Although I usually do strategy or digital transformation projects for bigger companies and rarely startups, i absolutely find the term growth hacker portraying how I try to realize growth for clients through market research, analytics, financial modeling, strategy development and implementation road maps. Using an empirical approach which I feel is often lacking in the advertising or creative space where ‘oh lets build a fun app’ can still be seen happening too often. It’s my previous experience in digital marketing and the necessary level of knowledge about SEO, analytics, usability, e-commerce, platforms and technology (also for a big part because of blogging) that help me to understand platform strategies and how big established companies can realize growth by designing or improving their digital initiatives.

“The rise of the Growth Hacker“
The new job title of “Growth Hacker” is integrating itself into Silicon Valley’s culture, emphasizing that coding and technical chops are now an essential part of being a great marketer. Growth hackers are a hybrid of marketer and coder, one who looks at the traditional question of “How do I get customers for my product?” and answers with A/B tests, landing pages, viral factor, email deliverability, and Open Graph. On top of this, they layer the discipline of direct marketing, with its emphasis on quantitative measurement, scenario modeling via spreadsheets, and a lot of database queries. If a startup is pre-product/market fit, growth hackers can make sure virality is embedded at the core of a product. After product/market fit, they can help run up the score on what’s already working.

This isn’t just a single role – the entire marketing team is being disrupted. Rather than a VP of Marketing with a bunch of non-technical marketers reporting to them, instead growth hackers are engineers leading teams of engineers. The process of integrating and optimizing your product to a big platform requires a blurring of lines between marketing, product, and engineering, so that they work together to make the product market itself. Projects like email deliverability, page-load times, and Facebook sign-in are no longer technical or design decisions – instead they are offensive weapons to win in the market.

While others are more critical of the term, I think it’s part of a larger trend as we shift to big data or even small data; the need for analytical data-driven analysts and marketeers. As portrayed by this NYTimes article:

“Good with numbers? Fascinated by data? The sound you hear is opportunity knocking.”
A report last year by the McKinsey Global Institute, projected that the United States needs 140,000 to 190,000 more workers with “deep analytical” expertise and 1.5 million more data-literate managers, whether retrained or hired.

In my small hub of Amsterdam I already see this trend taking shape. Every few months I have someone asking if i know any analytical students or juniors that are good with numbers and would be interested in an analytical job often combining finance and web analytics. There seems to be a bright and interesting future ahead especially for those who love numbers and marketing.

Here is an example of three keyboard shortcuts I often use in Excel. Especially the paste special > values shortcut is a useful one to know.

Head over to macabacus for the full list of useful Excel shortcuts. The website includes a pdf file with a list of the most important Excel shortcuts to have on hand. The website also includes some macros and custom tool bars, but they seem mainly aimed people working in finance making financial models. You are welcome to share macros and other Excel resources in the comments below.

How to get ThesisTools questionnaire data into SPSS

Thesistools is a free survey/questionnaire website that most of the Dutch students use to create their questionaires. Importing your Thesistools data into Spss can be done by following these steps.

1.       Go to the ThesisTools website > Modify Questionnaire. Login, click ‘Results’ and download the data as an Excel file.

2.       Open the Excel file (if you get a warning, just click ‘Yes’) . Delete the Page, Title and Legend rows.

3.       Rename what is now the top-left cell (‘Question’) into respondentID (or something like that). Also, you can delete any blank columns. The result is a nice, clean Excel file (see below). Save the file as an Excel (.xls) file . Close Excel.

4.       Open SPSS > File > Open Data, set “Type of files” to “Microsoft Excel (*.xls)”  and select your Excel file

5.      A dialogue screen will appear. Make sure that “Read variable names from the first row of data” is selected (which it is by default) and click ‘OK’.  SPSS should now have opened your file, which can later save a proper SPSS file (.sav file). For practical purposes, you may want to shorten the labels/names of the variables. You can do this by going to the ‘Variable View’ sheet.

SPSS Analyses of Survey Data tutorial

Introduction

This document contains a short description of the analyses that have to be performed on the data collected with the survey that is part of the BRM course. The aim of these analyses is to test your hypotheses by for instance getting insight into (1) the differences between groups within the organization or between different variables and (2) the relationships between different variables.

In this process, three main steps are distinguished:

1. Prepare data
2. Test operationalizations
3. Test theory (hypotheses)

Prepare data

First, we have to make sure the data are ready for further analyses. This concerns the following steps:

Check for errors

-          WHAT?
Make sure that there are no errors in the dataset, such as impossible values or other mistakes that would affect your analysis.

-          HOW?
- Look through the data set for strange things
- Get the descriptives for all items

-          WHERE?
- Analyze > Descriptives > Frequencies
- Analyze > Descriptives > Descriptives

Recode variables

-          WHAT?
Make sure that all items (= questions in the dataset) “point the same way”. Sometimes, the same concept is measured by items that are both positive and negative in terms of that concept. For instance, measuring attitude towards a product with items saying both “I like…” and “I dislike…”.

-          HOW?
Reverse the codes for items that “point the wrong way”. Recode into new variables, and change values from 5>1, 4>2, 3>3, 2>4 and 1>5.

-          WHERE?
Transform > Recode > into different variables

Make new variables for open questions (if necessary)

-          WHAT?
Some questions may be open questions, e.g. “What’s your function?”. If that is the case, you don’t have a variable you can use to compare groups (for instance). In that case, you have to make a new variables with categories (e.g., management, sales, engineering, etc.).

-          HOW?
Insert a new variable in the dataset, and manually assign categories to the different functions. Go through the dataset case per case.

-          WHERE?
Edit > Insert variables

Test operationalizations

Having gotten our data up to date, we can check for the quality of our operationalizations – in other words, the quality of the way we have measured our variables. Before continuing with the data analysis, we have to make sure that the measures we are basing our analyses on, are OK.

