ABSTRACT:
This study examined the determinant of Banking Sector Intangible Asset Recognition in
Gambia. For the period 2012 - 2021 a sample of (10) selected banks were used. The thesis
was based on the concept of both Ex-post facto and quantitative studies and used secondary
data for interpretation. The data gathered were analyzed using descriptive statistics, matrix of
correlation and regression of ordinary least square. The result indicated that there is a
substantial and negative interaction between bank size and Intangible Asset Recognition
which was statistically significant at 5 per cent significant level while a favorable and
significant relationship was reported against Bank Age and Intangible Asset Recognition
which was statistically significant at 1 per cent significance level. The finding shows that all
the independent variables of our sampled banks over the 10-year period accounted for 71
percent of the system variation in Intangible Asset Recognition, while about 29 percent of
the total variations were not accounted for, thus captured by the stochastic error term. The
study therefore recommends, among others, that a reduction in the size of banks should be
encouraged as it enhances the recognition of intangible assets among banks in Gambia while
encouraging the existence of old generation banks as it helps to comply with recognition
policy.
CHAPTER ONE
1.1 Background to the Study
The purpose of writing an annual report for a bank is to divulge the results of the bank. In
fact, the annual report provides details on the operating results, financial situation and cash
flows of a company (Krstic & Dorðevic; 2010). However, in order to achieve this purpose
adequately, the financial report must
provide sufficient detail relating to the various
elements or components (capital and recurrent) of the final accounts. To determine the
financial performance and the firm's position, assets and liabilities are reported at their net
book values when preparing these annual reports. Nevertheless, the monitoring and
recognition of intangible assets is a vital aspect of this financial reporting which is unduly
ignored in the financial position statement. In fact, it has been found that the market value of
a company is usually higher than its book value, and the difference can be due to the nonrecognition of intangible assets in the financial position of the company (Bukhet, Gormsene
& Mouritsen; 2005). In other words, the magnitude of the difference in banks' market
values and book values is an indication of the impact of intangibles on those banks.
Intangible asset recognition has been a problem in Gambia's banking sector and this has
resulted in inconsistency in measurement, valuation and financial reporting on a wide range
of intangible assets. Furthermore, the prevailing traditional accounting model does not
guarantee a thorough understanding of accounting reporting for 21st-century accounting
research and does not provide empirical insights into the voluntary recognition of intangible
assets. The challenge of inconsistency also arises in the common framework of measurement,
valuation and financial reporting on a wide range of intangible assets, and the inability of
the traditional accounting standard or even the late Gambiaian accounting standard to
address reliability, separability, Measurement, valuation and specific financial reporting
concerns relating to the declaration of intangible assets have not made itself transparent to
clients and financial reporting consumers and, as a result, have contributed to the
introduction of formal accounting principles. As such, this weakness has led to the
development of standards (IAS 38) capturing how intangible assets can be measured and
reported in the financial position statement. Moreover, the International Accounting
Standard No. 38 (IAS 38) and even the late Gambiaian Accounting Standard Statement
provide little or no guidance on the financial reporting of intangible assets, posing a serious
problem for decisionmakers. Nevertheless, the International Accounting Standards Board
(IASB) (2000) has instructed companies to report, at least on a voluntary basis, on their
stock of intangible assets in order to supplement the financial reports and also provide
explanations on unrecognized assets. It indicates that these details would be conveyed above
mandatory criteria (Abdul Halim & Baxter; 2010), which should only be freely shared.
While studies on similar topics have been carried out by some academics, the researcher still
noticed some discrepancy in literature. These studies include: Ribeiro, Gomes and Duenas,
(2015), Alexander, Philip, Belgium and Mai Dao, (2009), Ancuta, Moisescu and Varlanuta,
(2017 ) analyzed the degree to which accounting treatment of intangible assets affects the
importance of financial details for Romanian pharmaceutical companies listed on the
Bucharest Stock Exchange, and found that accounting treatment of intangible assets. The
few studies that have been conducted in Gambia have been performed on sectors other than
bank. It is on this that we agreed to review banking sector to close the gap in literature by
looking at the determinant of recognition of intangible assets in banking sector in Gambia.
Against this background , this study examined the determinants of banks' voluntary
recognition of intangible assets in Gambia and the extent to which various factors affect such
voluntary recognition in Gambian banks, as well as the critical factors for the provision of
useful information that allow users of accounting information to evaluate the available
options. Hence, understanding the accounting reporting practices of developing economies
such as Gambia as they relate to the recognition of intangible assets is essential.
1.2 Statement of Research Problem
This study delves into the core research problem of understanding the determinants that
shape the recognition of intangible assets within the banking sector of The Gambia.
Several studies have examined whether or not the implementation of IFRS has resulted in
actual convergence of reporting practices and in the expected outcomes. Some studies find
that the harmonization of accounting standards do not lead to convergence of the financial
reporting and expected outcomes due to differences in countries enforcement practices
(Leuz, 2010; Christensen, Hail & Leuz, 2013). Another study claims that variations in
accounting practices are unavoidable due
to the principle-based nature of IFRS (Kvaal & Nobes, 2010). There is also a risk that the
motives of the management may influence the financial reporting (Ford, 2008). Pope &
McLeay (2011) concludes that the degree of compliance with the IFRS standards relies on
the incentives of the preparers, which in turn partly relies on the quality of enforcement.
They base this statement on findings in previous research such as Garcia-Osma and Pope
(2010) result that earnings management
depends on the strength of countries enforcement and legal institutions. They further mention
that,“there are good reasons to predict that benefits will only follow if implementation and
enforcement are high quality” (Pope & McLeay, 2011, p. 246).
Various studies have been conducted on asset recognition. Some studies focus on recognition
of tangible asset (La Porta, Lopez-de-Silanes, Shleifer & Vishny, 1998). In general, these
studies find that there are differences in financial reporting across countries, for example,
due to differences in legal traditions (La porta et al. 1998). Other studies have analyzed the
determinant of intangible asset in manufacturing industries.
Since intangible assets have become drivers of firms’ business performance, it would be
interesting to examine the determinant of intangible assets recognition in Gambian Banks.
1.3 Research Questions
The study was guided by the following research questions:
1.What is the relationship between bank size and the recognition of intangible assets in the
commercial banking sector in Gambia?
2.What is the impact of bank age on the recognition of intangible assets in the commercial
banking sector in Gambia?
3.To what extent do the independent variables (bank size, bank age, etc.) account for the
variation in intangible asset recognition among sampled banks in Gambia?
4. Does leverage have a significant effect on the recognition of intangible assets in the
commercial banking sector in Gambia?
1.4
Objectives of the Study
The main objective of the study is to evaluate the Determinant of intangible assets
recognition in Gambian Banks. The specific objectives are:
1. To assess the significance of leverage on the recognition of intangible assets in the
commercial banking sector in Gambia.
2. To examine the relationship between bank size and the recognition of intangible
assets in the commercial banking sector in Gambia .
3. To investigate the impact of Profitability on the recognition of intangible assets in the
commercial banking sector in Gambia .
4. To investigate the impact of bank age on the recognition of intangible assets in the
commercial banking sector in Gambia .
1.5
Research Hypothesis
The following research hypothesis are formulated in null form.
H1: Leverage has no significant effect on recognition of intangible asset.
H2: Bank size has no significant effect on intangible asset recognition.
H3: There is no significant effect between profitability and recognition of intangible asset.
H4: Bank age has no significant effect on intangible asset recognition.
1.6
Scope of the Study
The study focuses on the commercial banking sector in Gambia. The study covers the period
from 2012 to 2021. The study will use a sample of 10 selected banks in Gambia.
The study will utilize secondary data for interpretation and employs both ex-post facto and
quantitative research methods, utilizing descriptive statistics, correlation matrix, and
regression analysis.
1.7
Significance of the Study/Justification for the Study
The intention of this research is to evaluate the determinant of Intangible assets recognition
in Gambian Banks and create awareness on intangible asset recognition in the banking sector
in Gambia and to emphasize on the need for Banks to implement effective strategies on
recognition of Intangible asset.
The findings of this research will be of importance to the following stakeholders:
1. Regulatory Bodies
Regulatory bodies in the banking sector such as Central Bank of Gambia will benefit from
the findings of this research as it will provide information on determinant of intangible assets
recognition in Gambian Banks.
2. Correspondent Banks
The findings of this research will benefit Correspondent Banks that provide
services to the banking industry in Gambia on gathering information of the degree to which
non-recognition of intangible assets has affected the banking industry in Gambia and
consequently come up with initiatives to
assist banks recoginse intangible assets.
