Global Journal of Business Research

VOLUME 9 NUMBER 2 2015

CONTENTS

Evidence on the Speed of Convergence to Market Efficiency: Evidence from Stock 1Spin-OffsHan-Ching Huang, Yong-Chern Su & Chun-E Shih

The Role of Information Systems in Enhancing the Performance of the Pharmacy Council of Ghana 9Kwabena Obiri-Yeboah, Eliezer Ofori Odei-Lartey & Kenneth Simmons

Transforming Leaders through Cultural Intelligence 23James B. Box, Judith A. Converso & Efosa Osayamwen

Performance of Technical Analysis in Declining Global Markets 41 Jogiyanto Hartono & Dedhy Sulistiawan

Determinants of Internet Corporate Social Responsibility Communication: Evidence from France 53Laetitia Pozniak & Perrine Ferauge

The Nature and Concept of Accountability: A Case Study of Three Entities in Fiji 65Ezaaz Hasan, Anjani Mala & Glen Finau

The Value Creation Model of Patent Market Intermediaries 75Jin Bih-Huang & Chun-Yu Chu

Does Investment Experience Influence Fund Investors’ Perceived Value and Purchase Intention? 87Ya-Hui Wang

What Makes Offline Word-of-Mouth More Influential Than Online Word-of-Mouth? 95Ahmet Bayraktar & Emine Erdogan

Global Journal of Business Research Vol. 9, No. 2, 2015, pp. 1-8 ISSN: 1931-0277 (print) ISSN: 2157-0191 (online)

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EVIDENCE ON THE SPEED OF CONVERGENCE TO MARKET EFFICIENCY: EVIDENCE FROM STOCK

SPIN-OFFS Han-Ching Huang, Chung Yuan Christian University

Yong-Chern Su, National Taiwan University Chun-E Shih, National Taiwan University

ABSTRACT

We use order imbalance to investigate dynamic relations among intraday return, volatility and order imbalance of stock spinoffs. A GARCH model is employed to examine whether the larger order imbalance is associated with larger stock price volatility. We do not find a significant positive relation between them, which implies that market makers do a successful job of mitigating volatility on spinoffs. Moreover, we develop imbalance-based trading strategies and find they can beat open-to-close returns only in the 5-minutes time interval. JEL: G14, G34 KEYWORDS: Spin-off, Order Imbalance, Market Efficiency, Volatility INTRODUCTION

n recent decades, many diversified firms have gone back to basics by focusing on their core business. A spinoff, defined as a pro-rata distribution of a share of the subsidiary to the original parent’s stockholders, is a common way to sharpen focus. The majority of studies document significant positive

abnormal stock returns around spinoff announcements (See Cusatis et al. 1993; Krishnaswami and Subramaniam, 1999; Mulherin and Boone, 2000; Huson and MacKinnon, 2003; Maxwell and Rao, 2003; and Son and Crabtree, 2011). Many papers also present evidence that spinoffs increase long-run shareholder value (e.g. Burch and Nanda, 2003; Ahn and Denis, 2004; Kim et al., 2008; Lin and Yung, 2013; Jordan et al., 2014). The above studies use daily data to explore the abnormal returns. To our knowledge, no existing study that explores the behavior of market microstructure on the announcement day of a spinoff. In this study, we use intraday transactions to examine convergence in spin-off market efficiency. We explore whether lagged stock order imbalances could be used to predict stock returns. According to Charoenwong et al. (2008), prior to the spinoff announcement, trading could be mainly initiated by insiders. Nonetheless, trading on the announcement day could be mainly initiated by uninformed traders, who could only trade the stocks after hearing the announcement day news. We employ a time-varying GARCH model to examine whether larger stock price volatility is positively associated with larger order imbalance. We develop an imbalance-based trading strategy, which is to buy the stock at the ask price just when the positive imbalance appears, and to sell the stock once the negative imbalance appears. This paper makes several contributions. First, on the announcement day of the spin-off, market makers can mitigate volatility from discretionary trades through inventory adjustments. Second, we investigate the relationship between order imbalances and returns as we explore the intraday dynamics that are essential in

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the convergence process of the spin-off announcement. The remainder of the paper is organized as follows. In the next section, we present a literature review. The following section provide a discussion of the data and methodology used. Next, we present the study results and close with some concluding comments. LITERATURE REVIEW The literature presents several explanations of the value gains to spinoffs (short-run stock abnormal return or long-run shareholder value). First, parent firms divest unrelated divisions to sharpen their core business competence (Daley et al. 1997; Desai and Jain, 1999). Second, the management team of the parent enjoys increasing managerial efficiency, reducing potential misallocation of investment, improving operating performance, and eliminating negative synergies to mitigate the information asymmetry between managers and investors by allowing more accurate estimation of firm value (Schipper and Smith, 1983; Ahn and Denis, 2004). Third, the wealth gains associated with spinoffs result from the correction of a prior mistake, which was an unprofitable earlier acquisition. A spinoff represents the undoing of that unwise takeover (Allen et al., 1995). In addition, transfer of wealth from bondholders to shareholders (Veld and Veld-Merkoulova, 2004; Veld and Veld-Merkoulova, 2008), relaxing tax and regulatory burdens (Schipper and Smith, 1983), facilitation of a merger or takeover (Cusatis et al., 1993) and sending positive signals to the stock market (Kunz and Rosa-Majhensek, 2008) also explain gains to parent firms following spinoffs. If someone is capable of earning profit in spinoff, it implies that the spinoff market is not efficient enough to respond the arrival of relevant new information. Nonetheless, as all investors engage in diversified investment behavior, the market will converge toward efficiency gradually. How does the market converge to efficiency? Chordia et al. (2005) interpret convergence based on individual actions. First, order imbalances arise from traders who demand immediacy for liquidity or informational needs. These order imbalances are positively auto-correlated, suggesting that traders are either herding or spreading their orders over time, or both. Second, NYSE specialists react to initial order imbalances by altering quotes away from fundamental value in an effort to control inventory. Finally, outside arbitragers intervene to add market-making capacity by performing countervailing trades in the opposite direction. This arbitrage activity takes at least a few minutes since arbitragers must ascertain whether or not there is new relevant information regarding values. Chordia et al. (2005) indicate that efficiency does not happen immediately since order imbalances can predict future returns over very short intervals. They find it takes more than five minutes but less than sixty minutes for the market to achieve weak-form efficiency. Visaltanachoti and Yang (2010) also find that, on average, it takes 30-60 minutes for a foreign stock listed on the NYSE to achieve market efficiency. For a comparable US stock, it takes only 10-15 minutes. Moreover, Chordia et al. (2005) report that there is little evidence of unconditional serial dependence on returns since no t-statistic exceeds 2.0 in absolute value and thirteen of the fifteen t-statistics are less than 1.0 in absolute value. This suggests that these stocks conform well to weak-form efficiency; that is to say, using only the past history of returns, there is little, if any, predictability of future returns even over intervals as short as five minutes. DATA AND METHODOLOGY We identify spinoffs from Securities Data Corporation (SDC). We use Trades and Automated Quotations (TAQ) to obtain intraday transactions that include bid and ask prices and trading prices as well as trading size in consolidate trades database from 9:30 AM to 4:00 PM on announcement dates of spinoffs. We remove the beginning of day observations that include lagged terms from the previous trading day to avoid generating spurious results. Samples range from January 1, 1994 through December 31, 2005 because NYSE TAQ initiated intraday dataset in 1994. We collect stock prices and outstanding shares in announcement years from the Center for Research in Security Prices (CRSP). Seventy-three firms are included in our sample.

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We apply Lee and Ready’s (1991) algorithm on each transaction in 5-minute, 10-minute and 15-minute time intervals. The unreported results show that the mean return is -0.21%, with a median of 0% and standard deviation is 3.44%. The skewness of daily returns is 0.1654 and the kurtosis is 7.4787. These figures imply that the distribution is positively skewed and leptokurtic. Average market capitalization of the sample is 23.6350 billion and the median is 5.4059 billion. We examine the regression of return-order imbalance relation as follow:

Rt = α + εt εt |Ωt−1~N(0, ht) ht = A1 + B1ht−1 + C1εt−12 + D1OIt (1)

where Rt is the return in period t, defined as (Pt- Pt-1)/Pt-1, OIt is the explanatory variable, order imbalance, εt equals the residual of the stock return in period t, ht is the conditional variance in the period t, and Ω t-1 is the information set in period t-1. Chordia and Subrahmanyam (2004) document a positive relation between current return and current order imbalances and a negative relation between current return and lagged order imbalance after controlling for the current imbalance because of “information over-weighting” from market makers. We expect a positively predictive power between return and lagged order imbalances in spinoffs. After controlling for the current imbalance, we expect that a positive sign of contemporaneous imbalances and the positive relation between lagged imbalance and returns disappears. To examine dynamic volatility-order imbalance in convergence, we employ a time-varying GARCH model as follows:

Rt = α + εt εt |Ωt−1~N(0, ht) ht = A1 + B1ht−1 + C1εt−12 + D1OIt (2)

where Rt is the return in period t, defined as (Pt- Pt-1)/Pt-1, OIt is the explanatory variable, order imbalance, εt equals the residual of the stock return in period t, ht is the conditional variance in the period t, and Ω t-1 is the information set in period t-1. Dynamic volatility-order imbalance relation is another focus in our study. Intuitively, the higher volatility is positively associated with the higher order imbalance. A spinoff is not an exception. RESULTS AND DISCUSSION Table 1 shows the positive and significant coefficients on lag one order imbalance under time intervals of 5-minute, 10-minute and 15-minute. At the 5% significance level, the positively and significant percentages for lagged-one imbalances are 4.11%, 5.48%, and 6.85%, respectively. This finding implies an efficient spinoff market on convergence. Previous studies argue a positive abnormal return for parent firms at spinoff announcements (Schipper and Smith, 1983). When firms announce spinoffs, information spreads out during convergence. Discretionary traders actively split their large orders at announcement. To accommodate large imbalances from discretionary investors, market makers raise inventory levels to mitigate volatility at the announcement. Market makers successfully use sufficient inventory to conduct countervailing transaction against informed traders at spinoff announcements. In Table 2, we find 9.59%, 8.22%, and 10.96% of lagged-one imbalance are negatively significant under different time interval of 5 minutes, 10 minutes and 15 minutes at 5% significance level and the average lag-one coefficients are positive. We argue that market makers, inheriting a responsibility to mitigate volatility at a spinoff announcement, gradually increase imperceptible bid and ask prices with large positive

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order imbalance pressure within a 5 and 10 minutes period since discretionary traders keep placing large orders. Nonetheless, perceiving decay of large imbalances from discretionary traders, market makers start to lower bid-ask spreads. The result shows a negative return-lagged one order imbalance relation within a 15-minute time interval. Thus, the 15-minute interval is the best interval for market makers to mitigate volatility. Table 1: Unconditional Lagged Return-Order Imbalance OLS Relation

Average Coefficient Positive Positive and Significant Negative and Significant Panel A: 5-minute Interval OIt-1 3.1358 58.90% 4.11% 6.85% OIt-2 -3.3657 43.84% 6.85% 8.22% OIt-3 3.4476 57.53% 9.59% 6.85% OIt-4 -0.8808 46.58% 2.74% 1.37% OIt-5 1.0839 45.21% 1.37% 6.85% Panel B: 10-minute Interval OIt-1 -2.3254 36.99% 5.48% 6.85% OIt-2 5.4016 50.68% 8.22% 1.37% OIt-3 -6.5821 28.77% 4.11% 5.48% OIt-4 -4.3265 38.36% 5.48% 2.74% OIt-5 0.6592 36.99% 1.37% 8.22% Panel C: 15-minute Interval OIt-1 4.6263 50.68% 6.85% 1.37% OIt-2 -6.1272 36.99% 2.74% 9.59% OIt-3 -2.7114 39.73% 1.37% 2.74% OIt-4 -1.6110 39.73% 1.37% 2.74% OIt-5 -3.7464 46.58% 1.37% 1.37%

This table shows regression estimates of the equation. Rt=α0 + α1OIt-1+α2 OIt-2+α3OIt-3+α4OIt-4+α5OIt-5+εt, where Rt is the current stock return of the individual stock, and OIt is lagged order imbalance at time t for each individual stock. Panels A, B and C present the results in 5, 10 and 15 minute interval respectively. The average coefficients are multiplied by 109. *, **,*** indicate significance at the 10, 5 and 1 percent levels respectively. “Significant” denotes significance at the 5% level. Table 2: Conditional Contemporaneous Return-Order Imbalance OLS Relation

Average Coefficient Positive Positive and Significant Negative and Significant Panel A: 5-minute Interval OIt 13.4858 94.52% 67.12% 0.00% OIt-1 1.2657 53.42% 4.11% 9.59% OIt-2 -3.1376 38.36% 6.85% 10.96% OIt-3 3.5498 54.79% 6.85% 5.48% OIt-4 -1.1139 42.47% 5.48% 2.74% Panel B: 10-minute Interval OIt 16.7854 95.89% 45.21% 0.00% OIt-1 1.3831 46.58% 4.11% 8.22% OIt-2 3.9911 53.42% 8.22% 0.00% OIt-3 -3.8365 35.62% 1.37% 8.22% OIt-4 -2.2692 43.84% 8.22% 1.37% Panel C: 15-minute Interval OIt 14.6301 90.41% 39.73% 0.00% OIt-1 -3.7516 38.36% 9.59% 10.96% OIt-2 -5.0414 34.25% 1.37% 6.85% OIt-3 -2.4110 41.10% 0.00% 5.48% OIt-4 0.2419 47.95% 1.37% 1.37%

This table shows the regression estimates of the equation. Rt=α0 + α1OIt+α2 OIt-1+α3OIt-2+α4OIt-3+α5OIt-4+εt where Rt is the current stock return of the individual stock, and OIt is lagged order imbalance at time t for each individual stock. Panels A, B and C present the results in 5, 10 and 15 minute interval respectively. The average coefficients are multiplied by 109. *, **,*** indicate significance at the 10, 5 and 1 percent levels respectively. “Significant” denotes significance at the 5% level.

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Table 3 exhibits a dynamic volatility-order imbalance relation from a time-varying GARCH model. We observe that either the percentage of significantly positive or negative coefficients is less than 5% for all significance levels in three different time intervals. The empirical results reject a significantly positive relationship between volatility and order imbalance on convergence. Apparently, market makers have done a successful job in mitigating volatility from large imbalances. Market makers are reluctant to adjust bid-ask spreads to accommodate discretionary orders. We find that market makers have good power to stabilize the market at spinoffs. Table 3: Dynamic Volatility-Order Imbalance GARCH (1,1) Relation

Average Coefficient Percent Positive and Significant Percent Negative and Significant 5-min interval -67.8 4.11% 0.00% 10-min interval -0.73 1.37% 0.00% 15-min interval 65.7 2.74% 1.37%

This table shows regression estimates of the equation: Rt = α+ εt ,εt︱Ωt-1 ~ N(0, ht), ht = A + Bht-1 + Cεt-12 +γ*OIt where Rt is the return in period t, and is defined as ln(Pt/Pt-1), OIt is the explanatory variable, order imbalance, γ is the coefficient describing the impact of order imbalance on stock volatility, εt is the residual value of the stock return in period t, Ωt-1 is the information set in period t-1. All coefficients are multiplied by 104. Based on previous empirical results, we develop an intraday imbalance-based trading strategy to beat the market. We trim 90% of small order imbalances in each day under three time intervals because larger imbalances have a more substantial impact on returns. For each stock, we buy the share at the ask price just when the positive imbalance appears, and sell the share once the negative imbalance appears. The trading strategies are built on quote price or trade price. In Panel A of Table 4, we find that returns of imbalance-based trading strategy for 5-, 10- and 15-minute time intervals are -0.74%, -1.04%, and -0.87%, respectively. The returns of imbalance-based trading strategy on quote price are significantly negative at the 1 % significant level. We use paired t-tests to examine whether the return from a trading strategy is higher than the open-to-close return on the announcement day of spinoff. Panel B shows the return of trading strategies for the 10-minute interval is significantly lower than an open-to-close return. Panel C shows that there is no significant difference among three different time intervals at the 10 % significant level. Table 4: Returns of Imbalance-Based Trading Strategy Based on Quote Price

Panel A: Returns Compared with Zero P-value 5-min return strategy 0.0013 10-min return strategy 0.0001 15-min return strategy 0.0001 Panel B: Returns Compared with Returns of Buy-and-hold Strategy

Mean Original Open-to-close Return P-value 5-min return strategy -0.0074 -0.0021 0.0530 10-min return strategy -0.0104 -0.0021 0.0093 15-min return strategy -0.0087 -0.0021 0.2602 Panel C: Differences in Returns among the Three Intervals P-value 5-min Return 10-min Return 15-min Return 10-min return 0.1508 15-min return 0.5932 0.1897

This table shows trading profits under the quoted price. For each stock, we buy the share at the ask price just when the positive imbalance appears, and sell the share once the negative imbalance appears. Panel A presents the p-values to be used to examine whether the return of imbalance-based trading strategy is positive. Panel B shows the p-values to be used to explore whether the return of imbalance-based trading strategy is higher than open-to-close return on spinoffs. Panel C exhibits the p-values to be used to examine whether there is no difference in return of the strategy among three different time intervals. Table 5 shows the returns of an imbalance-based trading strategy on the basis of trade price. Panel A shows the return for 5-minute intervals is 0.76%, which is significantly positive and higher than the return on a