Reliability analysis

-          WHAT?
Test whether the different items that we assume to measure one variable, can indeed be taken together into one scale (e.g., kshare1 up to kshare8).

-          HOW?
Perform a reliability analysis. Pay attention to the following criteria:

o   Cronbach’s alpha (is this higher than .65?)

o   Corrected item-total correlation (higher than .30?)

o   Alpha if item deleted (can we relevantly improve reliability by deleting items?).

Based on these criteria, decide whether to continue with the scale, to delete some items or do away with the scale altogether.

-          WHERE?
- Analyze > Scale > Reliability Analysis
- You can click “Statistics” here and ask for Descriptives for scale, items and scale if item deleted.

Create scales

-          WHAT?
Create the scales that you have just determined to be sound measurements of the variables you are measuring.

-          HOW?
By adding the different items you have decided do belong together, or (preferably) computing the means of these sets of items. The advantage of using the means is that the scale will have scores between 1 to 5, and that you are able to compare the scores on different scales. Otherwise, you couldn’t compare the mean score on a scale consisting of 6 items (with scores ranging from 6 to 30) with the mean score on a scale with 8 items (with a range from 8 to 40).

-          WHERE?
Transform > Compute

o   The “target variable” is the scale you are creating (for instance, “kshare”).

o   The “numeric expression” would be the formula to create this scale, e.g.

§  Mean (kshare1, kshare2, kshare3,…, kshare8)

Test theory

The previous steps have left you with a dataset in which all the key concepts are represented by scales that are valid measurements of these concepts. You now have a scale for each relevant concept, and of course the first few questions in the survey that provide you information on functions, departments, length of employment etc. You can now use these scales and other variables to test assumptions about differences and relationships.

Test for differences

-          WHAT?
It is interesting to see whether (for instance) people fulfilling different positions in the organization score differently on relevant variables. Do high level experts share more knowledge than administrative personnel? Do managers have a more positive view of the organization than non-managers? Does IT have a more positive perception of IT performance than the business side?

-          HOW?
For differences between two groups: Perform a t-test. If you’re comparing two groups, choose the independent samples t-test. The grouping variable is the variable based on which you make groups – for instance, function, where group 1 is managers and group 2 is non-managers. The test variables are the variables for which you want to test whether the groups score differently (for instance, knowledge sharing). Pay attention to the following criteria in the output:

o   F-value and significance of F-value (undere “Levene’s test for Equality of Variances”). This value tells you nothing about the actual differences found, it basically tells you in which row of the output you should look for results. What’s tested here, is whether the variances of the scores of both groups on the test variable are equal or not. If they’re not, SPSS is extra cautious with a number of things. If the significance of this F-value is lower than .05, this means that the variances differ significantly – and that you can not assume that there are equal variances, in other words, you should look in the bottom row of the output. That’s where you look for the following statistics.

o   T-value: gives an indication of the strength of the difference.

o   Degrees of freedom.

o   Significance of t-value: this is the decisive statistic. If the t-value is significant, this means that there is a significant difference between the groups – in other words, that (for instance) high level experts share more knowledge than administrative personnel.

-          For differences between more than two groups: Perform an analysis of variance (ANOVA). The “Factor” is the variable determining your groups (for instance, “department”), the “Dependent List” contains the variables for which you want to test whether there are differences between these groups (for instance, knowledge sharing). Pay attention to the following criteria in the output:

o   F-value and significance of F-value. For this test, this is the decisive statistic: If the F-value is significant, this means that you have found a significant difference between the groups on this variable. Look at the descriptives (you can click that in the menu for the ANOVA) to get more insight into the actual nature of these differences.

o   The F-value and descriptives don’t provide a definitive insight into which of the groups you distinguish differ from each other. In order to be able to determine exactly which departments (for instance) score higher or lower than the other ones, click Post Hoc. Then, you can choose from a number of post hoc tests that do tell you which groups differ from each other. Common post hoc tests are LSD, Bonferroni and Tukey. LSD is relatively simple, as it performs a number of t-tests between each of the groups. If the F-value is not significant, doing a Post Hoc test does not make much sense. Then, the conclusion is that there are no differences.

-          WHERE?

o   T-test: Analyze > Compare Means > Independent Samples T-test

o   ANOVA: Analyze > Compare Means > One-way ANOVA

§  Don’t forget Post Hoc

Test for relationships between variables

-          WHAT?
Both theoretically and practically, you can have a lot of assumptions about relationships between variables. For instance, is a knowledge friendly organizational culture positively related to the level of relational social capital? Do the different dimensions of social capital explain the level of knowledge sharing in the organization?

-          HOW?
Correlations: If you’re only interested in the way variables are related, but not in the distinction between dependent and independent variables, you can compute correlations between these variables. Compute the Pearson correlation coefficient and see if the variables are positively related, negatively related, or not related at all. Pay attention to the following criteria in the output:

o   Pearson’s r: this tells you the strength and direction of the relationship. If this statistic is 1, there is perfectly positive correlation, if it is -1, there is a perfectly negative correlation, and if it is 0, there is no correlation. In practice, of course, it will lie somewhere in between these extremes. There are a number of different views concerning what constitutes a strong or weak correlation, but the leading criterion is:

o   The significance of this coefficient. If the level of significance is below .05 (p<.05), the correlation is significant and we can conclude that the two variables are related.