3. Management of banks
The top level management teams within banks in Gambia will benefit from this research by
providing a basis on which they can formulate effective strategies to recognise intangible
asset based on the accounting standard.
4. Researchers
Future researchers will benefit from this research by providing additional information on the
determinant of intangible assets recognition in Gambian Banks.
1.8 Definition of Key terms
Intangible Assets: Intangible assets are non-physical assets that lack a physical substance
but have value to a company. Examples of intangible assets include patents, trademarks,
copyrights, brand value, customer relationships, and intellectual property.
Recognition: Recognition refers to the act of providing information or making information
available to stakeholders, such as investors, regulators, and the public. In the context of the
study, it specifically refers to the recognition of intangible assets by commercial banks in
Gambia. Recognition of an intangible asset is governed by IAS 38.18 and IAS 38.21-23.
Bank Size: Bank size refers to the measure of a bank's total assets, which indicates the scale
or magnitude of the bank's operations and financial resources.
Bank Age: Bank age refers to the number of years since a bank's establishment or
incorporation. It represents the longevity or experience of a bank in the industry.
CHAPTER TWO
LITERATURE REVIEW
2.1
Conceptual Review
The Gambian economy is fast growing with firms and organizations struggling to survive
and keep up with the high competition in their industries. Economists consider that the main
feature of this new economic environment is the essential role played by intangibles as a
fundamental determinant of value creation of business companies (Meritum, 2001).
Intangible assets have become the focus of companies, financial analysts, investors,
accountants, and regulations alike in recent time and this has initiated attempts to understand
and narrow the gap between a company’s book and market value (Barton, 2005). This
chapter provides some empirical insights to the factors that might explain the level of
recognition of intangible assets by firms. It examines some studies by authors and
researchers both direct and indirect that help to throw more light on this issue.
Concept of Intangible Assets
The debate on the recognition of intangibles is upcoming and heated. According to Zeghal
and Maaloul (2011), the lack of recognition of intangibles has affected the value-relevance
of financial information. As such, if financial statements must become value relevant in this
modern time, recognition of intangibles in the statements must be of essence. Similarly,
(Kampanje, 2012) asserts that the increasing importance of intangibles can be attributed to
information age, an age
where information is what drives performance and not just the possession of physical assets.
He further noted that businesses are being challenged by the rapid industrialization and
globalization to develop and acquire intangible assets as a survival strategy and means of
gaining competitive advantage amidst the dynamic business environment. Thus, the
significance of intangible assets as well as its appropriate recognition and measurement for
the purpose of adequate financial reporting is of paramount necessity. Furthermore, (Lee,
2010) asserts that a measure aimed at improving financial reporting is the adoption of fair
value estimates in the measurement of intangibles. Thus, the understanding of the concept of
intangibles is of immense importance.
Definitions of intangible assets seem to draw from the definition as given by the
International Accounting Standard Board. Ghamari et al. (2012) in their study, described
intangible assets as assets that are latent, non-monetary and do not have a physical nature
while IAS 38 defines intangible assets as assets that are identifiable, non-monetary assets,
and without physical substance. The standard asserts that intangibles should be recorded and
recognized if they fall within the bounds of the definition given and if the assets meet the
recognition criteria. The recognition criteria is twofold viz the probabilities that the expected
future economic benefits attributable to the asset will flow to the entity; and that the cost of
the asset can be measured reliably. Furthermore, the definition of asset by the standard is
given as anything that is capable of being separated or divided from the entity and sold,
transferred, licensed, rented or exchanged, either individually or together with a related
contract or arises from contractual or other legal rights, regardless of whether those rights are
transferable or separable from the entity or from other rights and obligations.
The classes of intangible asset include; brand names, masthead and publishing, computer
software, license and franchises, copyrights, patents and industrial property rights, services
and operating rights, recipes, formulae, models, designs and prototypes, intangible assets
under development. (Collings, 2011) in his review opine intangible assets to comprise assets
such as licenses and quotas, patents and copyrights, computer software, trademarks,
franchises, and marketing rights. Still on the types of intangibles, Wyatt and Abenethy (2003)
tend to focus on four broad classifications of intangibles: acquired intangible assets- this
includes acquired identifiable intangible assets (IIA) such as acquired patents and trademarks,
brands, and purchased goodwill that is acquired in business combinations; research and
development (R&D)- this includes expenditures associated with R&D activities performed
within the firm. Expenditures for exploration, evaluation and development
costs in mining and other resource-based firms are usually accounted for separately to R&D
because of the specific risk profile of these expenditures; internally generated intangible
assets (IGI)- this includes identifiable intangible assets produced by the firm, and internal
goodwill that is not easily attributable as to its source of value. Identifiable intangible assets
and internal goodwill relate to such things as the firm’s information systems, its
administrative structures and processes, market and technology knowledge, trade secrets,
customer and supplier networks; intellectual property- these are a sub-set of acquired and
internally generated intangible asset classifications that have legal or contractual rights (i.e.
patents, trademarks, designs, licenses, copyrights, firm rights, mastheads).
Despite the long list of assets that might be categorized as intangibles, it is argued that not all
these have allowable recognition on the financial statement. According to Collings (2011),
IAS 38 prohibits the group of intangible deemed as internally generated from being
recognized on the balance sheet. He further opines that customer lists, brands, mastheads,
and publishing titles are examples of intangibles that should not be recognized on the
statement. Also, IAS 38 specifically mentions the use of either the cost model or revaluation
model in the measurement of intangibles. It
however noted that for the revaluation model to be used there must be an active market and
the revaluation must be at fair value. For a market to qualify as active, the items traded in it
must not be heterogeneous, effective buyers and sellers are ever present in the market and
information about the prices are available to the public.
Determinants of Intangible Asset recognition
There are numerous determinants or motives for firms’ recognition of intangible assets in
their annual financial reports. In this sub-heading, a review of literature on some of the
popular determinants which includes auditor type, industry type, profitability, leverage,
company with foreign activities and age of the firm is given. These variables and their
relationship with intangible asset recognition as discussed below.
Auditor Type
An auditor is an independent person appointed to examine the organization records and
financial statement to form an opinion on the accuracy and correctness of the financial
statement of the firms (Oladipupo, 2005). Firms audited by big four (4) auditing firms (i.e.
high standard and well known auditing firms in the country) are likely to voluntarily disclose
more information about intangible than those that are audited by non-big four (4) auditors
(Oliveira et al., 2006). Therefore, bigger audit firms encourage their clients to disclose more
information in annual reports (Hossain et al.,1995). Oliveira et al. (2006) argued that large
auditing firms might encourage their clients to disclose more information, as they want to
preserve their reputation, develop their expertise, and ensure that they retain their clients.
Industry Type
Membership of a given industry is argued to affect levels of voluntary recognition through
political cost, signaling, proprietary cost, and legitimacy theory. Firms belonging to
industries with high levels of intangibles are likely to voluntarily disclose information about
intangible (Oliveira et al., 2006) . The level of intangible assets voluntary recognition is
higher for emerging market companies in either IT-related or consumer services and product
industries (Kang and Gray, 2006). Also in
their work, industry type is considered from the IA perspective. IA relating to technology
and brand names are arguably the most important, or at least the best known, specific assets
which are intangible (Barth et al., 2001). For example, previous literature has found that
companies
operating
in
high-tech
industry
sector
(information
technology/telecommunications services) recognize more technology-related expenses and
R&D. On the other hand, in the consumer product industry sector, brands are regarded as a
key competitive factor, which influences consumer preferences for a product and therefore
the sales of the company (Stolowy et al., 1999), and subsequently are disclosed more often
by companies in the consumer product sector.
Profitability
As cited by Lu and Tsaic (2010), there are evidence that higher intangible values are
significantly associated with higher profitability. The higher the profit, the higher voluntary
recognition of intangible assets by firms and this attract potential investors to their firm. Also,
the voluntary recognition of intangible assets shows the true profit of a firm and enhances
profitability of the firm thereby increasing its cost of share capital in the market drawing
more shareholders to invest.
