Australian Accounting Standards


Corporate Accounting

They have asked you and your colleagues to sample a selection of listed companies, and establish the extent to which companies comply in detail with the spirit of the law. Management are curious about the compliance in regard to several particular areas of consolidations.


Your assignment is divided into two (2) parts. Part 1 is the analysis of a sample of annual reports and Part II.

You are to form into groups of two for the submission of this assignment

Part 1 (approx. 500words)

You are required to select three companies from the Australian Stock Exchange (ASX).

You are then to obtain copies of those companies’ latest annual reports and investigate the consolidation practices of these companies. That is:

  1. Review the most recent financial statements of three Australian firms from different industries. Analyse, compare and discuss the firms’ disclosure of consolidation practices to explore both firms’ compliance with relevant Australian Accounting Standard AASB10 and support your arguments using relevant research literature.

Part II (approx 1500 words)

‘Financial capital maintenance ensures that profit and distributions of profit for an entity, including a group, do not derive from the mere act of acquiring another entity’s assets or acquiring an equitable interest in that other entity’s net assets (Knapp 2013, p.192.)’

(Source: Knapp, J. 2013. A reconsideration of consolidation accounting requirements and pre-acquisition dividends. Australian Accounting Review. Vol. 23 No. 3, pp. 190-207.


(hint: After reading the article by Knapp, identify the key terms, define them, what do you believe to is the main problem, and compare the cost and ‘new’ consolidation approach.)

Total Approximate word Length: 2000 words for two member groups.

Marking: This assignment is worth 20% and will be scored out of 20 marks. The assignment is a TEAM assignment and therefore scores will be applied to all members of a team without distinction.

Due Date: Week 9

You have to submit TWO copies of your report:

  • Only single copy should be submitted to Turnitin via a link on VU Collaborate. The report should be submitted ONLY by one team member or team leader.
  • The second (hard copy) of the report should be submitted to your lecturer / tutor / assignment box.

Penalty for late assignments: 1 mark per working day (weekday) and no mark will be awarded after five days.


  • Team members must belong to the same tutorial group.
  • It is YOUR responsibility to form teams, thus swapping contact details and arranging regular progress meetings is essential. Remember, once a team is formed, it is up to you and your fellow team members to maintain team discipline. No changes to group members are allowed during the last three weeks before the submission deadline. All group members are equally responsible for the submission of the assignment.
  • Any person signing up in a group needs to obtain approval from other group members PRIOR to signing up. In case there are complaints that a student signed up without prior approval from other group members, he/she will be removed from the group immediately. All complaints need to be submitted via email (using VU student email)
  • Groups must assign a leader and keep all records of meetings and tasks assigned to group members. Students must meet at least three (3) times face-to-face but more often by electronic means. Meeting records must be attached at the end of your report. Marks will be allocated for your meeting logs. A suggested meeting log is attached.
  • Where a group member is not cooperating or contributing to the group and the team has made all efforts to resolve the issue, the team must inform the tutor and unit coordinator/s immediately via email. Where a group does not report this matter immediately, it will not be entertained later on, especially before the due date of the assignment. The defaulting member will be given a warning and should the member continue to default, the group will continue without the defaulting member and the defaulting member will receive zero for the task.

Referencing and style

  • Assignment must be typed using Word document and double-spaced with a normal margin (i.e. 3cm)
  • The required referencing style is Harvard (Please visit VU library for examples of Harvard Referencing Style at

Some useful journals include:
Academy of Accounting and Financial Studies journal

Academy of Accounting and Financial Studies proceedings

Accounting and business research

Accounting and finance (Parkville)