Regression analysis: Most of the time, you will be interested in the distinction between dependent and independent variables, and in the extent to which dependent variables are explained (or influenced) by these independent variables. Then, you will perform a regression analysis, which enables you to test a model like the following:

Typically , Y will be what you enter in the “dependent” box, and X1, X2 and X3 will be entered in the “independents” box. In a regression analysis, pay attention to the following criteria in the output:

o   (Adjusted) R-square. This tells you how much variance in the dependent variable is explained by the independents in your model. For instance, an R-square of .48 means that 48% of the variance in the dependent is explained by your model. There is some discussion whether you should look at the R-square itself or the Adjusted R-square, but since the latter is a more conservative estimate it is preferable to choose the Adjusted R-square.

o   F-value and significance of F-value. Here, the F-value tells you whether the proportion of variance explained by your model is significant. In other words, whether the (Adj) R-square is significant, if the model has enough explanatory power to be valuable. If the F-value is not significant, that means it makes no sense to continue your analysis and you have to design a different model.

o   Betas and significance of Betas. The Beta can be compared to the correlation coefficient, and tells you how strong the relationship between your independent and dependent variable are, and what direction it has (positive or negative). Typically, Beta values will lie between -1 and 1, and the most important thing here is whether the Beta value is significant (p<.05).

-          WHERE?

o   Analyze > Correlations > Bivariate

o   Analyze > Regression > Linear

Moderation & Mediation Analysis in SPSS

a.     analyze>descriptives>save as standardized values (select the independent and moderating variable)

b.     transform>compute (calculate the product of the 2 standardized variables)

c.      analyze > regression > linear (select your dependent variable, insert the independent and moderating variable in step 1, click next, and add the product in step 2)

d.     Is the best of the product significant? Then there is moderation.

Mediation

a.     Is there an association of the independent variable with the mediator? (analyze >regression > linear; independent variable is inserted as independent variable and mediator is added as dependent variable)

b.     Is there an association of the mediator with the dependent variable? (analyze > regression > linear; mediator is inserted as independent variable and the dependent variable is added as dependent variable)

c.      Is there an association of the independent variable with the dependent variable? (analyze > regression > linear; independent variable is inserted as independent variable and dependent variable is added as dependent variable)

d.     Does the association of independent and dependent variable reduce significantly (partial mediation) or disappear (full mediation) in case the mediator is added? (analyze à regression à linear; independent variable is inserted as independent variable and dependent variable is added as dependent variable in step 1; click next, and add the mediator in step 2)

More video tutorials on SPSS

For more information and video tutorials on SPSS go to SPSS student movies and view the following flash movies (these videos run more smoothly when you download them first onto your hard drive):

o  Entering Data
o  The Syntax Window
o  Transforming Data
o  Regression
o  One-Way Independent ANOVA

As a leader, whenever you delegate a task, you need to make it clear what level of authority you are conferring to others:

• Level 1: Do exactly what I have asked you to do. Don’t deviate from myinstructions. I have already researched the options and determined what I want you to do.

• Level 2: Research the topic and report back. We will discuss it, and then I will make the decision and tell you what I want you to do.

• Level 3: Research the topic, outline the options, and make a recommendation. Give me the pros and cons of each option, but tell me what you think we should do. If I agree with your decision, I will authorize you to move forward.

• Level 4: Make a decision and then tell me what you did. I trust you to do the research, make the best decision you can, and then keep me in the loop. I don’t want to be surprised by someone else.

• Level 5: Make whatever decision you think is best. No need to report back. I trust you completely. I know you will follow through. You have my full support.

The problem is that my mentee thought he was delegating at Level 2. The person on his team assumed he had given him Level 4. The whole problem could have been avoided by clarifying the expectations on the front end. If you’re not sure which level of delegation your boss meant, ask him to clarify.

Always make sure it’s clear to both parties which level of delegation is expected to avoid conflict.

I really enjoyed the process of the research as preparation for the final master thesis. The field of M&A presented to be a fascinating world! Enjoy the read or skip to the management matrix to find how you can best manage acquisition experience in the pre-formation given your level of current acquisition experience.

Managing the influence of previous M&A experience on potential synergies in the pre-formation process.

Introduction

While the intuitive expectation is that M&A experience would be positively related with M&A performance the literature shows mixed results (Haleblian et al., 2010; King et al, 2003) and frequently fails to positively relate acquisition experience to acquisition performance (Haleblian & Finkelstein, 1999). While some acquirers with acquisition habits successfully perform acquisitions to gain market power, it seems that generally acquirers fail to realize this potential from acquisition experience (Hayward, 2002). The academic literature on acquisition experience suggest different moderating influences on the possible relation between acquisition experience and firm performance. This makes theoretical integration and the applicability for managers difficult.

The acquisition process is moderated by many conditions that can influence the final acquisition performance (Haleblian et al.,2010). It is also suggested that there might be an existence of unidentified variables that may explain the significant variance in post-formation performance (King et al., 2003). These unidentified variables could have an influence on the effect of acquisition experience on performance and be the cause of the mixed research results.

I argue that the mixed results in previous acquisition experience research are due to the broad scope used. Most papers analyze the formation process as a whole without a clear distinction between pre- and post-acquisition process and those that focuses on the post-formation process and performance still shows mixed results due to the complexities and interdependent variables in the integration stage (Larsson & Finkelstein, 1999) and unidentified variables that influence the post-formation process (King et al., 2003).

Most of the existing literature on acquisition experience analyze the performance or success in the post-formation process, while the influence in the pre-formation process has been largely ignored. In this paper I answer the following research question: How does acquisition experience influence the potential synergy realization in the pre-formation process, and how can this be managed?

This paper looks at the influence of acquisition experience on the success in the pre-formation process and contributes by explaining how this can be managed in practice. The dependent success variable is not defined as performance, but as the potential synergy realization. By doing this the inconclusive influence of other variables mainly in the post-formation process can be excluded and with this new scope it’s possible to arrive at new insights on acquisition experience in this pre-formation process.