Leverage
Higher leverage (usually measured by the ratio of total liabilities/total equity) suggests
higher agency costs, due to the potential size of wealth transfer from debt-holder to
shareholders. Thus firms with higher leverage have more incentive to disclose information
voluntarily, thereby hoping to reduce agency costs (Oliveira et al., 2006); (Kang and Gray,
2006). Kang and Gray (2006) also proposed an alternative hypothesis that there is a negative
relationship between leverage and intangible assets voluntary recognition by emerging
market companies based on the following two premises. First, IA and their subsequent
voluntary recognition may not be as relevant to existing creditors as they are to shareholders
and potential future investors. That is, it may not be the level of debt that is significantly
related to the level of IA recognition; rather, it is the amount of equity in the capital structure
that is positively associated with the voluntary recognition. In other words, it is possible that
there is a negative association between leverage and IA voluntary recognition. Second, it is
proposed that the association between leverage and IA recognition may be influenced by the
underlying conceptual status of the debt market in emerging economies.
Company with Foreign Activities
Managers of companies operating in several geographical areas have to control a greater
amount of information, due to the higher complexity of the firm’s operations (Cooke,
1989).They are prone to increase their voluntary recognition to show their international
presence to stakeholders as a perceived good signal. The extent of voluntary recognition of
intangibles information is positively related to the internationalization of firm (Oliveira et al.,
2006).
Age of Company
This is concerned with the age of company from its conception. It is proposed that “older”
and therefore more established companies are more likely to have a chain of value creating
IA as part of their operating activities since these companies have had more time to establish
their customer and supplier networks, contribute towards communities, and set up
opportunities such as alliances with research centers and universities to benefit from these
ventures. As such, they would engage involuntary recognition practices to inform various
stakeholders of their IA.
Another argument, which supports a positive relationship between age of the company and
the level of IA voluntary recognition, is based on the premise that established companies are
more likely to consider expanding their operations or to provide investment opportunities in
the global market.
That is, these companies perhaps would consider global markets as a way of raising capital,
and therefore engage in higher level of voluntary recognition practices (Kang and Gray,
2006).
2.2
Empirical Review
Determinants of intangible assets’ recognition
Perry & Grinaker (1994) investigate on a US sample the extent to which earnings
management goals appear to affect the pattern of research and development activity. The
results of this study are consistent with the hypothesis that managers adjust R&D
expenditures to meet earnings expectations. Because R&D is a critical factor in the global
competitiveness of U.S. industry, they believe that the FASB should consider allowing firms
to capitalize more R&D expenditures. Luft & Shields (2001) use an experimental design to
test whether capitalization vs. expensing of intangibles expenditures results in fixation even
when individuals have opportunities to learn. Their results show that learning is not
necessarily a quick remedy for fixation on accounting, because accounting can influence the
learning process itself. (Muller, 1999) uses the specific UK context where voluntary
recognition of brands was allowed, and examines UK firms’ contracting cost incentives for
capitalizing estimates of acquired brand value in their financial reports. His results indicate
that firms’ decisions to capitalize acquired brands were influenced by the impact that the
immediate write-off of goodwill to equity has on the London Stock Exchange’s shareholder
approval requirement for future acquisitions and disposals. Finally, Klassen, Pittman, &
Reed (2004) investigate the impact of tax incentives and financial constraints on corporate
R&D expenditure decisions by comparing R&D expenditures in the United States and
Canada.
They document positive relations between tax credit incentives and R&D spending
consistent with both Canadian and U.S. public companies responding as though they are not
financially constrained. The Canadian credit system induces, on average, $1.30 of additional
R&D spending per dollar of taxes forgone while the U.S. system induces, on average, $2.96
of additional spending.
Consequences of intangible assets’ recognized as an asset
Using simulated data, Healy, Myers, & Howe (2002), show that for providing valuerelevant
information to investors, capitalization is a better accounting treatment than expensing for
intangibles. The model simulates alternate performance measures for pharmaceutical firms,
with expensed or capitalized R&D expenses. Their results show that performance measures
based on capitalization are twice as explanatory as performance measures based on
expensing. In an experiment utilizing MBA student participants, Seybert (2010) finds that
managers responsible for initiating an R&D project are more likely to overinvest when R&D
is capitalized. High self-monitors (those most likely to alter their behaviors to convey a
positive image) are most likely to overinvest, suggesting that reputation concerns contribute
to this behavior. A follow-up survey reveals that, when R&D is capitalized, experienced
executives anticipate overinvestment and expect project abandonment to have a stronger
negative impact on the responsible manager’s reputation and future prospects at their firm.
Mohd (2005) investigates the impact of capitalizing or expensing software development
costs on information asymmetry. Within the software industry, the study finds that
information asymmetry is significantly lower for firms that capitalize than for those who
expense software development cost. Thus, accounting for software development costs seems
to reduce information asymmetry and consequently, the cost of capital. Following these
results, Ciftci (2010) explores how accounting choice for software development costs affects
earnings quality in the software development industry. He documents a decline in the quality
of earnings in the software industry after the adoption of SFAS No. 86, whereas no such
decline is observed in other high-tech industries. Within the software industry, the quality of
earnings for expense is greater than for capitalizers. Among capitalizers, those with a large
increase in software capital have lower earnings quality than others. Based on a sample of
Australian listed companies, Wyatt (2005) examines the extent to which management makes
accounting choices to record intangible assets based on their insights into the underlying
economics of their firm. The results suggest that management’s choice to record intangible
assets is associated with the strength of the technology affecting the firms operations, the
length of the technology cycle time, and property-rights-related factors that affect the firm’s
ability to appropriate the investment benefits. On the Australian context as well, Ahmed &
Falk (2006) examine the value relevance of Australian firms’ discretionary R&D accounting
policy and the association between this expenditure and the firm’s future economic
performance. The results indicate that managerial discretionary accounting practice,
capitalizing or expensing R&D expenditure, demonstrates greater value relevance than
accounting figures that are the product of mandatory R&D expensing, R&D capitalized
expenditure is positively and significantly associated with the firm’s future earnings. Jones
(2011) also uses Australian data for evaluating the merits of intangibles capitalization from a
bankruptcy and default risk perspective. The results show that failing firms capitalize
intangible assets more aggressively than non-failed firms over the 16-year sample period, but
particularly over the five-year period leading up to firm failure. Managers’ propensity to
capitalize intangible assets has a strong statistical association with earnings management
proxies, particularly among failing firms. Voluntary capitalization of intangibles has strong
discriminating and predictive power in a firm failure model.
Two papers have studied the UK context where capitalization of R&D was allowed.
Anagnostopoulou & Levis (2008) examine the impact of R&D investments on the
sustainability or persistence of operating growth and market performance. They document a
positive relation between R&D intensity and consistent growth in sales and gross income
(but only in the cases when a firm needs to engage in R&D activity because of the industry
in which it operates) and show that R&D intensity improves persistence in subsequent excess
stock returns. Oswald (2008) investigates the determinants and value relevance implications
of the accounting method choice for R&D expenditures in the United Kingdom. The
decision to expense versus capitalize R&D expenditures is influenced by earnings variability,
earnings sign, firm size, R&D intensity, leverage, steady-state status of the firm’s R&D
program, and R&D program success. There is little difference in value relevance between
reported and adjusted numbers for both the expensers and the capitalizers. The evidence in
this paper suggests that managers choose the accounting method for R&D in order to best
communicate the private information which they hold. The Italian case has been also studied,
Markarian, Pozza, & Prencipe (2008) examine whether companies’ decisions to capitalize
R&D costs are affected by earnings-management motivations. The results show that
companies tend to use cost capitalization for earnings-smoothing purposes. But the
hypothesis that firms capitalize R&D costs to reduce the risk of violating debt covenants is
not supported.
In the French context, Cazavan-Jeny, Jeanjean, & Joos (2011) examine whether managers’
decisions to capitalize or expense R&D expenditures convey information about the future
performance of the firm. They show that the decision to capitalize R&D is generally
associated with a negative or neutral impact on future performance, even after controlling for
self-selection. Their results suggest that management is unable to truthfully convey
information about future performance through its decision to capitalize R&D.
Consequences of intangible assets recorded as an expense
Bah & Dumontier (2001) use international data from Europe, UK, Japan and US for
examining the evidence on the cross-sectional structure of corporate policy choices by
focusing specifically on firms that spend a significant proportion of their net revenue in
R&D expenditures. They show that, in accordance with the under-investment paradox, the
asset substitution problem and the asset specificity proposition, R&D intensive firms exhibit
significantly lower debt and dividend payment levels, but longer debt maturity and higher
cash level than non-R&D ones.