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buy-and-hold strategy at the 1 % significant level. However, returns for 10- and 15-minute intervals are not significant at the 10 % significant level. Panel B, shows the return of imbalance-based trading strategy for 5-minute intervals successfully beat the market at the 1 % significant level. Panel C shows there are significant differences between 5 and 10 minutes as well as 5 and 15 minutes at the 1 % significant level. CONCLUDING COMMENTS Previous studies argue that information asymmetry problems between managers and investors is alleviated or exacerbated after the spinoff. They examine whether the spinoff event provides any information to lead traders to earn abnormal excess return around the announcement periods. However, if someone can earn profit at spinoff, it implies the spinoff market is not efficient enough to respond to the arrival of new information. Therefore, we use order imbalance as an indicator of insiders trading information in this study. We examine dynamic relationships between order imbalance, volatility and return of spinoffs on the announcement date. We also develop an imbalance-based trading strategy to test convergence to market efficiency of spinoffs. We examine the unconditional return-order imbalance regression relation based on Chordia and Subrahmanyam (2004). Our empirical results provide an insignificant positive relation between current stock returns and lagged order imbalances, which is inconsistent with Chordia and Subrahmanyam (2004). We investigate conditional contemporaneous return-order imbalance relation. We document a positive contemporaneous return-order imbalance relation for three different time intervals, which is consistent with Chordia and Subrahmanyam (2004). Further, we use a time-varying GARCH model to examine whether the larger order imbalance is positively associated with greater stock price volatility. Our empirical study indicates no strong positive relationship between them. We believe that market makers have good power to stabilize the market through inventory adjustments. Finally, we develop an imbalance-based trading strategy on the basis of quote and trading prices. Only returns on trades priced in the 5-minute interval could beat open-to-close returns. Thus, the spinoff market is not efficient in the 5-minute interval. This paper focuses on the impact of stock order imbalances on stock returns of spinoffs. Because the investors also trade options of underlying stocks, future research should examine the influence of option order imbalances on stock returns of spinoffs. Table 5: The Returns of Imbalance-Based Trading Strategy Based on Trade Price

Panel A: Returns Compared with Zero P-value 5-min Return of strategy 0.0027 10-min Return of strategy 0.3053 15-min Return of strategy 0.4930 Panel B: Returns Compared with Returns of buy-and-hold Strategy

Mean Original open-to-close Return P-value 5-min return strategy 0.0076 -0.0021 0.0006 10-min return strategy 0.0007 -0.0021 0.1990 15-min return strategy 0.0002 -0.0021 0.2602 Panel C: Differences in Returns among the Three Intervals P-value 5-min Return 10-min Return 15-min Return 10-min return 0.0013 15-min return 0.0013 0.4799

This table shows trading profits under the trade price. For each stock, we buy the share at the ask price just when a positive imbalance appears, and sell it once a negative imbalance appears. Panel A presents p-values to examine whether the return of imbalance-based trading strategy is positive. Panel B shows the p-values to explore whether the return of imbalance-based trading strategy is higher than open-to-close return on spinoffs. Panel C exhibits the p-values to examine whether there is no difference in return of the strategy among three different time intervals.

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REFERENCES Ahn, S. & Denis, D.J. (2004) “Internal Capital Markets and Investment Policy: Evidence from Corporate Spinoffs.” Journal of Financial Economics, vol. 71 (3, March), p. 489-516. Allen, J.W., Lummer, S.L., McConnell, J.J. & Reed, D.K. (1995) “Can Takeover Losses Explain Spin-Off Gains.” Journal of Financial & Quantitative Analysis, vol. 30 (4, December), p. 465-485. Burch, T.R. & Nanda,V. (2003) “Divisional Diversity and the Conglomerate Discount: Evidence from Spinoffs.” Journal of Financial Economics, vol. 70 (1, October), p. 69-98. Charoenwong, C., Ding, D.K. & Pan, J. (2008) “Asymmetric Information and Conglomerate Discount: Evidence from Spinoffs.” Working Paper, Nanyang Technological University. Chordia, T., Roll, R. & Subrahmanyam, A. (2005) “Evidence on The Speed of Convergence to Market Efficiency.” Journal of Financial Economics, vol. 76 (2, May), p. 271-292. Chordia, T. & Subrahmanyam, A. (2004) “Order Imbalance and Individual Stock Returns: Theory and Evidence.” Journal of Financial Economics, vol. 72(3, June), p. 485-518. Cusatis, P.J., Miles, J.A, & Woolridge, J.R. (1993) “Restructuring Through Spin-Offs: The Stock Market Evidence.” Journal of Financial Economics, vol. 33 (3, June), p. 293-311. Daley, L., Mehrotra, V. & Sivakumar, R. (1997) “Corporate Focus and Value Creation Evidence from Spin-Offs.” Journal of Financial Economics, vol. 45 (2), p. 257-281. Desai, H. & Jain, P.C. (1999) “Firm Performance and Focus: Long-Run Stock Market Performance Following Spin-Offs.” Journal of Financial Economics, vol. 54 (1), p. 75-101. Huson, M.R. & MacKinnon, G. (2003) “Corporate Spinoffs and Information Asymmetry between Investors.” Journal of Corporate Finance, vol. 9(4, September), p. 481-503. Jordan, B.D., Liu, M.H. & Wu, Q. (2014) “Corporate Spin-offs and Innovation.” Working Paper, University of Kentucky. Kim,S., Klein A. & Rosenfeld, J. (2008) “Return Performance Surrounding Reverse Stock Splits: Can Investors Profit.” Financial Management, vol. 37 (2, Summer), p. 173- 192. Krishnaswami, S. & Subramaniam,V. (1999) “Information Asymmetry, Valuation, and the Corporate Spin-Off Decision.” Journal of Financial Economics, vol. 53 (1, July), p. 73-112. Kunz, R.M. & Rosa-Majhensek, S. (2008) “Stock Splits in Switzerland: To Signal or not to Signal”. Financial Management, vol. 37(2, Summer), p. 193-226. Lee, C.M.C., & Ready, M.J. (1991) “Inferring Trade Direction from Intraday Data.” Journal of Finance, vol. 46(2, June), p. 733-746. Lin, Y.C. & Yung, K. (2013) “Motives for Corporate Spinoffs: Evidence from Ex-Ante Misvaluation.” Working Paper, Missouri University of Science and Technology. Man, K. & Chen, C. (2009) “On a Stepwise Hypotheses Testing Procedure and Information Criterion in Identifying Dynamic Relations between Time Series.” Journal of Data Science, vol. 7(2), p. 139-159.

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HC. Huang et al | GJBR ♦ Vol. 9 ♦ No. 2 ♦ 2015

Maxwell, W.F. & Rao, R.P. (2003) “Do Spin-Offs Expropriate Wealth from Bondholders.” The Journal of Finance, vol. 58(5, October), p. 2087-2108. Mulherin, J.H. & Boone, A.L. (2000) “Comparing Acquisitions and Divestitures.” Journal of Corporate Finance, vol. 6(2, July), p. 117-139. Schipper K. & Smith A. (1983) “Effects of Recontracting on Shareholder Wealth: The Case of Voluntary Spin-offs.” Journal of Financial Economics, vol. 12 (4, December), p. 437-467. Son M. & Crabtree, A.D. (2011) “Earnings Announcement Timing and Analyst Following.” Journal of Accounting, Auditing & Finance, vol. 26 (2, April), p. 443-468. Veld C. & Veld-Merkoulova, Y.V. (2004) “Do Spin-offs Really Create Value? The European Case.” Journal of banking & Finance, vol. 28 (5, May), p. 1111-1135. Veld C. & Veld-Merkoulova, Y.V. (2008) “The Risk Perceptions of Individual Investors” Journal of Economic Psychology, vol. 29 (2, April), p. 226-252. Visaltanachoti N. & Yang T. (2010) “Speed of Convergence to Market Efficiency for NYSE-listed Foreign Stocks” Journal of Banking & Finance, vol. 34 (3, March), p. 594-605. BIOGRAPHY Han-Ching Huang is Associate Professor of Finance and Director of International Master of Business Administration at the Chung Yuan Christian University. His research appears in journals such as Journal of Banking and Finance, Pacific Basin Finance Journal, Investment Analysts Journal, and Applied Economics. He can be reached at Chung Yuan Christian University, 200, Chung Pei Road, Chung Li District, Taoyuan City, Taiwan, 32023, samprass@cycu.edu.tw. Yong-Chern Su is Professor of Finance at National Taiwan University. His research appears in journals such as Journal of Banking and Finance, Pacific Basin Finance Journal, Investment Analysts Journal, and Applied Economics. He can be reached at National Taiwan University, 50 Lane 144 Sec. 4, Keelung Road, Taipei, Taiwan, ycsu@ntu.edu.tw. Chun-E Shih is Master of Finance at National Taiwan University. She can be reached at National Taiwan University, 50 Lane 144 Sec. 4, Keelung Road, Taipei, Taiwan, r96723052@ntu.edu.tw.