Where previous large-sample studies provide limited new insights into the factors that influence M&A outcomes (Healy et al., 1992) I use qualitative research to study the effect of acquisition experience in the pre-formation process in more detail. Applying the existing theories to the pre-formation process and combining it’s predictions with qualitative research from 12 interviews that were held with M&A consultants and managers with M&A experience about the pre-formation process.

This paper starts by analyzing the existing literature and potential moderators on the acquisition experience effect. Combined with the findings from the interviews a conceptual model is developed in which I argue that acquisition experience can be managed to realize higher synergy potential. This is connected further with finding from the interviews to develop a management matrix which managers can use to analyze the effects and the aspects of acquisition experience in the pre-formation process given their level of acquisition experience. Inexperienced acquirers can use the matrix to analyze opportunities on how to escape from the negative effects of low acquisition experience.

Theoretical background

Pre-formation

This paper focuses on the pre-formation part of a merger or acquisition. The pre-formation process starts from the first step of intent or research into an acquisition target till the point where the final contract is signed. After the signing of the contract the post-formation (integration) process starts.

Potential synergy realization

The M&A literature on acquisition experience presents a couple of ways to approach the dependent ‘success’ variable. The dependent variable in acquisition experience research is often approached as: acquisition performance (Heleblian & Finkelstein, 1999; Haywards, 2002), announcement returns (Haleblian & Finkelstein, 2002), or even as part of the performance of a whole acquisition program (Laamanen & Keil, 2008).

The existing M&A literature on acquisition experience often looks at the (stock) performance in the post-formation process. Whether it’s at day 0 at the announcement, half a year into the integration or final results of the post-formation process. Healy et al. (1992) argue that stock price performance studies are not able to determine the gains of an M&A or identify the sources of these gains. I argue that by focusing on the success measures of the pre- and post-formation stages independently, the sources of these gains can be identify better and that the influence of unidentified moderating variables (King et al., 2003) in the post-formation process can be left out. The success of the pre-formation process does not guarantee success in the post-formation process which is greatly dependent on drivers of post-formation integration (Epstein, 2004).

Figure 1. Integrative Merger & Integration model

In this paper the dependent ‘success’ variable in this pre-formation process is defined as the potential synergy realization. These potential synergies arise in the pre-formation planning. The potential synergy realization is comparible with the combination potential where post-formation process starts with and has a direct and non-direct effect on the final realized synergies (Larsson & Frinkelstein, 1999).

Organizational learning

To make predictions of the influence of acquisition experience in the pre-formation process a organization learning perspective is used to analyze the existing acquisition experience literature.

The organizational learning perspective explains how organizations learn form direct experience and even from experience of others. According to this perspective organizations learn by encoding history into their routines which in turn guide behavior. Experience is encode into conceptual frameworks and the likely hood these routines are used increases as they are associated with success (Levitt & March, 1988).

Acquisition experience

Haleblian & Finkelstein (1999) find that the effect of prior acquisition experience on the performance of acquisitions is U-shaped instead of linear and that it can be positive as well as negative. Using a behavioral learning theory from psychology they explain this effect. Firms start with a baseline at their first acquisition, but due inexperience make inappropriate generalizations of their acquisition experience at following dissimilar acquisition. Once firms gain sufficient acquisition experience they start to appropriately discriminate between acquisitions increasing performance again.

Laamanen & Keil (2008) suggest that acquisition experience has a more complex relationship with acquisition performance. From a acquisition program-level perspective they find that acquisition experience has a negative direct effect on acquisition performance, but a positive indirect moderation effect on the negative effects of high acquisition rates and variability of the rates. Acquisition programs can benefit from experience by learning which capabilities they have to manage an acquisition, the optimal number of firms to acquire, timing and the type of firms to acquire.

Other authors (Zello & Winter, 2002; Hayward, 2002) argue that experience alone is not sufficient and doesn’t ensure acquisition performance on its own. Zollo & Winter (2002) find no direct relation between experience and acquisition performance, but state that codification of experience leads to higher acquisition performance. Especially for inexperienced acquirers codification can moderate the negative effects of generalization due to high heterogeneity in task experience.

Hayward (2002) finds that prior acquisition performance is positively related with acquisition performance, if the target isn’t too similar or dissimilar to the previous acquisition and if it’s not temporally too close or distant from the previous acquisition. Experience per se is insufficient, but also necessary for acquisition learning. With an organizational learning perspective Hayward (2002) formulated that organizations must learn how to select the right acquisition and that this relates to the quality and not the quantity of the firm’s experience.

Although it seems intuitive that M&A experience would be positively related to M&A performance the literature shows mixed results. Acquisition experience is presented both as a independent variable with a positive and negative influence on performance as well as a moderator that moderates effects on performance (Haleblian et al., 2010). These unclear relations make managing acquisition experience to realize a positive effect difficult. I argue that these mixed results are due the broad scope, unidentified variables in the formation process as a whole and difficulty in measuring the influence on acquisition success with financial performance measures.

Overall the literature finds many interrelated variables and given the various circumstances acquisition experience could be managed to result in a direct, indirect or U-shaped positive effect.

Target similarity

The theory on acquisition experience suggests that target similarity is an important moderator that influences the effect of acquisition experience. This moderator fits with the organizational learning perspective.

The more similar a target is to the acquirers prior targets, the better the acquisition performs. Haleblian & Finkelstein (1999) find a positive relation of target similarity with acquisition performance. Hayward (2002) finds that acquisition performance is positively related with acquisitions that are not highly similar or dissimilar to the focal acquisition.

This effect is especially important for inexperienced firms that can make wrong generalizations of their acquisition experience due to high heterogeneity in task experience (Zollo & Winter , 2002). Target similarity especially helps inexperienced firms to moderate the negative effect of wrongly generalizing previous acquisition experience to unrelated current targets. While highly experienced firms are better able to discriminate between acquisitions.