Using US data, Kothari, Laguerre, & Leone (2002) compare the relative contributions of
current investments in R&D and PPE to future earnings variability and show that R&D
investments generate future benefits that are far more uncertain than benefits from
investments in PPE. Amir, Guan, & Livne (2007) complement this paper in investigating the
association of investments in R&D and in physical assets with subsequent earnings
variability. They document that R&D contributes to subsequent earnings variability more
than CAPEX only in relative R&D intensive industries. However, in physical assetsintensive industries, there are no such relations. The results also suggest there was a shift in
the relations between R&D and CAPEX over time. Ho, Xu, & Yap (2004) investigate the
relationship between a firm’s R&D intensity and the risk of its common stock, by analyzing
a sample of firms which are more profitable, larger in market capitalization and more R&D
intensive than the universe of US-listed firms. They show that R&D intensity is positively
related to systematic risk in the stock market. The greater systematic risk is largely
attributable to the greater intrinsic business risk and the greater operating risk of R&D
intensive firms.
Brown & Kimbrough (2011) examine the effect of intangible investment on earnings noncommonality, defined as the extent to which a firm’s earnings performance is determined by
firm-specific factors versus market and industry factors. Their results suggest that the
success of intangible investment as a differentiation strategy depends largely on the
effectiveness of mechanisms used to protect intangible investments from expropriation.
Ciftci & Cready (2011) explore how R&D-related earnings performance, earnings variability
and value relevance depend on firm size. Positive association between the level of future
earnings and R&D intensity increases with firm size. They document a positive association
between the volatility of future earnings and R&D intensity that decreases with firm size,
consistent with R&D productivity increasing with scale. R&D scale is associated with lower
market returns, consistent with the idea that R&D investment risk declines with scale.
Johnston (2012) examines whether the variability in the future earnings stream generated by
the investment in environmentally-related R&D projects is different than that created by the
investment in other R&D projects. He concludes that the environmental component of R&D
expenditures contributes significantly less to the variability of future earnings than the
residual component.
In a recent paper, Shust (2015) examines whether the extent to which firms engage in R&D
(R&D intensity) is associated with the extent to which they engage in accrual-based earnings
management, as measured by discretionary accruals. He shows that R&D intensity is
positively associated with accrual-based earnings management, more specifically that greater
R&D intensity is associated with a smaller positive effect of discretionary accruals on
earnings volatility, suggesting that R&D intensity and discretionary accruals constitute non
additive sources of information uncertainty.
Pandit, Wasley, & Zach (2011) complement those studies in looking at the production of
patents. They study how measures of innovation outputs, that is, patent counts and patent
citations, are related to firms’ future performance. One contribution of this study is that it
demonstrates that the relation between R&D expense (inputs) and future operating
performance is better understood by incorporating information about the productivity (output)
of a firm’s R&D outlays in the form of patent counts and citations. They show that firms’
future operating performance is positively related with the quality of their patents and that
this relation is stronger for more productive and innovative firms. Overall, firms that have
more productive R&D exhibit higher and less volatile future operating performance.
Overall these papers provide evidence of a link between R&D capitalization and earnings
management purposes, but also a good indicator of future firm’s performance. They also
conclude that R&D investments are riskier than CAPEX.
2.3
Theoretical Review
Carnegie & Kallio (1988) examine the annual reports of the 100 largest listed Australian
companies, ranked by market capitalisation, in the financial year 1987. They examine the
accounting policies adopted for intangible assets other than goodwill and consider the impact
of Accounting Guidance Release 5 (AGRS) "Accounting for Intangible Assets Recognised
in Accordance with Statement of Accounting Standards AAS 18 'Accounting for Goodwill'".
The findings show that non-compliance with the guidance release is continuing, with 35.4%
of the sample companies adopting the practice of zero amortisation for Intangible asset. One
half of the sample companies which were not amortising Intangible asset provided the reason
that amortisation was not necessary because these assets did not diminish in value. Goodwin
& Harris (1991) examine the annual reports of a sample of top 90 listed Australian
companies (excluding trust and funds, and mining companies) in the period 1987 6 to 1989,
ranked by market capitalisation, in order to identify the accounting policy changes resulting
from compliance with both the Australian Accounting Standard AAS 18 "Accounting for
Goodwill" and ASRB 1013 "Accounting for Goodwill". The findings show that, by 1989,
only a few companies in the sample were not complying with the requirements of AAS 18
"Accounting for Goodwill" and ASRB 1013 "Accounting for Goodwill". However there is
an increase in the number of companies who record Intangible asset and do not amortise
either in full or in part. One-third of the companies recording Intangible asset revalued at
either directors' valuation or an independent valuation, rather than recording Intangible asset
amortised at cost. The findings suggest that the management's accounting choices for
goodwill and Intangible asset in most of Australia's largest companies seem to be influenced
by the goodwill accounting standard ASRB 1013 "Accounting for Goodwill". Wines &
Ferguson (1993) examine a sample of 150 Australian Stock Exchange listed companies in
the period 1985 to 1989 in order to examine the accounting policies adopted for goodwill
and for II As. The findings show that, over the study period, non-compliance with goodwill
accounting standards AAS 18 "Accounting for Goodwill" and ASRB 1013 "Accounting for
Goodwill is decreasing, but the practice of companies recording Intangible asset and electing
not to amortise Intangible asset io increasing. As a result, this supports the argument that
companies have been recognising Intangible asset to ;,, reduce the influence of the
requirement of accounting standards for goodwill amortisation on reported operating profits,
7 In summary, the findings of the above studies sug;est that the compliance with the relevant
goodwill accounting standard to amortise goodwill systematically over a period not
exceeding 20 years (ASRB, 1988, ASRB 1013, clause .35) may cause the companies shift to
non-compliance with AGRS "Accounting for Intangible Assets Recognised in Accordance
with Statement of Accounting Standards AAS 18 Accounting for Goodwill ". In other words,
the companies have started to re-classify goodwill as Intangible asset and have adopted the
practice of zero amortisation for some or all of the Intangible asset. It appears that the only
empirical study which is using contracting theory is Coombes et al. (1993). They select a
sample of top 150 Australian Stock Exchange listed companies, ranked by market
capitalisationr as at 30 June, 1988, over the period 1986 to 1989. They examine the
Intangible asset' amortisation accounting policy choices and the corporate lobbying
submissions made in 1989 on ED49 "Accountin; for Identifiable Intangible Assets". The
findin;~ support the Intangible asset' growth option hypothesis and the effects of Intangible
asset' lives hypothesis, that is, the incentives to amortise Intangible asset depend upon the
payoffs under claimholder contracts in the firms in order to indicate 8 the cash flows to be
derived from the Intangible asset. Support of the findings for the profit-based management
compensation incentives to amortise Intangible asset only occurs in 1989 when the AARP
introduced ED49 "Accounting for Identifiable Intangible Assets". In addition, the findings
reveal that the debt covenants hypotheses for existing debt and future debt raisings, and the
political visibility costs hypothesis receive little or no support. From the literature reviewed,
with the exception of Coombes et. al. ( 1993), the use of contracting theory to demonstrate
some explanation for the amortisation of Intangible asset seem& to be extremely thin. It is
the purpose of this research to employ contracting theory to examine the reasons why
comi'anies adopt particular Intangible asset 1 amortisation policy. Contracting theory will be
discussed in the following section.
2.4
Existing Gap in Literature
A number of research mentions that there are differences between countries in the
implementation of the IFRS 3 standard, which may influence the recognition of identifiable
intangible assets. However, the issue of enforcement differences between countries and their
impact on the implementation of the standard is largely unaddressed in the academic
literature .
The paper also notes that previous research may have had a different focus area and may be
influenced by different factors, which could explain the divergent results in the recognition
of identifiable intangible assets.
Therefore, there is a gap in the literature regarding the specific determinant between
countries on the recognition of identifiable intangible assets in the banking industry
according to the IAS 38 standard. Further research is needed to explore this relationship and
its implications for financial reporting consistency and comparability.
2.5
Theoretical Framework
Stakeholders Theory
Edward Freeman R propounded the stakeholder theory. in 1984. The stakeholder theory
argues that the recognition of corporate information to stakeholders relating to the most
important activities has an organizational responsibility, being the principal
source of recognition through financial statements Maria, Ana & María, (2011). In this
regard, Rodrigues (2006) considers that, due to the complexity of the economic reality and
the increasing ownership by groups of intangible assets, these statements continuously
deviate from the purpose of providing the picture of the business reality to the external users.
