wiles, Morgan, and Rego (2012)

• Dinner, Kushwaha, and Steenkamp (2018)

• Marketing Alliance Capabilities: Swaminathan and Moorman (2009)

• Digitized Selling Capabilities:

• Big Data Capabilities:

• Social CRM Capabilities:

• Customer relationship capabilities:

library(frontier)

mar_cap <-
sfa(
log(sales) ~ sub_market,
log(adstock) + log(marketingstock) + log(techbase) + log(receivable) + log(installedbase),
data = capability,
ineffDecrease = T, # inefficiency decreases the endogenous variable for estimating a production function
timeEffect = T # Error Compoennts Frontier are time-variant
)

efficiencies(mar_cap)
# defunct
# install.packages("FEAR")

# install.packages("sfaR")
library(sfaR)
mar_cap <-
sfacross(
log(sales) ~ sub_market,
log(adstock) + log(marketingstock) + log(techbase) + log(receivable) + log(installedbase),
data = capability
# ineffDecrease = T, # inefficiency decreases the endogenous variable for estimating a production function
# timeEffect = T # Error Compoennts Frontier are time-variant
)

efficiencies(mar_cap)

Linking marketing capabilities with profit growth

• Objective: Examine the connection between a firm’s marketing capabilities and its profit growth, with a particular emphasis on how specific marketing functions impact the composite components of profit growth.

• Context: Although profit growth stands as a pivotal determinant of a firm’s stock price, there’s a limited understanding of how integral marketing capabilities interlink with this growth trajectory.

• Methodology:

• Engaged a cross-industry dataset from 114 firms.

• Delineated the exploration into three cornerstone marketing capabilities: market sensing, brand management, and customer relationship management (CRM).

• These capabilities were then juxtaposed with the dual facets of profit growth: revenue growth and margin growth.

• Key Findings:

• The scrutinized marketing capabilities exhibited both direct and synergistic influences on the growth rates of revenue and margin.

• A pivotal revelation was the counteractive effects of brand management and CRM capabilities on the growth rates of revenue and margin. Specifically, while one capability might promote revenue growth, it might simultaneously inhibit margin growth (and vice versa).

• Such intricate dynamics imply that a surface-level analysis, overlooking the nuanced contributions to revenue and margin growth, could obscure the genuine relationships tying marketing capabilities to the overarching profit growth trajectory.

Survey:

Review:

#### 18.2.5.4 Organization Capabilities

Embeddedness of Organizational Capabilities

• Core Issue:

• Managers need to efficiently use limited resources to build lasting organizational capabilities for a sustainable competitive edge.
• Challenge:

• Neglecting the intricate underlying processes of organizational capabilities can hinder understanding their impact on competitive advantage.
• Key Insight:

• Managerial decisions regarding resource usage directly impact the depth at which capabilities are ingrained in the organization, termed “capability embeddedness.”
• Research Method:

• Introduced a hierarchical composed error structure framework.

• Uses cross-sectional data and can be applied to panel data.

• Case Study: Retailing

• Organizational capability embeddedness directly influences retailer performance.

• This influence remains significant even when considering both tangible and intangible resources/capabilities.

• Takeaways:

1. Acknowledging how resources and capabilities affect performance at various organizational layers helps managers make informed decisions.

2. Essential to recognize whether the objectives of capabilities align (convergent) or differ (divergent).

3. The alignment or misalignment of these objectives can dictate how much embedded capabilities boost firm performance.