Theoretical framework

Figure 2. Theoretical framework

Using an organizational learning perspective combined with the existing literature acquisition experience will be gained by trail-and-error and when successful, this experience is turned into routines of the organization and thus has a positive effect on the potential synergy realization.
Within this perspective acquisition experience is positively related to the ability to judge targets and estimate synergy expectations. Intuitively a higher level of acquisition experience leads to better and more careful pre-merger planning which creates these potential synergies. With experience its possible to identify better acquisition targets and manage the pre-formation process better than with less experience. Trough organizational learning the organization and its managers learn from M&A experience and become better at managing the pre-formation process. Without experience it will be hard for managers to realize high potential synergies. Learning seems to play an important role in realizing this positive effect.

This effect is moderated by target similarity. As target similarity increase the on the acquisition experience effect becomes more positive and increases this effect. Target similarity has the strongest moderating effect on firms with low acquisition experience.

Here target similar is the similarity to the previous target. This can also be targets that are similar to the firm, but from a organizational learning perspective I argue that similarity to the previous target and experience with these processes is important. Thus the concept of industry familiarity is approached more in a process way where experience is gained, than in a knowledge perspective of having knowledge of the target industry compared to the own industry.  Whether the target is similar to the firm or not doesn’t matter form a learning perspective.

Results

The findings from the interviews confirm the developed theoretical model on the influence of acquisition experience in the pre-formation process. A positive effect of acquisition experience was found, while problems that arise due to inexperience were also found in the interviews. Target similarity is also found to be related with acquisition experience and can be managed to stimulate its positive effect.

Importance of experience in the pre-formation process

The interviews indicate that acquisition experience is very important in the whole pre-formation process. The more experience the bigger the change to do everything right without making mistakes. Experience helps to oversee the processes and helps to know what to say and when. Firms with experience indicate that they use this information to avoid failures. Without acquisition experience it would be hard to realize potential synergies. Experience is so important in the pre-formation process that some indicated the need to hire and external advisor to access acquisition experience.

Level of acquisition experience

Acquisition experience in the pre-formation process is managed in different ways depending on the level of acquisition experience.

High levels of acquisition experience are used by managers in the pre-formation process to be realistic about the potential synergies and in which areas these are possible. Firms with high levels of acquisition experience create and use benchmarks and detailed planning to estimate the potential synergies. Another important factor found in the interviews is speed. Acquisition experience helps to increase speed by getting to negotiations quicker and the ability to accesses agreements.
The interviews show that managers create benchmarks and checklists based on their experience to make the pre-formation process more efficient and to estimate the potential synergies. Over time these firms with many previous acquisitions and high levels of acquisition experience develop a rigid process to manage and benefit from this experience.

Low levels of acquisition experience are not familiar with all the necessary processes which will make the pre-formation process long and difficult. With higher levels of experience they would be able to go through the process quicker and know when to get to making the deal.

Firms who lack acquisition experience try to access experience from outside the company. An external advisor or consultancy is approached to advice during the pre-formation process or to do certain parts of the process where the firm lacks experience. The processes of Due Diligence is often conducted by external consultancies.

Target similarity

Experience with a similar type of target increases the efficiencies and potential synergies that can be realized in the pre-formation process. Target similarity makes this process easier, because the departments and specific methods that are evolved are familiar, thus enabling the use of previous experience. Many of the steps in the pre-formation process are the same and some, for example industry evaluation, don’t need to be done again. Experience in evaluating the value of assets for similar firms can also save times and money spend in the pre-formation.

The interviews also indicate that the influence of similarity is low for firms with high levels of acquisition experience, because of the strict acquisition process that was developed to prevent wrong generalizations.

Management matrix

The results of the different levels of both acquisition experience and target similarity and how these are managed are translated into a management matrix.

Figure 3. Acquisition experience management model

This matrix can be used by managers in the pre-formation process to analyze the effects of acquisition experience given their level of experience. Firms that find negative effects can escape these negative effects by increasing target similarity or by using the suggested management processes. Inexperienced firms can learn from the management processes that experienced firms used to reap the benefits of acquisition experience.

Firms with low acquisition experience see little or even a negative effect of experience due to wrong generalizations. Without accessing external experience in the form fo an advisor or consultancy it will be hard to realize synergies. By increasing target similarity this negative effect can be moderated.

Firms with high acquisition experience benefit the most form acquisition experience and trough learning developed processes that enables them to increase efficiency and speed in the pre-formation process. Target similarity mainly effects firms with low acquisition experience, but can also help experienced firms to increase their pre-formation process efficiencies.

Conclusion

By focusing the scope on the pre-formation process and applying a organization learning perspective combined with the existing literature on acquisition experience I was able to develop a theoretical framework that predicted the positive effect of acquisition experience on potential synergy realization. This effect is indeed found to be moderated by target similarity. In the interviews I found empirical evidence of the positive effect of acquisition experience, the importance and how the acquisition experience gets managed to benefit from. From a organizational learning rationale I argue that low levels of experience that are not moderated by target similarity can experience a negative effect that becomes positive as experience increases.

Acquisition experience is found to be an important aspect in the pre-formation process, which can be managed. Increasing acquisition experience through organizational learning has a increasing positive effect on the potential synergy realization in the pre-formation process. Firms that lack acquisition experience will have more difficulties to realize potential synergies and more often access external acquisition experience by hiring a consultancy for more parts of the process. Firms with higher levels of acquisition experience were found to create processes that increase the efficiency during the pre-formation process and their ability to estimate potential synergies.

Target similarity moderates the positive effects of acquisition experience mostly for firms with low levels of acquisition experience. These firms can especially benefit from target similarity where some parts of the pre-formation process can be similar and can help them to increase efficiency.