Recently, Freeman (2002 ) argued in its updated stakeholder
theory that organizations need to go beyond maximizing shareholder wealth to address the
interests of its stakeholder groups and individuals who may affect or be affected by the
Determinants of recognition of intangible assets for the Commercial banking purpose and
existence of the organization. Such stakeholders are known as possible winners or bearers of
any danger the company might be subjected to (Post et al . , 2002) and Dubey, Gunasekaran
and Papadopoulos (2017) and Dubey, Gunasekaran, Papadopoulos, Childe, Shibin and
Wamba (2017). In line with that, Freeman concluded that beneficiaries should be changed
from stockholders to stakeholders, and that effective decision- making authority should be
given on a par with the executives of the firm (Stieb, 2009). The author further argued that
"each of these stakeholder groups has the right not to be treated as a means for some purpose
and must therefore participate in determining the company's future direction in which they
have an interest" (Freeman, 2002).
Several stakeholders' quality analysis in business publications confirms the significance of
this hypothesis within our research (Guthrie et al., 2006). The philosophy of stakeholders
suggests that all stakeholders are entitled to knowledge on how corporate practices have
influenced them, even though they chose not to do it (Deegan, 2000). The various pressure
groups considered to have an stake in influencing some elements of an organization can be
conveyed easily by the annual report (Guthrie, Perry & Riccert, 2004). Companies will also
voluntarily disclose information such as human capital to meet stakeholders' demands that
have the power to control the organization's required resources. Stakeholders should also be
seen not only as existing Determinants of recognition of intangible assets for the Commercial
banking ones, but as having legitimate impacts on the companies. The partnership can be
seen as a friendship of two kinds (Olajide, Olugbenga, Lateef & Ajayi, 2018). What
stakeholder the company needs can vary. Some will actively seek to influencewhat the
organization is doing and others might be concerned with limiting the effects of the activities
of the organization on themselves. Stakeholder partnerships can also vary; future
relationships may include disputes, sponsorship, daily dialogue, and joint partnership.
2.2 Leverage and Intangible Assets Recognition
Leverage reflects the share of fixed-interest capital within a bank's capital structure.
Enterprise leverage suggests using debt to finance the company's activities. Proponents of
the agency hypothesis claim that a high-leverage organization is forced to reveal more
information to reduce service expenses, resulting from the possible scale of the transition of
capital from bond holders to owners in accordance with signalling and stakeholder
hypotheses (Oliveira, Rodrigues, & Craig. 2006). In comparison, leverage, in many
comparative analyzes as a systemic feature, has appeared with conflicting findings in terms
of its impact on transparency activities within businesses. Black, Lee and Tower (2007) and
Craig and Olivera, Rodrigues and. (2006) accurately noted that, in several surveys in various
countries and industries, no consistent association between indebtedness and release of
information was Determinants of recognition of intangible assets for the Commercial
banking found. In addition, Roberts and Gray (1995), Ferguson, Lam and Lee ( 2002) found
a positive relationship. Chow and Wong-Boren (1987), Wallace, Naser and Mora (1994),
and Wallace and Naser (1995) did not even find any connection between leverage and
voluntary recognition of intangible assets. Meanwhile, in its annual reports, Sujan and
Abeysekera (2007 ) found a favorable association between a firm's low-leverage financial
arrangement and further recognitions. Leverage has a connection with firm success which is
negative and statistically important. For thehighly oriented firms, the relationship between
leverage and output of the company is statistically important at a point of importance of 10
per cent. This observation is in tandem with Gleason, Mathur and Singh (2000), Agarwal and
Elston (2001), Abor (2007), and Chen Firth, and Zhang ( 2008), respectively. That
demonstrates that leveraging has a negative effect on firm results. A rise in the leverage ratio
(decrease) would minimize (increase) company output by 15.8 per cent. As the relationship
isstatistically significant, it is consistent with the expectation on traditional capital structure
theory. On the contrary, in our analysis, macroeconomic variables show a positive effect on
Ogebe, Ogebe, & Kemi (2013) firm results. There are some inconsistencies, however, that
existed in the literature, which is why the current study does not intend to propose any signs,
rather we hypothesize that there is a significant relationship between leverage and
recognition of intangible assets.
CHAPTER THREE
METHODOLOGY
3.1
Research Design
The research design of this study will focus on the determinant of recognition of intangible
assets in the Commercial Banking sector in Gambia. It will utilise a combination of ex-post
facto and quantitative research methods, relying heavily on historical data from annual
reports of selected banks in Gambia .
The study will collect data from 10 selected banks quoted on the Gambian Stock Exchange
floor, which consistently submitted their annual reports from 2012 to 2021. This data will be
used for empirical analysis of past events .
The research design to be employed include descriptive statistics, a matrix of correlation, and
regression of ordinary least square to analyze the gathered data .
This study is aimed to determine the relationship between bank size, bank age, and
intangible asset recognition. It will examin the interaction between these variables and their
impact on the recognition of intangible assets in Gambia Banking sector.
The research design will be base on secondary data, indicating a retrospective approach to
understanding the determinants of intangible asset recognition in the banking sector in
Gambia.
3.2
Area of Study
The area of study is the recognition of intangible assets in the Banking sector in
Gambia.The research will focus on determining the factors that influence the recognition
of intangible assets in the banking sector.
3.3
Population of the Study
The population of this study is 12 operational banks in Gambia, as at the end of 2021.
The study collected data from a sample of 10 selected banks in Gambia that were quoted
on the Gambia Stock Exchange floor and consistently submitted their annual reports
from 2012 to 2021.
3.4
Sampling Technique and Sample Size
This study will use random sampling technique to select the sample Banks, ensuring equal
chances of representation and no bias in selection. The population for the study consisted of
quoted Gambia Banks with annual financial reports from 2012 to 2021.A sample of 12
Gambian banks was selected over a Ten-year period (2012-2021).The sample banks' annual
financial statements from 2012 to 2021 will be examined to determine the recognition
voluntary of intangible assets.
3.5
Types and Sources of Data Collection
The study will utilized secondary data collection methods. The data will be collected from
the annual financial statements of 10 randomly selected Gambian quoted banks over a tenyear period (2012-2021) .The recognition of intangible assets in the annual reports of these
banks will be examined.The data to be collected include information on bank-specific
characteristics such as industry type, profitability, leverage, company with foreign activities,
and age of the firm .The study will also reviewed literature on determinants of intangible
asset recognition, which will provide additional insights into the factors influencing
recognition practices .
The sources of data for the study will be the annual financial reports of the selected Gambian
quoted banks.
3.6
Definition and measurement of Variables
The study will focus on the determinants of intangible asset recognition in the Gambian
Banking sector. The variables to be examined in the study include:
Intangible Asset Recognition (INTAR): This variable represents the extent to which
banks recognise their intangible assets in their financial reports .
Bank Size (FSIZE): This variable measures the size of the banks included in the study,
which may have an impact on the recognition of intangible assets .
Bank Age (BAGE): This variable represents the age or vintage of the banks, which is
expected to influence the recognition of intangible assets.
Leverage (LEVGE): This variable measures the level of debt or financial leverage of the
banks, which may affect the recognition of intangible assets .
Profitability (PROF): This variable reflects the profitability of the banks, which is
examined for its potential impact on the recognition of intangible assets.
Model Specification
The following model (Regression model) which examines the relationship between a
dependent variable and two or more repressors or independent variables was adopted for the
respective variables and hypotheses in order to test for the relevance of the hypotheses
regarding the determinant of the recogniton of intangible assets in the Gambia Banking
sector.
Y = β0+ β1 FSIZE + β2BAGE + β3LEVGE+ + β4PROF+ μ (1)
ß0, are the intercepts;
ß1, B2, B3 and B4 are the coefficients of the explanatory variables and also the coefficients
of the moderating variables and μ are the error or disturbance term that absorbs the influence
of omitted variables in the proxies used.
Where
Y = Intangible Asset Recognition (INTAR) as dependent variables
while independent variables are as follows:
FSIZE = Firm Size(X1)
BAGE= Banks Age(X2)
LEVGE = Leverage(X3)
PROF = Profitability measured using Profit Margin(X4).
3.7
Validity and Reliability of Research Instruments
The study used descriptive statistics, correlation matrix, and regression analysis to
analyze the data . With consideration of underlying assumptions and data quality,
descriptive statistics, correlation matrix, and regression analysis can be reliable tools for
summarizing, exploring relationships, and predicting outcomes in research. This study
relied on secondary data from the annual reports of selected banks in Gambia for the
period 2012-2021.The Pearson correlation coefficients were used to assess the
relationship between the variables. Binary logistic regression analysis was also
conducted to test the hypotheses formulated in the study.
3.8
Method of Data Analysis
This
study will utilized descriptive statistics, correlation analysis, and regression
analysis to analyze the data .