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Global Journal of Business Research Vol. 9, No. 2, 2015, pp. 9-21 ISSN: 1931-0277 (print) ISSN: 2157-0191 (online)

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THE ROLE OF INFORMATION SYSTEMS IN ENHANCING THE PERFORMANCE OF THE

PHARMACY COUNCIL OF GHANA Kwabena Obiri-Yeboah, Kwame Nkrumah University of Science and Technology, Ghana

Eliezer Ofori Odei-Lartey, Kintampo Health Research Centre, Ghana Kenneth Simmons, Ashanti Regional Pharmacy Council, Ghana

ABSTRACT

Information systems present great potential for public institutions in developing countries to reengineer their processes, meet the current global trends and improve performance. This study describes the role of information systems in enhancing the performance of the Pharmacy Council of Ghana. Primary data was collected through semi-structured questionnaires. Questionnaires were administered to two groups; pharmacy operators that have been licensed by the Pharmacy Council and staffs from all the eight offices of the Pharmacy Council of Ghana. Presented in the analyses are the current information systems environment of the Pharmacy Council; the value of information systems as perceived by the Pharmacy Council staff and pharmacy operators; and respondent views on challenges facing the use of information system at the Pharmacy Council. Results from this study suggest that the Pharmacy Council has potential for computerization. Results further suggest that level of experience and exposure to computerization has significant level of influence on perceptions about computerization. Results also suggest major concerns about the availability of dedicated expertise to manage and maintain an information infrastructure at the Pharmacy Council. Results propose that the Pharmacy Council should examine all frequently recurring services and formulate strategies for computerization. JEL: M15, O33 KEYWORDS: Pharmacy Operators, Information System, Information and Communication Technology, Client Service, Public Institutions INTRODUCTION

nstitutions would ideally want to take decisions which are accurate and timely (Huber, 1990). Coming out with decisions largely depend on the information that is available and when it is made available (O'Reilly, 1982). Thus, data must be processed accurately and timely and must be easily accessible

when needed. Information systems are actively adapted to process data. The benefits inuring from making good decisions as a result of the proper implementation of an information system (I.S.) could be a competitive advantage for an institution (Porter & Millar, 1985). The business value of I.S. has received considerable interest from the business community due to the increased realization that it potentially improves productivity and has significant impact on business performance (Brynjolfsson, 1993; Brynjolfsson & Hitt, 2000; Davenport & Short, 2003). For most organisations information systems has changed the way in which they conduct business. Perception of information systems as a strategic way of enhancing or improving the efficiency of businesses is not exclusive to developed countries(Castells & Development, 1999; Diagnostic, Foster, & Briceño-Garmendia, 2010). The Government of Ghana (GoG) also recognizes the importance of information technology to improve the service delivery of public institutions (Gyamfi, 2005; Heeks, 2002a, 2002b; Martey, 2004). To this effect, the GoG developed a policy document for the country, the Ghana ICT for Accelerated development (ICT4AD), in 2003. In this policy

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document government seeks to deploy information systems to help improve service delivery by civil and public institutions to the public (Dzidonu, 2003; Policy, 2003). Although the use of Information systems is increasing in Ghana, it does not mean that all organisations especially public institutions are able to derive value from it. But regardless of this, organisations continue investing in IS. It can be also seen that most of these organisations have computers and other forms of information technology but how are these being used within the organisation to enhance their operations? How is information systems and IT being implemented in public organisations to assist in data gathering and processing within the organisations to help them achieve these objectives? The Government of Ghana acknowledges this in its policy document that the mere deployment of ICTs within public sector organizations and institutions does not necessary translates into improvements in productivity, efficiency and service delivery which collectively could impact on the overall developmental process of the country. The GoG suggests that, the deployment of ICTs within public institutions and business organizations and entities will have little or no impact on the nation’s development process if not accompanied by a number of organizational and procedural changes as well as changes in attitude to work and work ethics (ICT4AD pg. 41). So the question here is, are public organisations moving in line with the government of Ghana’s policy objectives as outlined for them in the ICT4AD? Another reason why organisations may not derive the business value of information systems in organisations is embedded in the phenomenon that information systems create business value indirectly but create business costs directly, making the value of information systems and the benefits thereof difficult for organizations to perceive. The Pharmacy Council as a government institution has computers and other forms of IT equipment at their disposal. They could be used to enhance the service delivery of the PC by improving on their service delivery capacity. This study explored the prospects and challenges of using information systems in government institutions in Ghana. In this study, we assessed the role of IS in enhancing the performance of the PC of Ghana in managing client information by identifying the current IS in operation, determining the human resource capacity available at the Pharmacy Council for operating information systems and soliciting views on the perceived value and challenges of IS from both staff and clients of the PC. This paper has been organised into six major sections. The first section is an introduction to the study. The second is literature review relevant to the discussion. The third section describes the methods used in the study. The fourth section elucidates results and discussions from the study. The fifth section concludes the study. The sixth section provides references to literature reviewed and the final section is a biography of the authors of this document. LITERATURE REVIEW An Information System (I.S.) could be described as the processes of collecting and analysing data in a function area using electronic tools, applications programming and implementation, data mining, and decision support systems (Alter, 1998; Ruiz, Mejia, & Kaplan, 2003). In a broader sense, the term Information Systems is used not to refer only to the information and communication technology that an organization uses, but also to the way in which people interact with this technology in support of processes. Within an organisational context I.S. commonly aims to support operations, management and decision making (Melville, Kraemer, & Gurbaxani, 2004). The time and process of gathering data in an organisation and transforming it into information is vital for making important decisions. This is because accurate and timely information is necessary to help organisation meet their set objectives (Daft & Lengel, 1986; Day, 1994; Lee, Strong, Kahn, & Wang, 2002; Naumann & Rolker, 2000). Information systems generally are classified into five categories: office information systems, transaction processing systems, management information systems, decision support systems, and expert systems (Joseph, 2013). However, it is difficult to classify a system as belonging exclusively to one of the five information system types mentioned owing to the reason that organizations increasingly are consolidating their information needs into a single,

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integrated information system. The impact of information systems on the performance of end users and the relationship between information systems and performance and productivity is of great interest to many researchers (Davis, 1993; Delone & McLean, 2003; Igbaria & Tan, 1997; Ravichandran & Lertwongsatien, 2005). While various studies have identified key factors for the success of information systems, other studies have been undertaken to measure the impact of I.S. on management performance of business organizations using different key performance indicators. Some studies have investigated the effects of a specific system performance of the user. These studies have established very important conclusions on the use of the system, system quality and reliability. DATA AND METHODOLOGY The study was conducted among staff in all ten (10) regions of the Pharmacy Council of Ghana. The study also included one hundred and thirty six (136) conveniently sampled from an alphabetically order list of four hundred and twenty (420) pharmacies registered with the Pharmacy Council in the Ashanti Region. With regards to the selection of pharmacy operators, factors of proximity, availability and willingness to participate were key determinants. Also, the sample excluded other pharmaceutical service providers such as licensed over the counter medicine sellers. This may place limitations on the extent to which this study can be generalized within the context of the Ghanaian pharmaceutical service providers. However, since activities do not vary within the organization there may not be wide deviations from what pertains with other service providers. Primary data was collected through structured interviews. Questionnaires were designed for this purpose with with a mix of closed-ended and open-ended survey questions. Reliability and validity of questions were assessed by repeating some questions. Questionnaires were interviewer-administered to staff of the pharmacy council on one hand, and managers and owners of the pharmacies on another. Microsoft Excel Spreadsheet 2007 was used to analyse frequencies and percentages of closed end responses. Regression and logistic regression analysis were performed with STATA 12. Open-ended qualitative responses were through data reduction and conclusion creation. RESULTS AND DISCUSSION Socio-Demographics Summary statistics on the socio-demographic characteristics of respondents from the Pharmacy Council staff are presented in Table 1. The summary statistics show the distribution of the respondents by sex, age grouping, regional office, department, position, level of computer knowledge/experience and access to use a computer at the department. Also presented are the percentage proportions for the distributions, relative to the total number of respondents. The age distributions are presented in six groupings, each with a ten year range up till age sixty and above. Results from Table 1 indicate that majority (67.74%) of staff respondents are males. This suggests that the number of male staff is more than twice the number of female staff at the Pharmacy Council. Results on the age distribution of staff respondents indicate that the ages of most of the staff respondents range from thirty (30) to forty (59) years. The age distribution of respondents from the Pharmacy Council staff reflects majority of the workforce fall within the active age of workers in Ghana. The distribution of staff respondents by the regional offices where they work, as presented in Table 1, indicate that over fifty per cent of staffs interviewed are located in the Greater Accra region. The very high number of responses from the Greater Accra region was because this region has two Pharmacy Council offices; the Greater Accra regional office and the head office. Responses from both the Greater Accra regional office and the head office were combined. With regards to distribution by departments, (49.11%) of the respondents were at the inspectorate department. The twelve staffs under the inspectorate department are distributed among the regions. All other departments however are at the head office of the Pharmacy Council. Results on the position/rank of staff also indicate that (47.83%) of the respondents held managerial positions. The number of managers interviewed was high because almost all managers at each regional office were respondents.