The effects of acquisition experience and target similarity for each high and low level used to develop an acquisition experience management model. This model can be used by managers to escape from the negative effects of low acquisition experience or to find the effects and management methods that can be used given their level of experience and similarity of their current target. Firms with low levels of experience can escape these negative effects by increasing target similarity. For each quadrant a set of management practices is suggested that firms were found to use to reap the most benefits of their acquisition experience.

References

Epstein M. J., 2004, The drivers of success in post-merger integration, Organizational Dynamics, Volume: 33, Issue: 2, Pages: 174-189

King D. R. , Dalton R., Daily C. & Covin J., 2004, Meta-analyses of post-acquisition performance: indications of unidentified moderators, Strategic Management Journal, Volume 25, Issue 2, pages 187–200

Laamanen T. & Keil T., 2008, Performance of serial acquirers: toward an acquisition program perspective, Strategic Management Journal, Volume 29, Issue 6, pages 663–672

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Brands should start to use social mashups to leverage social media beyond just participating in conversations on external social networks. Integrating conversation mashups surrounding a brand on the website  can create trust like reviews do for products. Mashups can bring the conversation to the website and give fans a voice or stimulate them to participate in the conversation. Creative campaign types of mashups -that are useful for the customers- can also function to increase traffic.

The new Samsung website enabling social media conversations

“Samsung approached JESS3 with a challenge common to other Fortune 500 companies: how do we not just include social media icons on our homepage, but really, truly leverage and integrate what social media has to offer.

In response, JESS3 worked with Samsung to create a first-of-its-kind homepage which includes a series of deep social media features ranging from a user generated driven homepage “hero” image, to a conversation tool that allows visitors engage with key Samsung employees and accounts through suggested handles and hashtags.”

Great to see a big brand like Samsung leveraging the strengths of social media by integrating it on the frontpage of their website. Enabling visitors to discover product reviews and engaging fans on the website to participate in the conversations on the social web. For now unfortunately only on their US website.

Asos social media sentiment and influentials mashup

Asos is very active in stimulating their online community and also make use of mashup. Asos reviews gives the customer (and Asos themselfs) an impression of the current sentiment surrounding their brand on Twitter and displays these messages. New customers can get an impression of the happiness of others with the service Asos provides and can help them in their Purchase Funnel decision process.

Asos follows fashion is a platform for fashion fans to follow influentials in the fashion world on Twitter. By creating such a mashup platform Asos can position their brand as knowing about fashion and meanwhile network with the influentials in their sector. It’s useufll for their current customers and can attract new fashion fans that use the mashup or heared about it to the Asos brand and website.

Two examples of brand who are fully leveraging the strength of social media by integrating it on their websites. Do know more examples? Share them in the comments below.

Skyhook Wireless is a software-only location system that quickly determines device location with 10 to 20 meter accuracy. It uses it’s worldwide database of mapped Wi-Fi access points to provide quick and accurate location results where GPS and Cell tower triangulation drop the ball. Shyhook is relatively unknown by customers, but the driving power behind modern location services and included in many mobile devices from big brands.

The battle for location data has started recently with Apple and Google moving into Skyhook’s turf.  Skyhooks seem to be entering some interesting times and is well prepared to defend their turf.

What Skyhook basicly does is getting data on people who are walking outside and discover what they do when they leave the house. In the video the CEO says: “We know where everybody is, but not where you are” and “No one ever had this much location data”. Currently they are trying to discover the value in the data besides the application to location services.

Skyhook seems interested in delivering statistics to outdoor advertising agencies based on the mobile devices that pass by their advertisements. Outdoor advertising agencies currently have ways to estimate how many people walk by their billboards, but are very limited and have no further data. This could mean a new revolution in outdoor advertising in a way that most weren’t expecting. It’s time for Posterscope and Skyhook to get in touch with each other, if they haven’t already.

When Levi’s launched their Friends Store they were the first webshop to fully integrate Facebook social tools to create a personalized social shopping experience. Now Urban Outfitters and other webshops seem to be making some first steps by integrating Facebook Like buttons to their product pages and offering overviews of the most liked products. The benefit compared to old Top Rated functionality is that customers can discover if their friends also like a certain product.

The social shopping experience only starts to really work when the amount of Like’s on product pages increases and your network of friends starts using the function. I think in Europe this approach to social shopping will not be as feasible, but can make nice interactive Most Liked overview pages for webshops to attract some social media attention and traffic.

By publishing my thesis in parts on my blog i received much feedback in the reactions and on twitter. Special thanks to Michiel Hartman for pointing out that the evolution of web 1.0 to 2.0 hasn’t been a linear process.

You can download the ‘The alignment of web 2.0 and social media with business strategy’ (van der Weel, A. T. ,2009) pdf here.

For those who want to read the short summery and conclusions I included them here at the end of the blogpost. I also added the references for those who are interested in finding more academic research papers on social media and business strategy.

Research questions

Corporations trying to adopt and use web 2.0 and social media applications in their business, raises the question of how organizations can use these in extend to their strategies.

• How should web 2.0 and social media be approached and how can organizations align these web 2.0 and social media developments with their organization strategy?

To answer this question the following sub questions need to be answered:

• What is web 2.0 and social media and what are good definitions?
• How can social media be approached and used by organizations?
• How can social media be aligned with the business strategy of organizations?

Short summary

I expanded existing academic theory on ‘Possible uses of the World-Wide Web for business’ by adding customer service as the main new opportunity offered by web 2.0 and social media.

My literature research identified two frameworks that can be used to analyze web 2.0 and social media. One is the ‘Technology-Centric view of IT in Business’ (Dhar & Sundararajan, 2007), which presents technological invariants and consequences in business. This framework can be used for fundamental analysis of the building blocks and consequences of web 2.0 and social media.

