Time Management in Corporations

Introduction

With the advent of digital technologies, corporate players have faced the trade-off between an unprecedented productivity gain, as uniformly shared by the market, and its flipside in terms of a unique edge that is progressively dissipated. Electronic commerce could amount to a major catalyst of whatever competitive advantage is on hand, albeit without affecting a substitute for one. Most sectors have turned into information-intensive with an eye on just how heavily they deploy computer-aided designs and social media marketing, or how much knowledge they need to put in as a matter of capturing the soon-to-erode niche in a fast cycle setup.

 

It would appear that such technologies have been able to free the majority of labor toward more productive uses or creative allocation. In light of the above, however, a trend is looming for the prompt response and efficient time management to make the difference in margin. The outdated complementarity of capital and labor should be rethought with an eye on how human capital allocates labor or time in ways that minimize the efficiency gap rather than maximizing productivity or gross margins alone. Whereas the conventional wisdom treats human capital as the aggregate of specific creative faculties that could be aligned against the corporate goals as core competencies, this study will view it as the ability to manage time towards minimizing whatever time-related efficiency slacks untapped.

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Problem Statement

One way to address or formalize efficiency would be to study how the gap between the supply of and demand for the ultimate products or the underlying labor input map into the equilibrium market price or wage. Although this dimension will be incorporated implicitly among other things, it is not the primary focus. The effective problem is about bridging labor allocations that are necessary from the standpoint of break-even or scale analysis versus sufficient as a matter of constrained optimization.

The latter embarks on a Lagrangian setup whereby the marginal revenue product of labor maximizes net variable labor costs and subjects to employment constraints. This may be about budget rationing or working capital management, yet in any case, the Lagrange multiplier (as the implicit or shadow price of deploying more or less labor compared to an optimum level) should be comparable to the wage and the marginal productivity.

By contrast, the break-even point analysis (BEP) suggests that some labor will still be allocated even if very little is being produced and sold, which amounts to the fixed cost component (e.g. the IT or accounting departments). Overall, it can be shown that the analysis of human capital with respect to time-managerial efficacy and efficiency proves to be rather involved even under strong assumptions, such as the demand functions being exogenous and the company being so small that it is more likely to take rather than shape the prices and wages.

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Purpose of the Study and Power Output

The primary focus will be on suggesting ways in which human capital can act as the implicit relationship between domains as diverse as, the efficiency gap, the static facets of corporate culture such as diversity or team profiling, and planning as well as goal setting, including the dynamic pillars. In other words, human capital will be the key construct that needs to be neither observable nor measurable directly, while still affecting all of this performance KPIs or internal controls acting as if they were entangled.

The initial power analysis based on alpha and beta at 0.05 each and a reasonably large ES of 0.6 (as rationalized further) suggests that a moderate R-square of 0.47 could be attained with a sample size as small as 28 units (Exhibit 1). Sensitivity analysis shows that, reducing the effect size to 0.3 calls for a sample size of 68, albeit garnering a meager R-square of 0.20 (Exhibit 2). Since a longitudinal study cannot be run at this stage (primary data would take a prohibitively long time to collect), a pooled data set will only be considered for future research. At the same time, the predictive power may have to be raised at the cost of forgoing some (1-beta) power, with prior significance staying the same and sample size hovering somewhere in-between.

In fact, post hoc analysis suggests a rather divergent sensitivity pattern depending on the effect size. For instance, for ES=0.6, the trade-off between R-square and power suggests values of 0.52 and 0.92 for a sample size of 25 units, respectively. For a larger sample of 30, these diverge with power increased to 0.97 and R-squared shrunk to 0.44. For an ES=0.7, however, the latter being unchanged, while the power totals 0.997. An even larger sample would further boost power immaterially, whereas the R-squared would drop to as low as 0.27. Interestingly, a low ES at 0.3 would preserve the R-squared while still enabling a satisfactory power at 0.83. With that being said, having the sample at a low ES would be a catastrophe for the power-hitting as low as 0.36. Therefore, whatever effect size might prove, a sample of 50 units of observation should suffice for all practical purposes.

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Research Question and Hypotheses

It is still unclear whether the efficiency gap or slack, if any, can adequately be captured or explained by a set of cultural traits, notably by a discipline of proper goal setting and action planning, to name a few.

The rest of diversity parameters, which routinely qualify as the essence of human capital (while taken as its peripheral facets in the present study), will be treated as fudge factors, control variables, or specification related inputs for significance and validity tests. Incidentally, the selfsame explanatory variables capturing diversity (e.g. gender, education, age, and ethnicity) might prove the core source of ethical hazards or manipulable design, which affect robustness and reliability.