Descriptive statistics will be used to summarize and describe the data collected from the
annual reports of selected banks in Gambia.The Pearson correlation coefficients will be
calculated to assess the relationship between intangible asset recognition and its
determinants. A correlation matrix will be constructed to examine the degree of
connection between intangible asset recognition and its determinants .Regression
analysis, specifically ordinary least squares regression, will be conducted to determine
the relationship between the independent variables (bank size, bank age, leverage, and
profitability) and intangible asset recognition.Binary logistic regression analysis will also
be performed to test the hypotheses formulated in the study.
CHAPTER FOUR
DATA PRESENTATION, ANALYSIS AND DISCUSSIONS
4.1
DATA PRESENTATION
This study examines the determinant of intangible assets recognition in Gambian banks. The
population for this study is made up of only 10 selected banks in Gambia with annual
financial reports for the period 2012 to 2021. Over ten years a sample of 10 banks in
Gambia were selected.
Descriptive statistics were conducted, correlation matrix and firmly observed binary
regressions in identifying the specific characteristics and exogenous factors of the possible
firm that would influence the firm 's decision to recognise intangible assets in the financial
reports. The variable for this study includes a dummy dependent variable that would
otherwise take the value of "1" for banks recognising intangible assets in their financial
report (INTAR) and "0". The autonomous variables were bank size (FSIZE), bank age
(BAGE) leverage (LEVGE) and profitability (PROF). This research examined and
interpreted data gathered from ten (10) banks selected in Gambia. The research followed the
ordinary least square ( OLS) approach in evaluating the data to classify the potential factors
that decide the recognition of the intangible assets. The study conducted some preliminary
analyzes, like descriptive statistics and matrix of correlation. The following is the descriptive
statistics for a ten year period from Ten sampled banks in Gambia.
4.2 Descriptive Analysis
The descriptive statistics for the dependent and independent variables used in this study were
presented in table 1 below:
Table 1. Summary of descriptive statistics for the variables employed in this
study
INTAR
LEVGE
FSIZE
BAGE
PROF
Mean
0.59
0.23
0.37
23.0
0.51
Median
1.00
0.17
0.34
17.0
0.41
Maximum
1.00
1.49
0.79
57.0
0.83
Minimum
0.00
0.15
0.19
11.0
0.24
Std. Dev.
0.46
0.24
0.14
14.2
0.19
Skewness -
0.27
5.51
0.88
1.19
0.31
Kurtosis
1.21
2. 87
4.65
3.26
1.61
Jarque-Bera
5.64
11.04
12.06
11.80
3.24
Probability
0.04**
0.00*
0.00*
0.00*
0.16
Sum
20.0
6.95
13.6
63.6
16.4
Sum Sq. Dev.
7.56
1.59
0.54
44.7
1.52
Note: *1% level of Significance , **5% level of significance
The concise statistics table 1 above tests the distribution of normality of all
variables by showing their mean, minimum, maximum values and Jarque-Bera (JB)
numbers. From the table above, the primary variable is the declaration of intangible assets
while independent variables are company scale, bank age, equity and productivity. As seen in
Table 1 above, the total allocation of intangible assets was 59 percent, and the standard
deviation was 49 percent, while the maximum and minimum value are 1/0 percent ,
respectively, since it is a binary number that means zero and one. This is to say that 59 per
cent of its total assets are the mean and average recognition of intangible assets. The result
gave some insight into the nature of the banks selected under study. Firstly, the large
difference between the maximum and minimum values of the determinants of the recognition
of intangible assets shows that the sampled banks differ greatly; this has also been reaffirmed
by the standard deviation value indicating that the sampled banks are not dominated by
banks whose recognition of intangible assets is below average. This reveals that in their
annual report, half of the banks or 59 per cent of the sampled banks recognise their
intangible assets. The variance in the maximum and minimum value of selected banks'
intangible asset declaration showed that our sampled banks are homogeneous, and the
selected calculation techniques do not take heteroscedasticity problem into consideration.
Hence, this justifies the use of ordinary least square regression techniques (OLS).
Ultimately, in Table 1, the Jarque-Bera (JB) checking the normality or presence of outliers or
extreme value of variables reveals that the firm scale, bank age and leverage are normally
distributed at a level of 1 percent, while the measurement of intangible assets is normally
distributed at a level of 5 percent, except for the productivity normally distributed at a level
above 10 percent. It means that no variables of outer are likely to skew the inference even
though there are, and are thus accurate for generalization drawing. Overall the descriptive
statistics showed that there is no sample collection bias or outlier in the results that would
hinder this study's generalization. It often supports the use of common least square
calculation methods.
4.3 Correlation Matrix
The research used the Pearson correlation coefficients (correlation matrix) when analyzing
the relationship between the variables, and the results are described in table 2 below. In order
to assess the existence of the interaction, i.e. positive or negative correlation, and the
importance of the relationship between contingent variable and independent variables,
Pearson's correlation matrix was extended to test the degree of connection between
intangible asset recognition and its determinants in Gambia. The results of the matrix for
correlation are given in Table 2.
Table 2. Pearson correlation matrix
INTAR
FSIZE
BAGE
LEVGE
INTAR
1.00
FSIZE
-0.36
1.00
BAGE
0.59
0.29
1.00
LEVGE
0.10
0.59
0.36
1.00
PROF
-0.25
0.51
0.49
0.24
PROF
1.00
The purpose of planning this matrix of correlation is checking the potential degree of multico linearity between the independent variables. Result from the aforementioned table showed
that the allocation of intangible assets is negatively associated with bank size and
competitiveness while being positively correlated with bank age and leverage. The results
from the matrix table of correlations revealed that a moderate negative relationship exists
between the recognition of intangible assets and their determinant, calculated by company
size and productivity, whereas a positive association exists between the recognition of
intangible assets and bank era. A close glance at the significance of the effects of the Pearson
correlation coefficient showed that all the factors are closely related to the firm's decision to
list intangible assets in its annual financial reports. This was also discovered that all the
determinants of the recognition of intangible properties have a clear and constructive
interaction with one another.
The study noticed from the correlation table, when checking for multi-colinearity, that no
two explanatory variables were perfectly correlated. This suggests that the model used for
the study lacks the multi-colinearity problem. It often supports the use of the common least
square.
4.4
Test of Hypothesis and Discussion of Findings
To examine the relationship between the dependent variable (intangible asset Recognition:
INTAR) and the independent variables (FSIZE, BAGE, LEVGE and PROF) and to test our
formulated hypothesis, we used an ordinary less square regression analysis, since the data
had both time series (2012 to 2021) and longitudinal properties (10 selected banks). Our
review is laid out below in Table 3:
Table 3. Summary of binary logistic regression analysis
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
0.81
0.18
4.51
0.00
FSIZE
-1.23
0.58
-2.11
0.04
BAGE
0.03
0.00
7.36
0.00
LEVGE
0.26
0.29
0.83
0.41
PROF
-1.29
0.32
-4.02
0.00
R-squared 0.74
Adjusted R-squared 0.72
S.E. of regression 0.29
Sum squared resid 2.36
Log likelihood -2.50
F-statistic 18.73
Prob(F-statistic) 0.00
Durbin-Watson stat 1.69
Table 3 above shows selected banks' ordinary, least square, regression analysis in Gambia.
From the data above the McFadden R-squared value from the data of the conditional logistic
regression reveals that all independent variables will collectively estimate about less than
one per cent of the outcome of the dependent variable. The McFadden R-squared 's strong
success shows that intangible reporting activities of selected banks in Gambia can be
estimated by the particular features of the banks that are included in the model. This means
that most banks in Gambia that may recognise intangible assets in their annual report do so
and they consider most of their specific features. When seen in Table 3 above, the 18.73 Fstatistics and their 0.000 Pvalue shows that all of our regression models are reasonably
relevant and well-specified . F-statistics for the model revealed that the overall model is
statistically meaningful and valid in explaining the dependent variable result. The result also
showed that the R-squared value of 0.74, which is equal to 74%, suggests that the
independent variables explained approximately 74% of the systemic variance in the selected
banks' intangible asset recognition strategy over the ten (10) years measured, while the
remaining 26% were explained beyond the unidentified variables collected by the selected
bank. The value of R-squared, which is the decision coefficient, stood at 74 percent , which
means that the model explained 74 percent of the statistical differences in actual dependent
variables, while 29 percent remained essentially unknown. We also observed an improved Rsquared value of 0.72. This suggests that all independent variables jointly explain about 72
percent of the system variation in our sampled banks' policy of recognition of intangible
assets over the 10-year period, while about 28 percent of the total variations were uncounted
for, thus captured by the stochastic error term. In fact, the Durbin Watson figures of 1.69
shows that the model is well distributed and that there was no problem with self or auto
association, and the error is independent of each other.