The ‘Strategic alignment model’ (Henderson & Venkatraman, 1993) indicates the importance of a functional and strategic fit between business and IT. Together with the the ‘Value Disciplines’ framework (Treacy & Wiersema, 1993) these frameworks can be used to align organization business strategy with social media and web 2.0.

The Value Discipline that express the business focus on organization or department level can be used to guide the focus of the social media strategy. While the thesis mainly looks at the business strategy of the organization as a whole, it can also be used for departments to align their social media strategy.

In the end the researched frameworks come together to present my ‘social media governance framework’ which looks at the internal and external dimensions and the usage channel of social media in the organization. It can be used to align new social media projects with business strategy or to analyze the strategic fit of current social media projects in the organization by placing them in the matrix.

Conclusions

Web 2.0 was identified after the dot-com crash and is a collection of technologies, services and platforms. Key to business in web 2.0 is the ‘harnessing of collective intelligence’ often also called the ‘power of the crowd’. Social media can be defined as ‘the collective goods produced through computer-mediated action on web 2.0 services and platforms’. When focusing on the business uses social media encompasses these services and platforms that can be used by organizations. Social media and web 2.0 are greatly driven by modularity and result in the creation of large IT platforms and computer-mediated spaces.

Like internet, social media can be used for: Publicity, marketing and advertising, direct online selling, research and development, communication, collaboration and customer service. E-services helped identify the customer service path as a new business use after the dot-com crash and rise of web 2.0. Enterprise use of social media mostly focuses on reaping benefits from internal communication and collaboration cross traditional organizational hierarchies. Social media can be approached from an inward(internal) e-commerce perspective or from an outward(external) e-service perspective e-strategies depend on the approach.

For investments in social media to be effective corporations need to have an alignment between social media (IT) and business strategy. Also the strategic fit between the internal and external IT domain needs to be considered. The social media governance framework I present helps identify how different types of organizations should deploy social media applications depending on the purpose and business strategy in the internal or external domain. Alignment between the social media (IT) and business strategy occurs by using the value disciplines of an organization to formulate social media strategies. I argue that operational excellence organizations should have a focus on internal use of social media and that customer intimacy organizations should have a focus on the external use of social media. With the growing importance of social media in organizations, social media should become part of IT governance in organizations top management to support the alignment.

References

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• Cockburn, C. and Wilson, T. D (1996). Business Use of the World-WideWeb. Vol. 16, No. 2, pp. 83-102.
• Dhar, Vasant and Sundararajan, Arun (2007). Information Technologies in Business: A blueprint for education and research. Information systems research, vol 18, No. 2, pp. 125-141.
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• Henderson, J.C. and Venkatraman, N. (1999). Strategic alignment: Leveraging information technology for transforming organizations. IBM systems Journal 38, 2/3 pg. 472.
• Lai, Vincent S. and Wong, Bo K (2005). Business types, e-strategies, and performance. Communications of the ACM, May Vol. 48, No. 5
• O’Reilly, Tim (2007). What Is Web 2.0: Design Patterns and Business Models for the Next Generation of Software Communications & Strategies no. 65, 1st quarter.
• Hoegg, Roman, Meckel, Miriam, Stanoevska-Slabeva, Katarina and Martignoni, Robert (2006). Overview of business models for Web 2.0 communities, Proceedings of GeNeMe: 23-37
• Kochan Thomas A., Useem, Michael (1992). Transforming organizations. Oxford University Press US http://books.google.nl/books?id=qU96bV4OtdgC.
• Raghupathi, W. RP (2007). Corporate governance of IT: A framework for development. Communications of the ACM , August Vol. 50, No. 8.
• Rai, Arun, Patnayakuni, Ravi and Patnayakuni, Nainika (1997). Technology Investment and Business Performance. Communications of the ACM, July Vol. 40, No. 7.
• Rust, Roland T. and Kannan, P.K. (2003). E-service: a new paradigm for business in the electronic environment. Communications of the ACM, June Vol. 46, No. 6
• Smith, Marc, Barash, Vladimir, Getoor, Lise and Lauw, Hady W. (2008). Leveraging Social Context for Searching Social Media. SSM‟08, October 30, Napa Valley, California, USA.
• Tracy, M. and Wiersema, F. (1993). Customer intimacy and other value disciplines. Harvard Business Review 71 (1), Jan/Feb, pp. 84-93
• Valacich, Joseph and Schneider, Christoph (2009). Chapter 6 Enhancing Collaboration Using Web 2.0 in Information Systems Today: Managing the Digital World, Prentice Hall; 4 edition (March 13)

Internal and external business approach to the electronic environment

The strategic alignment model states that strategy must be addressed in an Internal and external domain. The research that follows on electronic strategies and business types also indicate organizations can have an internal or external focus.

E-service: a new paradigm for business in the electronic environment

Rust & Kannan (2003) argue that the traditional path of e-commerce has largely failed after the dot-com crash, and that organizations must learn to embrace the e-service paradigm that can offer new forms of competitive advantage. Putting the firm in a position attending to the needs of the customer by providing software service in addition to selling products

The transformation of physical products to service products is enabled and hasted by electronic networks. For firms to garner long-term customer relationships they must take full advantage of the e-service opportunities that these electronic networks can offer. They predict that product-centered orientated firms that resist the call of the customers for control are not likely to survive in this electronic environment and give the example of record labels.

The traditional path is focused on automation and efficiency to reduce costs. While the e-service path is focused on enhancing service and building profitable customer relationships to increase revenues. There is a shift form focusing on technology and systems towards focusing on understanding the customer. While the new path offers new forms of competitive advantage both can still be used by organizations to perform successfully.