The omnibus null hypothesis of insignificance could be stated as follows:

The respective coefficients explained and analyzed in the subsequent sections. The host of alternative hypotheses suggests that at least some of the coefficients are significantly non-zero. In fact, maintaining an omnibus for the alternative case could substantially compromise the power at any level of alpha significance.

Literary Review

The proposed survey of literature represents the analytical scope that has been already established.

One of the primary concerns is the control of the possibility of gender and time management practices appearing intertwined within the context of utilizing human capital. Krings, Nierling, and Pedaci (2010) have demonstrated a differential impact of gender on the work time allocation preferences as well as opportunities. The research found that women who score lower on educational attainment simultaneously settle for more traditional gender roles as part of the corporate culture and labor allocation. This could be one way of showcasing how human capital is endogenized, as well as intertwined with its short-term efficiency impacts alongside the more lasting effects on the corporate culture, thus revealing a recursive pattern of vicious or virtuous circles of opportunity formation.

Bhagwatwar, Bala, and Ramesh (2014) have proposed an alternative insight into outsourcing with respect to in-house IT service management. In fact, the IT unit could be a more apparent example of what will be seen as the fixed cost, which can only be minimized via sourcing solutions at the expense of ushering in extra risks.

Teece (2010) was under the influence of Williamson’s Nobel-winning legacy in institutional economics and industrial organization, with transaction cost determining the nature of a firm and organizational size beyond scale efficiency and fixed cost. On the one hand, deliberation cost can be merged into this category while justifying the choice of the more minimalist design and specifications, as corporate players are presumed to minimize these cost items. In fact, it can be shown that the institutional implications of Williamson’s contribution could extend far beyond the role of macro-level or external culture (i.e. informal social institutions), with the microenvironment trade-offs between risk management and internal controls largely reduced to the deliberation cost issue.

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As far as operations and planning, Lenfle and Loch (2010) have argued that the present-day project management has long strayed off its original intent that was largely about endogenizing the stage list and durations. In other words, good planning and time management have nothing to do with proposing projects whose structure and time horizons are set in stone from the outset. Nor it can be reduced to the ex-post rethinking of these dimensions. In line with and in terms of the approach proposed in the present study, they argue that human capital is largely about learning and improving and endogenously driven time allocation.

The design and sampling part of the project could confront issues of crucial importance, which has to do with the impact of the diversity explanatory variables on the quality of the primary data as well as design validity. For instance, Zijlstra, van der Ark, and Sijtsma (2011) have observed that removing outliers indiscriminately might usher extra bias, which can be mitigated by focusing on the random response outliers.

Liu, Huang, Liu, Chien, Lai, and Huang (2015) have proposed that gender can affect learning in knowledge-intensive setups as a matter of emotional response to interim feedback. It appears that this implication could carry over into the design and sampling setup, with an eye on controlling how the gender dummy is correlated with measurement errors and data quality at large, insofar as an emotive load can interact with the socially garnered perception of the individual response or status, as expected by the interviewee. Similar validity and reliability implications could be suggested for the race or ethnicity variables, in line with Briggs, Briggs, Kothari, Bank, and DeGruy (2015) and Covay, Desimone, Spencer, & Phillips (2015).

Research Methods

Background

Mention was made of constrained optimization and BEP analysis securing the key pillars of candidate formalization. In other words, a formal model based on a set of theoretically stipulated priors will inform further specification and design approaches while determining the expected signs of the effects or the convergent and discriminant validity thresholds.

The details provided further shed the light on the structure and the rationale while showing how the actual functional stretchings might be of a second-order relevance. To begin with, the efficiency gap will be driven by the divergent structural results as suggested by optimization versus BEP. In the former case, the generalized objective function looks as follows:

The first-order condition draws upon a chain-rule decomposition for the price derivative, which is maintained to be exogenous with respect to the equilibrium quantity of a product. Then, the BEP counterpart appears differently:

Both relationships have to be solved for labor allocation L, while Taylor-expanded around some minimum L0 level. At this rate, they can be reconciled as a linear relationship, thus, suggesting an ordinary linear squared (OLS) regression as a starting point. Moreover, since either can best be solved as a standalone reduced-form rather than via SEM, the endogeneity issue is bypassed even in a pooled data setting.

In addition, it will be assumed that time management adapts, or could actually be reduced to at least some of the conventional methods of project management with such notions as the critical path, the crushing function, and slacks applying uniformly thus allowing for meaningful comparison across companies. In other words, this could be one way of mitigating the ambiguity when rendering the responses on action planning and setting the operational goals.

Moreover, it will be presumed that further analysis is largely centered around the so-called variance analysis and managing by exceptions (MbE) as practiced in managerial accounting. In other words, any gaps between the planned versus actual performance can be analyzed depending on how relevant they are from the standpoint of the relative magnitude of impact.