In addition to the above, the results of binary logistic regression of measurable banks as
shown in Table 3 are given and their different findings from each explanatory variable are
translated as follows:
H1: Leverage has no significant effect on recognition of intangible asset.
It can be observed that the leverage has a positive coefficient value of 0.26 which was
insignificant for statistics. Leverage was found to be statistically insignificant and positively
linked to the likelihood that banks will disclose intangible assets for stakeholders. This
suggests a increase in Gambia banks' debt reduces their risk of reporting intangible assets.
Thus, firms with higher leverage have more incentive to voluntarily disclose information,
thus hoping to reduce the cost of the agency. It can be observed that the leverage has a
positive coefficient value of 0.26 which was insignificant for statistics. Leverage was found
to be statistically insignificant and positively linked to the likelihood that banks will disclose
intangible assets for stakeholders. This suggests a increase in Gambia banks' debt reduces
their risk of reporting intangible assets. Thus, banks with higher leverage have more
incentive to voluntarily disclose information, thus hoping to reduce the cost of the agency.
That is, the level of debt will not be directly related to the extent of declaration of Intangible
Assets; instead, it is the amount of equity in the capital framework that is favorably
correlated with the voluntary recognition. In other words, there could be a favorable
correlation between the power and the voluntary release of intangible assets. Second, the
correlation between leverage and recognition of intangible assets may be affected by the
underlying structural role of the bond sector in underdeveloped economies such as Gambia.
This observation may be true for Gambian creditors, since most long-term and short term
creditors in Gambia are not interested in focusing on financial assets, but rather how their
debt is serviced. This result led to the rejection of alternative hypothesis, which suggests that
the recognition and leverage of intangible assets by the firm should be significantly related.
So we accept our three null hypothesis and assume that debt has no major impact on the
accounting of intangible assets.
H2: Bank size has no significant effect on intangible asset recognition.
Based on the t-statistical values of the declaration of intangible assets and their correlation,
firm scale seems to be consistent with apriori assumptions which were statistically important
in justifying the decision by banks to reveal intangible assets to stakeholders in financial
results. The analysis resulting from the impact of bank size on the recognition of selected
banks' intangible assets showed a coefficient value of -1.24, a t-value of -2.11 and a P-value
of 0.04. The coefficient value of -1,24indicates that bank size has a negative effect on the
divulgation of chosen banks' intangible assets. It suggests that a decrease in the overall log of
bank assets contributes to an rise of about 1.2 trillion in the release of selected banks'
intangible assets. By extension, this implies that an rise in banks' size would result in its
estimation of intangible assets falling to around 1.2 trillion. That means small banks are
likely to reveal more subjective information on a voluntary basis than big banks. Lesser
banks also allow their clients to share more detail in annual reports. We argued that small
banks could allow their clients to share more details because they want to protect their
integrity, improve the framework of their assets and ensure that they maintain their clients.
The t-value of -2.116shows that bank size greatly impacts their decision to report their
intangible assets. The probability value of 0.04 indicates that the impact of bank / firm size
on the recognition of intangible assets of selected 10 banks in Gambia is statistically
important at a degree of significance of 5 per cent. The result p-value reaffirms the results of
the t-test statistics. Based on the substantial findings obtained from the results of the study,
this finding denies the hypothesis (H1), which notes that bank size has no significant impact
on the recognition of intangible assets by banks and instead supports the alternative
hypothesis and suggests that bank size has a significant impact on the recognition of
intangible assets by banks.
H3: There is no significant effect between profitability and recognition of intangible asset.
The profitability of the Bank (PROF) also tends to be statistically significant and negatively
associated with the risk that banks would declare intangible assets in financial reports, based
on tstatistical values of intangible asset recognition and its correlation. This indicates that a
decline in the level of profitability of banks results in an increase in the recognition of
selected banks' intangible assets to 1.29 percent. This implies that an increase in the banks'
profit level will result in a decrease in intangible assets of around 1.29 per cent. There is
evidence of a strong correlation between higher intrinsic qualities and higher productivity.
The higher the profit, the higher the banks 's recognition of intangible assets and this attracts
potential investors to their business. This ensures that if Gambia's banks' productivity
improves they are likely to expose intangible assets. Furthermore, the voluntary sale of
intangible assets shows a firm's true benefit and increases the firm 's competitiveness while
raising the equity capital investment in the market, enticing more shareholders to invest. The
t-value of -4.02 indicates that competitiveness of the banks has a significant impact on the
declaration of chosen banks' intangible assets. The probability value of 0.00 shows that the
impact of competitiveness on the recognition of selected bank's intangible assets in Gambia
is statistically important at 1 percent sense level. The result p-value reaffirms the results of
the t-test statistics. This finding contradicts the Hypothesis (H4), which notes that the
admission and productivity of intangible assets by the company has no significant impact
and instead suggests that productivity has a significant effect on the admission of intangible
assets, and was statistically important at a point of 1 percent.
H4: Bank age has no significant effect on intangible asset recognition.
From its inception this concerns the age of the bank. Bank Age (BAGE) has also been shown
to be favourably linked to the recognition of intangible assets, and their association was
statistically important in affecting the decision of banks to report intangible assets. The study
test revealed a coefficient value of 0.03, a t-value of 7.36 and a P-value of 0.00 respectively.
The coefficient value of 0.035 shows that bank age has a strong and beneficial impact on
banks' recognition of intangible assets, meaning that the older the banks, the greater their
recognition policy execution. The meaning of the slope coefficients, which is not consistent
with apriori standards, indicates that keeping other factors steady, older banks are less likely
than young banks to report intangible assets to stakeholders. It may be so because most
recently developed banks are more willing than old companies to follow best reporting
practices and standards. It is suggested that "older" and more established companies are more
likely to have a value chain creating intangible assets as part of their operational activities, as
these companies have had more time to establish their customer and supplier networks,
contribute to communities and create opportunities, such as alliances with research centers
and universities, to benefit from these undertakings As such, they will participate in
cooperative divulgation activities to educate various stakeholders about their intangible
properties. Another argument that supports a positive relationship between the age of the
company and the level of voluntary recognition of intangible assets is based on the premise
that established banks are more likely to consider expanding their operations or offering
opportunities for investment on the global market. This is, these banks will perhaps view
financial markets as a means to collect money, and therefore participate in higher volunteer
transparency activities The t-value of 7.36 shows this bank age is statistically important,
whereas the likelihood value of 0.00 suggests that the impact is statistically significant at 1
percent of importance. This finding contradicts hypothesis two and thus suggests that bank
age has a substantial influence on the recognition of intangible assets, which was statistically
important at a level of significance of 1 per cent.
4.5 Problems encountered in the Field
The research involve human participants, requires careful consideration of ethical guidelines.
Ensuring informed consent, maintaining confidentiality, and minimizing harm to participants
are critical concerns.
Defining and measuring key concepts such as intangible asset, recognition is complex.
Developing appropriate indicators and measurement tools that capture the multidimensional
nature of these concepts was crucial.
Limited time and resources for conducting fieldwork, data collection, analysis, and writing
pose practical challenges.
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1
Summary
This study examined the determinants of intangible asset recognition in the Gambia
Banking sector over the period 2012-2021 .The research used secondary data from the
annual reports of 10 selected banks in Gambia .Descriptive statistics, correlation
analysis, and regression analysis (ordinary least squares and binary logistic regression)
were employed to analyze the data .The study found a negative interaction between bank
size and intangible asset recognition, while a positive relationship was observed between
bank age and intangible asset recognition.The independent variables (bank size, bank
age, leverage, and profitability) accounted for 71% of the variation in intangible asset
recognition, with 29% of the variation unaccounted for. This study recommended
encouraging a reduction in bank size to enhance intangible asset recognition and
supporting the existence of old generation banks to comply with recognition policy.This
research highlighted the importance of
recognising intangible assets in financial
reporting and the need for guidelines and reporting standards in this area.
