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.

18.1.2 Economic Value Added

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:

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

18.1.3 Market Value Added

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)

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

18.1.4 Unexpected size-adjusted advertising investments

(Chakravarty and Grewal 2016; MinChung Kim and McAlister 2011; Yan Liu, Shankar, and Yun 2017) used

18.1.5 Shareholder Complaints

(Wies et al. 2019) used data from RiskMetrics

18.1.6 Profitability

(Grewal et al. 2008; McAlister et al. 2016)

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

18.1.7 Firm Size

(Grewal, Chandrashekaran, and Citrin 2010; McAlister et al. 2016; Nezami, Worm, and Palmatier 2018) used log of total asset in million as firm size

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

18.1.8 Sales Growth

(Grewal, Chandrashekaran, and Citrin 2010; Nezami, Worm, and Palmatier 2018; V. R. Rao, Agarwal, and Dahlhoff 2004)

Percentage change in gross sales = sales growth

18.1.9 Financial Flexibility

18.1.10 Cash flows

(Chakravarty and Grewal 2011; Malshe and Agarwal 2015) Cash flows = log(operating cash flow in mil)

18.1.11 Financial Leverage

(Kashmiri and Mahajan 2017; Chakravarty and Grewal 2011)

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

18.1.12 Stock return

(Markovitch, Steckel, and Yeung 2005; Chakravarty and Grewal 2011, 2016) used dummy if stock returns exceed industry averaged stock returns

18.1.13 Financial Flexibility

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 (Davis, Fama, and French 2000))

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 (Kahle and Stulz 2017) 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 (Roy et al. 2017)

18.2.2 Sentiment

(J. Hartmann et al. 2023) 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

(J. Hartmann et al. 2021) 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 (Roland T. Rust et al. 2021)

18.2.5 Capabilities

(Shantanu Dutta, Narasimhan, and Rajiv 1999) 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 (Shantanu Dutta, Narasimhan, and Rajiv 2005, 281)

Advertising stock weight = 0.4 (Peles 1971) or 0.5 (Zhan Wang and Kim 2017)

R&D Capability

2 dimensions of the quality of technological output

innovativeness (Trajtenberg 1990b, 1990a)
width of applicability (Jaffe, Trajtenberg, and Henderson 1993)

Quality-adjusted technological output = f(technological base, cumulative R&D expenditure, marketing capability)

R&D expenditure weight = 0.4 (Shantanu Dutta, Narasimhan, and Rajiv 2005, 281)

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)

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:

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)
The weight assigned to a firm’s patents = # of citations / industry sample average
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()

# view(head(total_citation))

# 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
    inner_join(wrdsapps_patents_link %>%
                   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

Bahadir, Bharadwaj, and Srivastava (2008)
Marketing Capability: (Xiong and Bharadwaj 2013)
Wiles, Morgan, and Rego (2012)
(Mishra and Modi 2016)
Dinner, Kushwaha, and Steenkamp (2018)
Marketing Alliance Capabilities: Swaminathan and Moorman (2009)
Digitized Selling Capabilities: (D. S. Johnson 2005)
Big Data Capabilities: (J. S. Johnson, Friend, and Lee 2017)
Social CRM Capabilities: (Trainor et al. 2014)
Customer relationship capabilities: (Zhan Wang and Kim 2017)

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)

(Neil A. Morgan, Slotegraaf, and Vorhies 2009) 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.

18.2.5.2 Digital Marketing Capabilities

Survey:

Review:

(Herhausen et al. 2020)

18.2.5.4 Organization Capabilities

(Grewal and Slotegraaf 2007) 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.

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