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Table 1: Socio-Demographic Characteristics of Pharmacy Council Staff

Panel A: Sex Distribution Staff of the Pharmacy Council n % Male 21 67.74 Female 10 32.26 Missing 1 3.13 Panel B: Age Grouping Staff of the Pharmacy Council

n % 20-29 years 3 9.68 30-39 years 12 38.71 40-49 years 9 29.03 50-59 years 7 22.58 60 years and above 0 0.00 Missing 1 3.13 Panel C: Distribution of by Region Staff of the Pharmacy Council (N=23)

n % Eastern 4 12.90 Ashanti 4 12.90 Western 2 6.45 Northern 1 3.23 Volta 3 9.68 Greater Accra 17 54.84 Missing 1 3.13 Panel D: Distribution by Department Staff of the Pharmacy Council (N=23)

n % Inspectorate 14 49.11 PPME 4 14.29 MIS&P 3 10.71 R & L 2 6.25 Accounts 4 14.29 ETD 1 3.57 Missing 4 12.50 Panel E: Distribution of Staff by Position Staff of the Pharmacy Council (N=23)

n % Accountant 4 17.39 Inspecting Pharmacist 2 8.70 Manager 11 47.83 Pharmacy Intern 1 4.35 Procurement Officer 1 4.35 Secretary 4 17.39

Table 1 shows the socio-demographic characteristics of the Pharmacy Council staff interviewed. Panel A shows the sex distribution of the respondents. Panel B shows the age distribution of respondents in years. Panel C shows their distribution by the region they work. Panel D shows their distribution by the department the work for. Panel E shows their distribution by staff role/position. The last row in each panel represents the missing values for that observation. The first column in each panel shows the socio-demographic variables observed. The figures in the columns labeled ‘n’ for each panel show the observations for each response. The figures in the columns labeled ‘%’ for each panel show the proportions of each observation in percentage. The total number of respondents (X) for each study area is reported as (N=X) at the header rows for each panel. Other staffs holding different positions are mostly from the head office. Responses were received from one hundred and thirty-six (136) pharmacy operators. In this section, summary statistics on the socio-demographic characteristics of respondents from the pharmacy operators are presented in Table 2. The summary statistics show the distribution of the respondents by sex, age grouping, educational level and ownership status. Also presented are the percentage proportions for the distributions, relative to the total number of respondents.

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Table 2: Socio-Demographic Characteristics of Pharmacy Operators

Panel A: Sex Distribution Pharmacy Operators (N=136) n %

Male 108 79.41 Female 27 19.85 Missing 1 0.74 Panel B: Age Group Pharmacy Operators (N=136)

n % 20-29 21 15.44 30-39 41 30.15 40-49 40 29.41 50-59 21 15.44 60 and above 12 8.82 Missing 1 0.74 Panel C: Education Level Pharmacy Operators (N=136)

n % Sec/Voc 22 16.18 Tertiary 113 83.09 Missing 1 0.74 Panel D: Ownership Status Pharmacy Operators (N=136)

n % Owner 53 38.97 Manager 46 33.82 Other 36 26.48 Missing 1 0.74

Table 2 shows the socio-demographic characteristics of the pharmacy operators interviewed in this study. Panel A shows the sex distribution of the respondents. Panel B shows the age distribution of respondents in years. Panel C shows the highest level of education attained by respondents at the time of interview. Panel D shows the ownership status of the respondents in relation to the pharmacy they operate. The last row in each panel represents the missing values for that observation. The figures in the columns labeled ‘n’ for each panel show the observations for each response. The figures in the columns labeled ‘%’ for each panel show the proportions of each observation in percentage. The total number of respondents (X) for each study area is reported as (N=X) at the header rows for each panel. In Table 2, results indicate that majority (79.41%) of respondents were males. Statistics from Table 2 suggests that most of the data gathered in this research reflects the opinion of male pharmacy operators. Results further indicate that the age grouping with the highest number of respondents (30.15%) was age range thirty to thirty-nine (30 – 39). Also, most of the pharmacy operators are within the middle aged population group. There is however a significant number of younger aged groups engaged in the operation of pharmacy. In general however, the statistics reflects the normal distribution of labour force in Ghana. With regards to the highest level of education attained, results indicate that all respondents had attained at least secondary/vocational level of education with a very large percentage (83.09%) of them having tertiary education. This pattern was expected due to the nature of work and level of knowledge/expertise required to successfully operate a pharmacy business. Summary statistics on the ownership status of the pharmacy operators indicate that more than half (38.97%) of respondents were owner managers. This could be attributed to the reason that pharmacy businesses in Ghana are operated as small enterprise with direct cash transactions. Owners of such enterprises usually operate the businesses on their own or with family members (Kwabena et al, 2013). The Information Systems Environment of the Pharmacy Council In this section, summary statistics on the information systems available at the Pharmacy Council are presented in Table 3. Also presented are the percentage proportions for the distributions, relative to the total number of respondents.

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Table 3: Summary Statistics on Pharmacy Council Regional Offices with Websites and Departments That Have Email Addresses and/or Computers

Region Has Website Departments with Email Addresses Departments That Have Computers Eastern Yes Accounts Inspectorate, Accounts Ashanti Yes Inspectorate, Accounts Inspectorate Western Yes - Inspectorate Northern Yes - Inspectorate Volta Yes - Inspectorate Greater Accra Yes MIS & P, Accounts Inspectorate, PPME, MIS & P,

R & L, Accounts, ETD Table 3 shows the summary statistics on Pharmacy Council regional offices and departments that have computers, websites and/or email addresses. From the left, the first column of Table 3 shows a list of the regions. The second column indicates responses to observations on the availability of a website. Third column indicates departments per region that have email addresses. The fourth column indicates, per region, departments that have working computers. Results in Table 3 indicate that not all regional offices and departments had email addresses. Results in Table 3 further indicate that all departments at the Greater Accra region have at least one computer. Results also indicate that the inspectorate department in each regional office has at least one computer. These results suggest that each regional office has at least one computer for staffs to work with; however some of the departments do not have email addresses they could use. This further suggests that some level of computerization already exists at each regional office of the Pharmacy Council. Responses were also received on the knowledge and/or experience of staff in the use of computers. Summary statistics on the responses is presented in Table 4. Results on computer knowledge are in three categories; high – to indicate advanced knowledge, average – to indicate basic knowledge, never – to indicate staff who have never had any experience with the use of computers. Responses were further solicited from staff on access to use a computer at the departments they worked. Summary statistics on their access to use computers at their departments are presented in Table 4. Table 4: Summary Statistics on Distribution of Staff by Computer Knowledge

Panel A: Computer Knowledge N % High 22 68.75 Average 9 28.13 Never 0 0.00 Missing 1 3.13 Panel B: Require Computers for Work N % Yes 24 75.00 No 7 21.87 Missing 1 3.13 Panel C: Use A Computer at Work N % Yes 27 84.38 No 4 12.50 Missing 1 3.13

Table 4 shows summary statistics on Pharmacy Council staff based on their knowledge and access to use computers at work. Panel A shows the distribution of respondents based on their knowledge about computers. Panel B shows the distribution of respondents who require computers to work. Panel C shows the distribution of staffs that have access to use a computer at work. The last row in each panel represents the missing values for that observation. The figures in the columns labeled ‘n’ for each panel show the observations for each response. The figures in the columns labeled ‘%’ for each panel show the proportions of each observation in percentage. Results from Table 4 indicate that at almost all respondents had some experience with the use of computers with more than two-thirds (68.75%) of the respondents having a high level of experience with the use of computers. These results suggest that majority of Pharmacy Council staff have high level of knowledge in operating computers. At least, every member of staff interviewed has knowledge in the use of a computer. This further suggests that the Pharmacy Council’s human resource is well equipped for a computerization program at the Pharmacy Council with little training. As indicated in Table 4, over eighty per cent of respondents have access to use computers at their departments. Interestingly, 4 respondents indicated that

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they don’t have access to use computers at their departments. Results from responses about access to a computer suggest that, most of the Pharmacy Council staff (84.38%) were exposed to using computers. The few (12.5%) who indicated that they did not have access to computers attributed that problem to the limited number of computers available at the region. Following analysis on computer knowledge, results on the experience of pharmacy operators on information systems are presented in Table 5. Statistics on level of knowledge in computers and operators who have their pharmacies computerized are presented. Also presented are the percentage proportions for the distributions, relative to the total number of respondents. Table 5: Summary Statistics on Computer Usage Experience

Panel A: Computer Experience N % High 90 66.18 Average 38 27.94 None 6 4.42 Missing 2 1.47 Panel B: Computerized Pharmacy N % Yes 76 55.88 No 56 41.18 Missing 4 2.94

Table 5 shows summary statistics on pharmacy operators based on their experience with the use of computers. Panel A shows the distribution of operators based on their level experience with computers. Panel B shows the distribution of operators with computerized pharmacies. The last row in each panel represents the missing values for that observation. The figures in the columns labeled ‘n’ for each panel show the observations for each response. The figures in the columns labeled ‘%’ for each panel show the proportions of each observation in percentage. Results in Table 5 indicate a high number of advanced users of computers. Six (6) respondents indicated that they have no experience using a computer. These results suggest that the Pharmacy Council may have a number of clients who may not have the ability to use computers. Although results in Table 5 indicate that almost fifty per cent of pharmacy operators interviewed have not computerized any of their business operations, the relatively large number of pharmacy operators who have computerized pharmacies supports the recognition of the value of computerization to the operation of businesses. This finding also suggests a potential environment for a seamless integration between the Pharmacy Council and pharmacy operators. An effort to computerize the Pharmacy Council may increase the number of pharmacy operators that may decide to computerize their pharmacies. Perceptions about Computerization of Processes/Services of the Pharmacy Council Responses were received concerning the extent to which Pharmacy Council staffs agree with the computerization of certain operations of the Pharmacy Council. Summary statistics of the responses is presented in Table 6. Results from the statistics indicate a high number of respondents agreeing with computerization of the listed operations of the Pharmacy Council. The relatively high strong approval from the staff respondents to computerize the renewal of license may be attributed to the fact that this activity is the most commonly recurring among the others. The few responses that disagreed to computerization of some of the services suggest that some staff have not yet adjusted to the use computers in their duties. With regards to opinions from pharmacy operators about the computerization of certain operations of the Pharmacy Council, summary statistics presented in Table 6 indicate that a high number of respondents agree with computerization of the listed operations of the Pharmacy Council. A relatively low proportion of pharmacy operators however do not strongly agree with the computerization of enquiry services.