 

Design and Methods: Strengths versus Weaknesses

A formal model underpinning the further econometric testing posits the overall setup, which is anything but a theoretical, with the constructs as well as instruments proving less of an arbitrary choice. All of the structural relationships are well defined, even though they can be approximated to any tolerance as a matter of economizing the deliberation cost while accommodating a particular design.

The flipside of it is that Taylor based approximation might compromise BLUE properties, even though they are induced by the first-order linearity in the first place. For one, omitting the higher-order terms might inflate the residual as if to further compromise data accuracy. On the other hand, these terms would be difficult to interpret for inferential, theorizing, or policy purposes. In any case, convergence might be secured for the delta L normalized to a unit interval. It will be shown later in the text how the latter possibility eliminates the issue of serial correlation or excessive heteroscedasticity in the data while suggesting additional implication for validity and reliability analysis.

Threats to Validity

The left-hand side (LHS), or the dependent variable will be measured as a labor slack, whereas the right-hand side (RHS), or the vector of explanatory variables will be comprised of dummy type indicators. In fact, this generic scheme should for now suffice to depict the validity trade-offs. For one thing, mention was made about the LHS delta L being represented either as levels differential or as a percentage. Irrespective of whether it is measured against some unobservable benchmark or vis-?-vis each other, a convergent validity test will point to a reasonable correlation between the two (or more) options in the LHS dependent variable.

By contrast, irrespective of the actual vector of the RHS explanatory variables (which can be measured in a variety of ways even with respect to the exact same categories), Cronbach’s alpha should point to a high overall correlation of designs or specifications. For instance, these categories can be quantified as binary dummies, Likert scores, or otherwise diverse units, yet the Cronbach alpha should be anywhere between 0.7 and 0.9 for a pilot study. A way of running one would draw upon correlations or VAR-COV matrix.

The exact same matrix, which applies to the LHS and RHS variable for the prior check, can reveal whether the latter category shows reasonable orthogonality, or indeed discriminant validity. In other words, it is desirable to detect that theoretically related or unrelated channels and mechanisms actually prove so. In any event, though, it is desirable to come up with a set of explanatory RHS variables that are uncorrelated to a posteriori regardless of whether they ought to be related ex-ante. In other words, this paradoxical trade-off suggests that prior validity could be at odds with prior BLUE performance, e.g. undermined significance of standalone factors in case of material multicollinearity.

Among other options, validity could further be boosted for the IT-related units of a learning and knowledge-intensive organization by referring to the non-IT lines of business or SBUs as control groups within the same companies are surveyed (Mishra & Baskar, 2010). A respective dummy variable could be added to the specification.

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Optimum Design versus Alternatives

The OLS setup draws upon the presumption that human capital minimizes the absolute value of the labor gap, positing a superior allocation capacity. Simultaneously, human capital stands for adequate action planning and goal setting as well as continual revision. The latter will be aligned with the learning propensity. For now, it is enough to argue that in light of the inverse relationships, the right-hand side explanatory variables are expected to reveal negative signs in the respective slope coefficients:

Notably, providing for a zero intercept should prove an optimal strategy, as there is little point in merging the group of the effect as a fixed aggregate adding up to the residual effects. The use of interactive coefficients will be discussed in line with the power analysis. When it comes to learning, the higher-order facet of human capital, this would pertain to a convergent change in the gap amounting to a favorable impact and a positive (reversed) expected to sign in the explanatory coefficients:

The pooled data setup has gained a random effect that captures time-driven variability while making up for the missing regular intercept or its fixed-effect counterpart.

Defining and Operationalizing Constructs

This section will elaborate on the aforementioned constructs. The grand rationale was to conceive the unobservable human capital as the ultimate construct that could be defined in a variety of ways. For the purpose of modeling, or optimizing the design for that matter, it has been defined in a parsimonious way, while capturing most meaningful relationships. In other words, it relates directly or otherwise, to the labor allocation gap, which is the dependent variable and the vector of dummy type explanatory variables in an inverse or compensatory manner. Both, the action planning and goal setting dummies, are considered the core regressors taking on a value of 1 if their underlying responses rank above sample average, and zero otherwise.

The rest of the dummies can be defined likewise. For instance, the ethnicity dummy could be reworked for ethical and sensitivity purposes as an ethnic diversity indicator taking on a value of 1 in the event the respondent company’s diversity was reported high on a Likert scale while ranking above average in toto. The age dummy could be determined based on the individual companies’ average employee age, followed by a sample average comparison. (In fact, the variability of averages, along with the dummy indicator as group identity, points to the design qualifying as a generalized ANOVA.) The same could hold for education, based on an average of schooling or training years.