The electronic environment enables both this inward and outward-looking view of e-commerce. The e-service paradigm identifies a new use of internet for improved customer satisfaction as opposed to increased efficiency and productivity. The e-service paradigm takes advantage of the nature the online environment with its flows of information to learn about customers, communicate and engage in long-term relationships to build customer equity (Rust & Kannan, 2003). E-services identify customer service as an important new approach of businesses in electronic environments.

According to Rust Kannan (2003): “Dell Computers is a good example of how a firm selling products in an increasingly commodity market can follow an e-service orientation to build its customer equity.“ Organizations as IBM, HP and Sun are also undergoing this shift and increasingly focus on their services as prime revenue source.

Business types, e-strategies, and performance

After the dot-com crash IT developments continued shaping towards web 2.0 and new services emerged (Hoegg et al. 2006) creating new business opportunities (Rust & Kannan, 2003)  and new e-strategies emerged. Lai & Wong (2005) mention that the companies that survived the dotcom crash needed to device innovative e-strategies and restructure around new business models. After reengineering efforts some companies recuperated from the debacle.

In their research they look at companies after the EC crash and investigate the influence of different business types(B2B & B2C) and non-EC on the company performance. Also the e-strategies from the different companies are identified and their effectiveness correlated with the company performance.

The results indicate a moderating the relationship between business type and e-strategy performance.

Findings indicate that many EC companies used four different types of e-strategies. Savings-related strategies were most effective for B2B and B2C companies, while marketing-related strategies were only effective for non-EC companies. The business type that benefited the most from the e-strategies were B2B companies and also performed better than their B2C counterparts.

Conclusion: How to approach social media

E-strategies further help identify how organizations approach the electronic environment and which strategies they use. The four main types of e-strategies are identified as: savings-related, structure-related, policy-related and marketing-related. These identified strategies can also be subdivided between internal and external strategies. Savings, structure and policy-related are inward focused strategies while marketing-related strategy is focused outwards.

Rust and Kannan (2003) identify two different paths of e-commerce. The traditional path focused on automation and efficiency to reduce costs and the e-service path focused on enhancing service and building profitable customer relationships to increase revenues (Rust & Kannan, 2003). The traditional e-commerce path and e-service path help identify two approaches of businesses to the electronic environment. One focused inwards on processes and the other focused outwards on customer satisfaction. Organizations can adopt an internal as well as an external focus.

Both papers indicate the existence of an internal and external focus for organization strategies and are in line with the strategic alignment model that was presented and help identify how different organizations can use social media and web 2.0 in their (electronic) strategies.

I argue the same strategies can be used when formulating strategies for the use of social media in organizations, because social media operates in the same electronic environment on web 2.0 services and platforms. Also the e-strategies theory can be connected to e-commerce or e-service paths by making a distinction between internal and external focus.

Functions of web 2.0

Possible uses of the World-Wide Web for business

Having answered what web 2.0 and social media is and developed definitions for both, now the possible use of social media by organizations can be researched. Cockburn and Wilson (1996) present a list of applications for which businesses are using the WWW. While their paper was written at the beginning of the internet era, it can still be used to identify the purposes and goals of businesses on the internet. They identified the following application areas:

Publicity, marketing and advertising:  the WWW appears to be an ideal medium for businesses to promote themselves and their products. Gaining access to millions of people can be achieved by a small fraction of the costs of conventional methods.

Direct online selling: can revolutionize the way in which people shop. Virtual shops and catalogues are available on the web where customers can directly buy products.

Research and development: especially companies involved in research and development can use the internet as an additional resource for collecting information. Many databases are available and discussion boards that can be searched and queried for information and data.

Communication: use of low-cost electronic mail(and other ways of online communication )that allows many companies to link together and communicate internal and external  in an easy and fast way.

Collaboration: fast and easy forming of links and collaboration over the internet instead of using private (network) links.

After the dot-com crash new web business characteristics were identified as web 2.0 (O’Reilly, 2007) and businesses had to understand the new rules for success on that new platform (Valacich & Schneider, 2009). The survey was done in 1996 before the dot-com crash and new developments have led to new approaches and use of the internet. Modern web 2.0 business uses like e-commerce can be seen as part of Direct online selling. Crowd sourcing that makes use of the collective intelligence of web 2.0 (O’Reilly, 2007) can be seen as part of Research and development and Collaboration.

Internal enterprise use of social media focuses reaping the benefits from internal collaboration and communication and makes use of web 2.0 services to achieve this. The free broadcasting platform that social media offers, helps to circumvent traditional organizational hierarchies (Brzozowski et al. 2009).

While the list of applications for which businesses use the internet still is valid, I argue that one business application is missing that was identified after the dot-com crash. Customer service is the main new business application that originated from the paradigm shift after the dot-com crash (Rust & Kannan, 2003) and should be added to the list. Zappos.com is an example of a company that mainly focuses on using internet to offer customer service, with the use of a blog, chat and other messaging services. These are used as a support function on their e-commerce services that are used for direct online selling.

Conclusion: How organizations can use social media

The possible uses of the Internet for business were identified by Cockburn and Wilson (1996) in their survey that presented a list of applications for which businesses were using the internet. I identified one main application that was missing and updated the list. The business uses of internet are: Publicity, marketing and advertising, direct online selling, research and development, communication, collaboration and customer service.

The list of possible business applications of the internet helps identify the use of social media for organizations. I argue that these applications can also be applied to the use of social media that operates on top of web 2.0 services and platforms. Web 2.0 and social media that operates on top of it offer new opportunities, but I argue that the same goals on the web remain. One of the new opportunities that arrived with the rise of web 2.0 and e-services is customer service that can be a goal of businesses on the internet, and was added to the list.

These possible uses of the internet overlap with the possible uses of social media. Only now web 2.0 and social media has really enabled the possibilities to achieve these uses and goals on the internet while at the start of the internet era the possibilities were still limited. These uses will be important for the model that is presented later on in this paper.