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Table 6: Summary Statistics on Staff Opinion about the Computerization of Listed Processes/Services

Panel A: Opinions from Pharmacy Council Staff SA A I D SD Missing Total Application for Relocation 22 6 2 1 0 1 32 68.75 18.75 6.25 3.13 0.00 3.13 100.00 Application for a New License 21 6 1 2 1 1 32 65.63 18.75 3.13 6.25 3.13 3.13 100.00 Renew License 27 3 1 0 0 1 32 84.38 9.38 3.13 0.00 0.00 3.13 100.00 Enquiries 17 8 3 2 0 2 32 53.13 25.00 9.38 6.25 0.00 6.25 100.00 Registration 24 4 0 1 0 3 32

75.00 12.50 0.00 3.13 0.00 9.38 100.00 Panel B: Opinions from Pharmacy Operators SA A I D SD Missing Total Application for Relocation 71 50 7 2 0 6 136 52.21 36.76 5.15 1.47 0.00 4.41 100.00 Application a New License 81 36 7 3 0 9 136 59.56 26.47 5.17 2.21 0.00 6.62 100.00 Renewal of License 99 30 2 1 0 4 136 72.79 22.06 1.47 0.74 0.00 2.94 100.00 Enquiries 88 37 3 1 0 7 136 64.71 27.21 2.21 0.74 0.00 5.15 100.00 Registration 97 30 1 2 0 6 136

71.32 22.06 0.74 1.47 0.00 4.41 100.00 Table 6 shows summary statistics on the opinions of both Pharmacy Council staff and pharmacy operators about the computerization of a list of services at the Pharmacy Council. Panel A shows opinions from Pharmacy Council staff. Panel B shows opinions from pharmacy operators. The figures in the columns labeled ‘SA’ for each panel show the observations that strongly agree to service computerization. The figures in the columns labeled ‘A’ for each panel show the observations that partially agree to service computerization. The figures in the columns labeled ‘I’ for each panel show the observations that are indifferent to service computerization. The figures in the columns labeled ‘D’ for each panel show the observations that partially disagree to service computerization. The figures in the columns labeled ‘SD’ for each panel show the observations that strongly disagree to service computerization. The column labeled ‘Missing’ in each panel represents the missing values for the observations Similar to responses from staff respondents, results from Table 6 suggests that pharmacy operators also strongly allude to the computerization of renewal of license for possibly the same reason that it is the most commonly recurring process. Following results on perceptions, responses from pharmacy operators on the computerization and service quality improvement at the Pharmacy Council were paired with their ages to determine the association between the age of the respondent and response on whether computerization can improve the quality of service at the Pharmacy Council. The strength of the association was also subjected to a chi-square test. Univariate logistic regression models on the on perception about the effects of computerization on the service quality of the Pharmacy Council against operators’ age and computer usage experience is presented in Table 7.

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Table 7: Univariate Logistic Regression Model on the Influence of Age on Clients’ Perception about the Effects of Computerization on the Service Quality of the Pharmacy Council

Panel A: Age Groupings Odds Ratio P-Value 20-29 1 30-39 0.63** 0.70** 40-49 1.95** 0.64** 50-59 0.30** 0.32** 60 and above 0.13** 0.10** Panel B: Experience with the Use of Computers Odds Ratio P-Value High 1 Average 0.83** 0.8** None 0.11** 0.03** Panel C: Has Computerized Pharmacy Odds Ratio P-Value Yes 1 No 0.89** 0.9**

This table shows logit models that use Odds Ratio and the Pearson’s Chi-square test to determine the significance of association for the observations among pharmacy operators in this study. Panel A shows the logit model for age grouping. Panel B shows the logit model for experience in using computers. Panel C shows the logit models for operators that have computerized pharmacies. The first column in each panel in Table 7 shows the variables for the observations that are tested. The figures in the second column of each panel are the Odds Ratios. The third column in each panel shows the P-value. The symbols ***, ** and * indicate significance at 1, 5 and 10 percent levels respectively. The symbol ‘

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CONCLUDING COMMENTS To conclude, the general objective of the study was to assess the role of information systems in enhancing the performance of the Pharmacy Council in its activities. The study was conducted at the Pharmacy Council, in addition to one hundred and thirty-six (136) Pharmacies in the Ashanti Region. The demographic and business characteristics of the pharmacy operators studied were determined and information was tapped from responses from both Pharmacy Council staff and pharmacy operators to achieve specific objectives set out for the study. A very large percentage of pharmacy operators had obtained tertiary education. This pattern was expected due to the nature of work and level of knowledge/expertise required to successfully operate a pharmacy business. Results from the statistics indicate that a high number of both the staff and pharmacy operators strongly agreed with computerization of the listed operations of the Pharmacy Council. There were relatively high approval from both the staff and pharmacy operators to computerize services for license renewal; the most commonly recurring service compared with the others. From this indication, an information system presents a great potential for the Pharmacy Council to reengineer its processes to meet the current global trends and improve on its service delivery. There is no significant association between the age of the pharmacy operators and their perception of the value of computerization to improve the quality of service of the Pharmacy Council. However, responses from pharmacy operators that have experience with the use of computers and those that have computerized pharmacy have significant associations with their perceptions about whether computerization can improve the quality of services of the Pharmacy Council. Based on the findings that there is a high level of computer literacy among the staff of the Pharmacy Council, it is recommended that more effort on computerization of the service of the Pharmacy Council should be concentrated on providing adequate equipment. The Pharmacy Council may have to consider increasing the number of computers available for use per region/department in order to ensure effective implementation of a computerized system. The complete access to computers at certain regions should be maintained. However, other regions with limited staff access to computers should be furnished with enough computers to cover, at least all staffs that require computer access in order to carry out their duties effectively. Since the existing human resource available at the Pharmacy Council have knowledge in the use of computers, the Council may require very little training for the staff. Concerning pharmacy operators, any effort to computerize the Pharmacy Council may have to take into account strategies that would not make the computerization exercise a disadvantage to the minority of operators that have no knowledge about using computers in order to ensure that computerization provides improved service delivery. Training programmes prior to and during computerization of the Pharmacy Council could be held to expose these pharmacy operators to the use of computers. On the other hand, packages such as end-to-end service integration with pharmacy operators who have their businesses already computerized may encourage more pharmacy operators to computerize their pharmacy businesses and hence provide more support to ensure that computerization enhances the service quality of the Pharmacy Council. Considering the high number of staff and pharmacy operators in favour of computerizing the renewal of license, the Pharmacy Council should consciously examine all other frequently recurring services and formulate strategies to computerize them. The identified association between experience of use and perception of the value of computerization suggests that, any effort toward computerization at the Pharmacy Council may be more effective if it is preceded by an awareness program; a program that should introduce pharmacy operators who have no experience in the use of computers to the benefits of computerizing systems. Based on the findings that the lack of personnel with high expertise coupled with the cost of hiring new personnel to manage an information system are significant challenges to computerization, the Pharmacy Council may consider alternative options of outsourcing the development and maintenance. On another hand, a little upgrade of the skills of a few of the existing staff with highly advance skills in using computers may prove cost effective. Following the challenges of commitment by top managers, it is recommended that one service process in one regional office should be computerized as a prototype. After

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a period, the experience from the prototype should be evaluated against those of other processes. This may serve as an evidence base to strengthen the commitment of top management to computerization. Concerning the difficulty in computerizing certain services, it is recommended that computerization should commence with the processes that could be easily computerized and gradually research into how other complex process could be also computerized. Further studies into the information systems of the Pharmacy Council should examine the availability and effectiveness of the administrative structures and Information Technology policy documents. Finally, data collection was skewed towards male operators of pharmacies. Further studies should endeavour to solicit views from an equal number of male and female operators for a richer response. From a technical perspective, further studies should examine should take into account recovery rate in case of system failure. REFERENCES Alter, S. (1998). Information systems: Addison-Wesley Longman Publishing Co., Inc. Brynjolfsson, E. (1993). The productivity paradox of information technology. Communications of the ACM, 36(12), 66-77. Brynjolfsson, E., & Hitt, L. M. (2000). Beyond computation: Information technology, organizational transformation and business performance. The Journal of Economic Perspectives, 23-48. Castells, M., & Development, U. N. R. I. f. S. (1999). Information technology, globalization and social development: United Nations Research Institute for Social Development Geneva, Switzerland. Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness and structural design. Management science, 32(5), 554-571. Davenport, T. H., & Short, J. (2003). Information technology and business process redesign. Operations management: critical perspectives on business and management, 1, 97. Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International journal of man-machine studies, 38(3), 475-487. Day, G. S. (1994). The capabilities of market-driven organizations. the Journal of Marketing, 37-52. Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of management information systems, 19(4), 9-30. Diagnostic, A. I. C., Foster, V., & Briceño-Garmendia, C. (2010). Africa's infrastructure: a time for transformation: World Bank Publications. Dzidonu, C. (2003). An Integrated ICT-led Socio-economic Development Policy and Plan Development for Ghana—the Ghana ICT for Accelerated Development (ICT4AD) Process. Accra: Institute for Scientific and Technological Information, Council for Scientific and Industrial Research. Gyamfi, A. (2005). Closing the Digital Divide in Sub-Saharan Africa: meeting the challenges of the information age. Information development, 21(1), 22-30. Heeks, R. (2002a). Information systems and developing countries: Failure, success, and local improvisations. The information society, 18(2), 101-112.