Describing the Sample

Power and sensitivity analysis has demonstrated that a total of 50 units of observation suffice with the effective degrees of freedom for a cross-sectional study building on 6 regressors totaling DF=50*6-6=294.

Sampling Method

A random sample will be gathered from a hypothetical population of information-intensive companies. More specifically, a corporate profile allowing an IT unit and top executives to be interviewed would reasonably accommodate the design. In case of low turnout or a large proportion of missing values, the primary dataset will have to be re-sampled. If this, however, occurs, the constructs will reveal low validity, and, thus, minimize manipulable data mining and design sensitivity. For the most part, as pointed out at the outset, the sampling will be facilitated via a Likert scale questionnaire to inform further dummy transforms.

Collecting and Constructing Data

A questionnaire will be administered on a sample of CEOs, from which primary dataset (based on Likert sentiments and unbiased quantitative averages), a further dummy-based output is to be discerned. As far as the dependent labor gap is concerned (which in effect amounts to a crushing time slack based on the hours put in), it is to be inferred by the formal model based on the company financial reports that should be readily available in SEC/EDGAR filings. That said, the CFOs would still have to be interviewed about prices or pricing information. In case this information goes undisclosed or there are too many values missing, power analysis could be put at threat. Therefore, for uniform comparability’s sake, a single demand function could be considered as a generic response subject to exogenously set prices.

Ethical Issues and Participation Hazards

Evidently, information that supplies input data for project management and managerial accounting could be proprietary or prove selective. In fact, the CFOs will unlikely reveal complete financials of relevance, whereas the 10K reports could draw on arbitrary cost allocation conventions rather than actual pricing schemes. One way around the issue could be to overlook the formal LHS section altogether while encouraging the CEOs and CFOs to provide consistent responses on how they feel about their labor allocation gaps on a Likert scale.

Other than that, a low turnout might result from the rest of the dummy input questionnaires, as people might feel reluctant to reveal their educational attainments subject to ageist prejudice. Likewise, the chief executives might be unwilling to report their team quality while claiming they boast superior portfolios of earning assets, if only for stock valuation purposes. Similar moral hazards might plague fund managers’ reports as well, as they normally maintain a style strategy on particular asset or project profiles. Worse yet, analyst following and outside ranking have not shown to be immune to the issue.

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Descriptive Statistics, Inference, and Analysis

Replacing the formally derived delta L as the dependent variable with a biased dummy response might stumble into the residuals being Bernoulli distributed, which is at odds with zero serial, auto-correlation or homoscedasticity. That would call for additional checks into the structure of residual variance, which could in turn border on a specification that is dimensionally minimalist yet rather involved structurally.

The age and education explanatory variables may or may not prove to be correlated or spuriously related, yet their dummy transforms has to remove the majority of multicollinearity. In other words, the model is less likely to exhibit insignificance in the dummy coefficients unless they actually account for little covariance with the delta L. On the other hand, the R-squared might not turn out to be very high for a specification focusing on a singular facet of human capital, which may or may not map into a high ad-hoc model significance overall, insofar as the F-statistic, it depends on both the R-squared and the degrees of freedom or sample size.

This would, in turn, set the p-value, which may reveal greater sensitivity vis-?-vis power analysis to be addressed shortly as compared to a priori alpha significance.

Power Analysis Background

Dummy type and proportion representations routinely embark on excessive t-statistics even under moderate sample sizes. Put simply, the effect size can be presumed high as per the attempted design and specification, which under relatively few regressors means the sample need not be very large for power to be secured at a high level. One the one hand, the added temporal dimension can effectively boost the number of observations, with choosing over the shorter time periods or number of entities toward more degrees of freedom remaining the researcher’s sole discretion when it comes to tossing up over random versus fixed effects. On the other hand, one should end up being unable to afford a full-blown panel to run a sensitivity analysis of power impacts.

For instance, a minor change in the sample size may raise the alpha without affecting the beta. By contrast, a major warp in the dataset, as in the event of low turnout or a sparse matrix of missing values, would urge a post hoc reduction in the power amidst prior significance faring intact.

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Handling Data and Accuracy Check

The CEO and CFO responses will serve as control groups for each other to capture any noisy or systemic divergence. The latter can be controlled more effectively.

Implementing the Design: Issues and Prevention

Outliers of the random responding and item-pair type will be controlled with the Mahalanobis distance (Zijlstra, van der Ark, & Sijtsma, 2011) as well as heteroscedasticity tests. The residuals will again be regressed on a set of the exact same diversity dummies, in order to obtain an accurate account of which factors have systematic structural impacts on heteroscedasticity if any. Apart from compromising the R-squared, the likely covariance of residuals and explanatory variables might render the OLS estimates non-BLUE, more so given the apparent micronumerosity on hand.

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