5.2
Conclusion
This research examined potential factors that could affect the recognition of intangible assets
by banks in Gambia. In this study, the 10 selected banks were drawn from all of the quoted
Gambia banks that maintained annual financial report from 2012 to 2021. In determining the
basic characteristics and exogenous factors of the future business that would affect the
decision of Gambian bank to report some type of Intangible Assets to its stakeholders; the
researcher performed concise data, correlation and clearly measurable differential logistic
regressions. Unlike most previous research that find banks scale, competitive banks, older
banks, and leverage as a major determinant of the bank’s decision to report intangible assets,
a majority of our variables have statistically relevant consequences in this analysis by use
Gambia data except for leverage. Although most variables maintained their apriori
expectations but were not statistically significant in influencing the decision of the selected
bank to recognise intangible assets in its annual reports. In general, the findings reveal that
the risk of most Gambain banks recognising intangible assets in their financial accounts is
closely linked to firm size and bank status. It is important to note from the report that
stakeholders involved in recognising of intangible assets pay more attention to particular
characteristics of the business except leverage which does not have any possible impact on
the recognition practices of intangible assets by banks. This is very important because banks
in Gambia which recognise intangible assets can not attribute this to their leverage status.
5.3
Recommendations
The following recommendation is hereby made from this study:
1.
This increase in firm size should be welcomed, as it enhances the recognition of
intangible assets by Gambian banks
2.
Less trust should be put on Gambia banks to improve the degree of competitiveness,
as it limits the willingness of banks to report their intangible assets.
The banks' age should be taken into account when forcing them to recognise their
3.
intangible assets as older banks prefer to follow such a strategy better than newly formed
banks.
5.4
Contribution to Knowledge
This research contributes to the understanding of the determinants of intangible asset
recognition in the Banking sector in Gambia.
It highlights the negative interaction between bank size and intangible asset recognition, as
well as the positive relationship between bank age and intangible asset recognition.
This study provides empirical insights into recognition of intangible assets and addresses the
inconsistency in measurement, valuation, and financial reporting on a wide range of
intangible assets in Gambia banking sector .
It emphasizes the need for guidelines and reporting standards, such as IAS 38, to ensure the
proper measurement and reporting of intangible assets in financial statements.
The research recommends encouraging a reduction in bank size and supporting the existence
of old generation banks to enhance intangible asset recognition and compliance with
recognition policy. Overall, this study contributes to the literature on intangible asset
recognition and provides valuable insights for decision-makers, stakeholders, and researchers
in the banking sector in Gambia.
5.5
Suggestions for further Studies
There is the need for the following:
Investigate the impact of other variables, such as profitability, leverage, and productivity,
on intangible asset recognition in the Banking sector in Gambia.
Explore the relationship between intangible asset recognition and financial performance
indicators, such as return on assets and return on equity, to assess the potential benefits of
recognising intangible assets on bank performance.
Conduct a comparative study to analyze the determinants of intangible asset recognition
in different sectors of the Gambian economy and identify any sector-specific factors that
influence recognition practices .
Examine the role of corporate governance mechanisms, such as board composition and
ownership structure, in promoting or hindering intangible asset recognition in the
banking sector . Investigate the impact of regulatory frameworks and reporting standards,
such as IAS 38, on the quality and consistency of intangible asset recognition in Gambia
Banking sector .
References
Agarwal, R. & J.A Elston. (2001), Bank-firm relationships, financing and firm performance
in German. Economics Letters, 22 (1), 225-232
Ahmed, K., & Falk, H. (2006). The value relevance of management’s research and
development reporting choice: Evidence from Australia. Journal of
Accounting and Public Policy, 25(3), 231–264.
Amir, E., Guan, Y., & Livne, G. (2007). The Association of R&D and Capital
Expenditures with Subsequent Earnings Variability. Journal of Business
Accounting, 34(1-2), 222–246.
Finance &
Anagnostopoulou, S. C., & Levis, M. (2008). R&D and performance persistence: Evidence
from the United Kingdom. International Journal of Accounting, 43(3), 293–320.
Bah, R., & Dumontier, P. (2001). R&D Intensity and Corporate Financial Policy: Some
International Evidence. Journal of Business Finance & Accounting, 28(5/6),
671–692.
Brown, N. C., & Kimbrough, M. D. (2011). Intangible investment and the importance
firm-specific factors in the determination of earnings. Review of Accounting
Studies, 16(3), 539–573.
of
Cazavan-Jeny, A., Jeanjean, T., & Joos, P. (2011). Accounting choice and future
performance: The case of R&D accounting in France. Journal of Accounting
and Public Policy, 30(2), 145–165. http://doi.org/10.1016/j.jaccpubpol.2010.09.016
Chen, G., Firth, M.; & W.W Zhang. (2008). The efficiency and profitability effects of
China's modern enterprise restructuring programme". Asian Review of Accounting,
16 (1) 74-91
Chow, C. & Wong-Boren, A. 1987.Voluntary financial disclosure by Mexican corporations.
Accounting Review 62(3): 533541.
Ciftci, M., & Cready, W. M. (2011). Scale effects of R&D as reflected in earnings and
returns. Journal of Accounting and Economics, 52(1), 62–80.
Dubey, R., Gunasekaran, A. and Papadopoulos, T. (2017), “Green supply chain management:
theoretical framework and further research directions”, Benchmarking: An
International Journal, Vol. 24 No. 1, pp. 184-218.
Ferguson, M.J., Lam, K.C.K. & Lee, G.M. (2002). Voluntary disclosure by state owned
enterprises listed on the stock exchange of Hong Kong, Journal of International
Financial Management and Accounting
13(2): 125-152.
Freeman, R.E. (2002), “Stakeholder theory of the modern corporation”, in Donaldson, T. and
Werhane, P. (Eds), Ethical Issues in Business: A Philosophical Approach, 7th ed.,
Prentice Hall, Englewood Cliffs, NJ, pp. 38-48.
Gleason K.C., Mathur I. & M. Singh (2000), Wealth effects for acquirers and investors
related to foreign divested assets", International Review of Financial Analysis, 9:1, 520.
Guthrie, J., Petty, R., Ricceri, f. (2006) The voluntary reporting of intellectual capital.
Comparing evidence from Hong Kong and Australia" Journal of Intellectual Capital,
7.254-271.
Ho, Y. K., Xu, Z., & Yap, C. M. (2004). R&D investment and systematic risk.
Accounting & Finance, 44(3), 393–418.
Johnston, D. (2012). Environmental R&D and the uncertainty of future earnings. Journal of
Accounting and Public Policy, 31(6), 593–6
http://doi.org/10.1016/j.jaccpubpol.2012.09.004
Jones, S. (2011). Does the Capitalization of Intangible Assets Increase the Predictability of
Corporate Failure? Accounting Horizons, 25(1), 41–70.
Kang H.H & Gray S.J (2008) Reporting intangible assets: voluntary disclosure practices of
top emerging market companies. Faculty of economics and business. University of
sydney
Kothari, S. P., Laguerre, T., & Leone, A. (2002). Capitalization versus expensing:
Evidence on the uncertainty of future earnings from capital expenditures versus
R&D outlays. Review of Accounting Studies, 7(4), 355–382.
Lu, Y., Tsai, C. & Yen, D. (2010). Discovering important factors of intangible firm value by
association rules. International Journal of Digital Accounting Research 10(2): 55-85.
Markarian, G., Pozza, L., & Prencipe, A. (2008). Capitalization of R&D costs and
earnings management: Evidence from Italian listed companies. International
Journal of Accounting, 43(3), 246–267.
Oliveira, L., Rodrigues, L.L. & Craig, R. (2006). Firm-specific determinants of intangibles
reporting: evidence from the Portuguese stock market. Journal of human resource
costing and accounting 10(1): 11-33.
Pandit, S., Wasley, C. E., & Zach, T. (2011). Information Externalities along the Supply
Chain: The Economic Determinants of Suppliers’ Stock Price Reaction
to Their
Customers’ Earnings Announcements. Contemporary Accounting Research, 28(4),
1304–1343.
Post, J.E., Preston, L.E. and Sachs, S. (2002), “Managing the extended enterprise: the new
stakeholder view”, California Management Review, Vol. 45 No. 1, pp. 6-28.
Shust, E. (2015). Does Research and Development Activity Increase Accrual-Based
Earnings Management? Journal of Accounting, Auditing & Finance
(fothcoming).
Sujan, A. & Abeysekera, I. (2007). Intellectual capital reporting practices of the top
Australian firm. Australian Accounting Review 17(2): 62-74. Sydney NSW 2006
Wallace, R. & Naser, K.(1995). Firm-specific determinants of comprehensiveness of
mandatory disclosure in the corporate annual reports of firms listed on the stock
exchange of Hong Kong. Journal of Accounting and Public Policy 25(97): 41-53.