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Heeks, R. (2002b). Reinventing government in the information age. Reinventing government in the information age: International practice in IT-enabled public sector reform, 9-21. Huber, G. P. (1990). A theory of the effects of advanced information technologies on organizational design, intelligence, and decision making. Academy of management review, 15(1), 47-71. Igbaria, M., & Tan, M. (1997). The consequences of information technology acceptance on subsequent individual performance. Information & management, 32(3), 113-121. Joseph, R. (2013). Service Delivery Through Information Systems in TANROADS: Challenges and Possibilities in Dar Es Salaam and Mwanza: Anchor Academic Publishing (aap_verlag). Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002). AIMQ: a methodology for information quality assessment. Information & management, 40(2), 133-146. Martey, A. (2004). ICT in distance education in Ghana. Library Hi Tech News, 21(5), 16-18. Melville, N., Kraemer, K., & Gurbaxani, V. (2004). Review: Information technology and organizational performance: An integrative model of IT business value. MIS quarterly, 28(2), 283-322. Naumann, F., & Rolker, C. (2000). Assessment methods for information quality criteria. O'Reilly, C. A. (1982). Variations in decision makers' use of information sources: The impact of quality and accessibility of information. Academy of Management Journal, 25(4), 756-771. Policy, G. I. A. (2003). A Policy statement for the realization of the vision to transform Ghana into an information-rich knowledge-based society and economy through the development, deployment and exploration of ICT’s within the economy and society. Ministry of Education, Accra, Ghana. Porter, M. E., & Millar, V. E. (1985). How information gives you competitive advantage: Harvard Business Review, Reprint Service. Ravichandran, T., & Lertwongsatien, C. (2005). Effect of information systems resources and capabilities on firm performance: A resource-based perspective. Journal of management information systems, 21(4), 237-276. Ruiz, M., Mejia, V., & Kaplan, A. (2003). Information system comprised of synchronized software application moduless with individual databases for implementing and changing business requirements to be automated: Google Patents. BIOGRAPHY Kwabena Obiri-Yeboah is a lecturer in Information Systems at Kwame Nkrumah University of Science and Technology (KNUST), Ghana. He had his MSc in Management Information Systems from University of Texas at Dallas. He has 12 year experience in IT systems development; 10 years with JCPenney Corporation in Dallas. His areas of interest include IT adoption in business, IT policy and IT education. He can be contacted at: Department of Decision Science, KNUST School of Business, KNUST, Kumasi-Ghana. Phone: +233241076524. Email: kyeboa@yahoo.com

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Eliezer Ofori Odei-Lartey is a Data Manager and a Research Fellow at the Kintampo Health Research Centre. He can be contacted at: Kintampo Health Research Centre, P. O. Box 200, Kintampo-North, Brong Ahafo Region - Ghana. Phone: +233246926396. Email: eliezer.lartey@kintampo-hrc.org or elelart@gmail.com Kenneth Simmons is currently the Regional Head of the Pharmacy Council at Ashanti Region, Ghana. He holds BParm and MBA degrees from KNUST, Ghana and a Certificate in Public Administration from GIMPA, Ghana. He has over 8 years of experience as a regional head for the Pharmacy Council. He can be contacted at P. O. Box KS778 Adum-Kumasi: Phone +233244274875. Email: sabeku@yahoo.com

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Global Journal of Business Research Vol. 9, No. 2, 2015, pp. 23-40 ISSN: 1931-0277 (print) ISSN: 2157-0191 (online)

www.theIBFR.com

TRANSFORMING LEADERS THROUGH CULTURAL

INTELLIGENCE James B. Box, Northcentral University

Judith A. Converso, Northcentral University Efosa Osayamwen, Northcentral University

ABSTRACT

Developing charismatic leaders in the 21st century must include fostering the cultural awareness skills for effectively managing employees from many new and unique backgrounds. The purpose of this quantitative study was to determine if there was a correlation between cultural intelligence (CI) and transformational leadership (TL) attributes of managers at American Fortune 500 companies. The data results indicated that there was a statistically significant positive relationship between the CI and the TL abilities of managers. The conclusion drawn from the findings provide new information to theory of cultures when CI and TL constructs are compared. It is recommended that American Fortune 500 leaders continue to strengthen culture-specific awareness’s through educational and personal pursuits. JEL: C14, C42, D78, E61, I21, J24, J50, J52, J71, M12, M21, N30 KEYWORDS: Cultural Intelligence, Transformational Leadership, Motivational, Inspirational, Charismatic, Employee Turn-over INTRODUCTION

orth American Fortune 500 companies continue to grow into microcosms of the diversity in society (Martelli & Abels, 2011). In the last decade, American businesses have undergone extensive immigration and human resource policy changes due to globalization (Merrifield, 2006). United

States Department of Labor Statistics issued forecasts due to globalization that ethnic minorities and immigrants will increase over the next decade as compared to the white Anglo population percentage; Asian American (44%), Hispanic/Latin American (36%), African America (21%), and White (9%) increases. To address the impacts of globalization, American companies have created hiring strategies that support the new diversity requirements in business, including outsourcing high-paid jobs while importing lower-waged workers, incorporating new communication methodologies, and adjusting to the new influxes of people from many distant places (Wallace & Figueroa, 2012). As an example, American Fortune 500 companies have increased the hiring of many people from foreign countries, resulting in a 33% increase in U.S. Citizenship and Immigration Services H-1B visa, form I-129, activity (Butler, 2012). International cooperation in business has become increasingly important for effective American company leaders (Bass & Riggio, 2006) as they must operate within new diverse settings (Center for American Progress, 2009). This research extended the study done by Ng and Sears (2011) and Keung (2011), applied to different levels of managers in American Fortune 500 companies. Ng and Sears discovered that Chief Executive Officers in Canadian companies needed to strengthen their cultural intelligence skills in order to be effective and transform their workforces into inclusive, diverse environments.

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The ongoing globalization processes involve interculturally training managers to be effective in the workplace, transforming societies and cultures through advances in economics, technology, and communications (Scherer & Palazzo, 2011). Leadership through the transformational paradigm compliments organizational performances with many meta-analyses (DeGroot, Kiker, & Cross, 2000). The purpose of this research was to determine if there is a relationship between the attributes of cultural intelligence (CI) and transformational leadership (TL) attributes of managers at American Fortune 500 Companies and what effect does these interactions have upon their abilities. LITERATURE REVIEW Leadership and Cultural Intelligence The study of leadership comes in many forms of organizational behaviors and performance outcomes (Pauliené, 2012). Early leadership theorists attributed charismatic influences of leaders to the strength of their abilities (Weber, 1968). Leadership is an elusive construct, riddled with ambiguity, and difficult to study systematically (Nohria & Khurana, 2010). The study of intelligence includes areas in psychology, neurobiology, and behavioral genetics (Gottfredson & Saklofake, 2009). Twentieth century leadership involved complex behaviors and complex interconnected relationships in order to accomplish work (Baligh, 1994). One of the most controversial behaviors discussed within leadership literature is intelligence (Eysenck & Kamin, 1981). In essence, leadership intelligence exists in many forms and is developed mainly through experience and continued education (Sternberg, 2011). The requirement for intelligent managers to prepare organizations for the changes needed in the 21st century requires charismatic leadership skills operating in dynamic environments. Brown and Starkey (2000) called for continued explorations of the essential elements of modern leadership. Leadership attributes such as intelligence, education, sensitivity, hubris tendencies, and competence were the dominant themes for this research study (Riggio & Mumford, 2011). This research furthered leadership paradigms for the 21st century by providing empirical data for leader potential analysis (Silzer, 2010), including cultural awareness understandings and influences. Leaders in the 20st century maintained an authoritative posturing over subordinates, believing that greater efficiencies came from different forms of stronger dominance (Bussel, 1997). Past leaders and managers had inclinations to assume ultimate power in positions of authority (McClelland, 1961). Leaders also made decisions based upon the scope of his or her knowledge and the contributions that the decisions made to the enterprises (Drucker, 1955, 2004). This psychological study of leaders and managers added to the general knowledge of human interactions (Maslow, 1966). The cultural intelligence theory proposed by Ang, Van Dyne, and Koh (2006) was used to create the Cultural Intelligence Scale, CQS, as a self-awareness style of inventory to improve an individual’s cultural awareness (Moshavl, Brown, & Dodd, 2003). The impact of cultural exposure is the awareness that generates the need to foster skills dealing with new people with unique customs coming from many unique places in the world (Hester, 2005). The contemporary American strategy of outsourcing work into foreign countries also drives the need for cross-culturally trained leadership, guiding multinational businesses spread across different continents (Kamann & van Nieulande, 2010). Sternberg (1977) proposed that intelligence was more than solving problems; it was also an analytical reasoning process. Sternberg explained that successful intelligence leads to the ability to cope during a work career and life. Sternberg (1996) added that people tend to judge intelligence by levels of academic achievement, which is measuring inert intelligence, defined as the inability to apply knowledge, which may not lead to realistic problem-solving.

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This research study of managers provided a general grounding to improve the knowledge of human interactions. No preeminent theory has evolved from any contemporary research studies of the relationship between transformational leadership and cultural intelligence (Chin & Gaynier, 2006; Keung, 2011). Leadership learning of the culture of colleagues builds trust, breakdown communication barriers, and improves team efficiencies in American Fortune 500 companies (Thomas, Zolin, & Hartman, 2009). Identifying future leaders whom are transformational and culturally intelligent remains a critical task for all Fortune 500 companies in the 21st Century (Wilson & Mujtaba, 2010). The relationship between leadership cultural intelligence and leadership transformational skills has been the subject of few research studies to date (Keung, 2011). Comparative research into organizational behavior has demonstrated that the softer skill areas of management are critical for the cultural intelligence growth necessary in the 21st century (Brungardt, 2011; Brungardt, Greenleaf, Brungardt, & Arensforf, 2006; Johnson, Lenartowicz, & Apud, 2006; Sawhney, 2008; Service, 2012). Earlier, Yukl (1999) postulated that there was much to be discovered about the underlying processes through which leaders influence follower attitudes, behaviors and motivation. Earley (1984) added that identifying how followers’ cultures impact preferences, the stereotypes of leader preferences, and how these elements impact business. Previous research discovered that global leaders are thought to exhibit common behaviors such as cosmopolitan, cognitive complexity, mental inquisitiveness, honesty, humility, and personal resilience (Earley & Ang, 2003; Javidan, Steers, & Hitt, 2007; Mendenhall, Osland, Bird, Oddou, & Maznevski, 2008). In addition, no single preeminent theory substantiates the link between cultural intelligence and transformational attributes of leaders and managers (Keung, 2011; Lugo, 2007; Mannor, 2008). One previous study showed substandard results between cultural intelligence and the social influence predictors of transformational leadership, suggesting the influence of an unidentified factor of emotional intelligence (Brown & Moshavi, 2005). The relationship of the cultural intelligence and transformational leadership skills of managers influences how successful they are incorporating changes in the modern workplace (Bikson, Treverton, Moini, & Lindstrom, 2003; Heames & Harvey, 2006; Moran, Harris, & Moran, 2011; Oreg, 2006). Popper (2002) argued that a good theory has to be risky, as it can be shown to be either true or false. A bad theory may fit any data set according to Fontaine (2007). The assessment of the relationship between cultural intelligence and transformational leadership remains undiscovered in the literature when defining leadership, culture, and organizational behaviors (Ismail, Mohamed, Sulaiman, Mohamad, & Yusuf, 2011; Keung, 2011; Lugo, 2007); as Loehr and Schwartz (2001) called for continued studies in the psychology of leadership as a multidimensional, culturally-linked phenomena. Leadership and Transformational Motivations Many management theories have been tested across cultures (Dickson, Den Hartog, & Mitchelson, 2003; Vallas, Zimmerman, & Davis, 2009. The relationship between cultural intelligence and emotional intelligence theory and skills has been developed and contrasted numerous times (Goleman, 1995; Hui-Wen, Mu-Shang, & Nelson, 2010; Racheli, Dolan, & Cerdin, 2005); which is similar to comparisons of the relationship between transformational leadership and emotional intelligence (Yitshaki, 2012). In contrast, conjoined research of the relationship between transformational leadership and cultural intelligence theories is limited (Pauliené, 2012). A secondary principle theory integrated within this research study was the leadership transformational skills developed by Bass and Avolio (1994). There are extensive studies on the singular emotional intelligence (EI) along with both cultural intelligence and transformational leadership skills (Dean, 2007; Kim, 2009; Lugo, 2007). Transformative leaders motivate subordinates to reevaluate known resolutions rather than apply old solutions to new problems (Jones, Harris, & Santana, 2008). Contemporary transformational

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leadership skills were compared for interactions with cultural intelligence attributes within this research study. The social psychology and attribution theories added situation-specific behaviors based upon cultural traits (Webb, 1983). The social cognitive theory of personality (Bandura, 1986) and self-determination theory (Deci, Koestner, Ryan, & Cameron, 2001; Ryan & Deci, 2008) are foundations to the cultural intelligence theory development of the scale instrument. In the social cognitive theory individuals believe they can intentionally influence their life circumstances (Bandura, 1986; Deci et al, 2001), complimenting the need for managers in American multinational companies to strengthen his or her cultural intelligence skills in the 21st Century (Deci & Ryan, 2000; Moon, 2010; Ryan & Deci, 2008). In the self-determination theory personal choices or intrinsic aspirations energize individual actions. Both theories allow individuals to adapt to new diverse environments (Deci et al., 2001). The development of culturally competent personnel and leaders involves four levels of understanding, education and relationships (Brownlee & Lee, 2012; Caligiuri & Tarique, 2012; Chemers, 1997). The first stage in building culturally competent leaders is acquiring the knowledge of the cultural characteristics, values, beliefs, and behaviors of another cultural group (Kiyokawa, Dienes, Tanaka, Yamada, & Crowne, 2012). The second stage of building culturally competent managers is maintaining the cross-cultural and cultural awareness, and being open changes in cultural attitudes toward other cultures (Browlee & Lee, 2012; Webb, 1983). The third stage involves the understanding of the cultural differences and being sensitive to the intercultural conflicts that arise (Brownlee & Lee, 2012; Chiu & Hong, 2005; Johnson, Cullen, Sakano, & Takenouchi, 1996; Shapiro, Ozanne, & Saatcioglu, 2008; Skarmeas, Katsikeas, Schlegelmilch, 2002; Zagorsek, 2004). The last stage of cultural competence combines all of the previous steps and integrates the different behaviors, attitudes, and policies into cross-cultural group settings (Brownlee & lee, 2012; Crowne, 2008; Gregersen & Black, 1990; O'Sullivan, 1999). Zander, Mockaitis, and Butler (2012) added that cross-cultural competence in essential for leadership functioning in multicultural teams. The progression and attributes are shown in Figure 1 for developing cultural competent personnel with the cultural and cross-cultural intelligence necessary to function effectively in the 21st century. RESEARCH DATA AND METHODOLOGY The methodological design for this research study was a non-experimental (no control group) quantitative survey method and multivariate design (using a survey as a data collection instrument). This method was selected because of the benefits of the survey type of research. Survey research describes a sample by the use of quantitative or numeric description of trends, attitudes, activities, or opinions (Fowler, 2009). The survey instruments were distributed to the target population of 1082 managers at two American Fortune 500 companies. There were 266 questionnaires returned (one was incomplete) for a 25% response rate. A secure socket-layer, SSL, provided a secure website that kept the data from being compromised. Managers received the request for the survey through the company secured site. The online survey contained a consent page describing issues such as privacy, confidentiality, and risks associated with this research. The Cultural Intelligence Scale was used for this research study to collect data from all participants.

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Figure 1: The Cultural Intelligence Circular Progression Process Revolves Around Four Stages of Development.

In stage one the manager has an in-country, or foreign cross-cultural, exposure to a new culture. This is the foundation of cultural awareness. Stage two highlights the differences in communications, behaviors, and belief systems that the exposed relates to based upon their own cultural attitudes. Stage three is the beginning of sensitivity toward a new culture. The managers’ motivational and behavioral skills transition to a higher level of operational ability. Finally in stage four the manager has the ability to cognitively function within the new level of cultural competence. RESULTS The survey instrument was found to have a Cronbach’s Alpha of .752 for the 10 items. All the correlations of the study variables were examined to determine the strength the inter-relationship (Ang et al., 2007; Van Dyne et al., 2008). The correlations for the research study between the outcome variables (CI Cognitive, CI Motivational, CI Metacognitive, and CI Behavioral) and the predictor variable (Transformational Leadership) ranged from 0.199 (CI Cognitive), 0.266 (CI Motivational), 0.295 (CI Metacognitive), to 0.322 (CI Behavioral). The results of this analysis for this research question indicated a significant statistical relationship for CI behaviors and CI motivations and TL. A factorial ANOVA analysis with covariate interaction was also performed to test the homogeneity of variance for CI and TL of all levels of managers. The Levene (1960) test was also performed to test the homogeneity