Accounting and the public interest

Accounting, auditing, & accountability

Accounting, auditing & accountability journal

Accounting forum

Accounting horizons

Accounting in Europe

Accounting, organizations and society

Accounting perspectives

Accounting research journal

Accounting review

Accounting standard

Accounting today

Advances in accounting

Advances in Accounting, Finance & Economics

Advances in international accounting

African journal of accounting, economics, finance and banking research

Asian Academy of Management Journal of Accounting & Finance

Asian journal of finance & accounting

Asian review of accounting

Asia-Pacific Management Accounting Journal

Australian accounting standard

Bank accounting & finance

British accounting review

Canadian accounting perspectives

Contemporary accounting research

Critical perspectives on accounting

European accounting review

International accounting bulletin

International journal of accounting

International journal of accounting, auditing and performance evaluation

International journal of accounting information systems

International journal of digital accounting research

International journal of intelligent systems in accounting, finance & management

Irish accounting review

Issues in accounting education

Issues in Social & Environmental Accounting

Journal of accounting & economics

Journal of accounting and finance research

Journal of accounting & organizational change

Journal of accounting and public policy

Journal of accounting, auditing & finance

Journal of accounting research

Journal of Accounting, Business & Management

Journal of bank cost & management accounting

Journal of business finance & accounting

Journal of contemporary accounting & economics

Journal of corporate accounting & finance

Journal of Financial Reporting & Accounting

Journal of international accounting, auditing & taxation

Journal of international accounting research

Journal of international financial management & accounting

Journal of management accounting research

Journal of modern accounting and auditing

Journal of public budgeting, accounting & financial management

Journal of theoretical accounting research

Malaysian Accounting Review

Pacific accounting review

Public accounting report

Qualitative research in accounting and management

Quarterly journal of finance and accounting

Quarterly Journal of Finance and Accounting

Research in accounting regulation

Review of accounting & finance

Review of accounting studies

Review of quantitative finance and accounting

Student No:


Assessment Criteria and Assignment Structure

Assessment Structure (marks) Max mark Your


Excellent Very Good Good Average Marginal Poor Very Poor
  1. Part I Analysis of 3 companies (3x4marks)
  1. Introduction and Conclusion
  1. Part II: Discuss the concept of capital maintenance
  1. Comparison of the cost Versus ‘new’ consolidation method (AASB 10)
  1. Conceptual problems in applying standard
  1. Bibliography, referencing and citations
  1. English expression, coherence, grammar and spelling
  1. Evidence of the group work-meeting logs (3×2)
  1. Total /40
  1. Total /20%
Posted on

Big Data Patents (Digital Intellectual Property Law)

Article By Sandro Sandri 


Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. The term “big data” often refers simply to the use of predictive analytics, user behaviour analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.1 “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.”

In another way Big Data is an evolving term that describes any voluminous amount structured, semistructured and unstructured data that has the potential to be mined for information. It is often characterized by 3Vs: the extreme Volume of data, the wide Variety of data types and the Velocity at which the data must be processed. Although big data doesn’t equate to any specific volume of data, the term is often used to describe terabytes, petabytes and even exabytes of data captured over time. The need for big data velocity imposes unique demands on the underlying compute infrastructure. The computing power required to quickly process huge volumes and varieties of data can overwhelm a single server or server cluster. Organizations must apply adequate compute power to big data tasks to achieve the desired velocity. This can potentially demand hundreds or thousands of servers that can distribute the work and operate collaboratively. Achieving such velocity in a cost-effective manner is also a headache. Many enterprise leaders are reticent to invest in an extensive server and storage infrastructure that might only be used occasionally to complete big data tasks. As a result, public cloud computing has emerged as a primary vehicle for hosting big data analytics projects. A public cloud provider can store petabytes of data and scale up thousands of servers just long enough to accomplish the big data project. The business only pays for the storage and compute time actually used, and the cloud instances can be turned off until they’re needed again. To improve service levels even further, some public cloud providers offer big data capabilities, such as highly distributed Hadoop compute instances, data warehouses, databases and other related cloud services. Amazon Web Services Elastic MapReduce is one example of big data services in a public cloud.

Ultimately, the value and effectiveness of big data depends on the human operators tasked with understanding the data and formulating the proper queries to direct big data projects. Some big data tools meet specialized niches and allow less technical users to make various predictions from everyday business data. Still, other tools are appearing, such as Hadoop appliances, to help businesses implement a suitable compute infrastructure to tackle big data projects, while minimizing the need for hardware and distributed compute software know-how.


The General Data Protection Regulation, which is due to come into force in May 2018, establishes a few areas that have been either drafted with a view to encompass Big Data-related issues or carry additional weight in the context of Big Data, lets analyse just two aspects.

– Data processing impact assessment

According to the GDPR, where a type of processing in particular using new technologies, and taking into account the nature, scope, context and purposes of the processing, is likely to result in a high risk to the rights and freedoms of natural persons, the controller shall, prior to the processing, carry out an assessment of the impact of the envisaged processing operations on the protection of personal data. This criterion is most likely going to be met in cases of Big Data analytics, IoT or Cloud operations, where the processing carries high privacy risks due to the properties of either technology or datasets employed. For example, linking geolocation data to the persons name, surname, photo and transactions and making it available to an unspecified circle of data users can expose the individual to a higher than usual personal safety risk. Involving data from connected IoT home appliances or using a Cloud service to store and process such data is likely to contribute to this risk.

– Pseudonymisation


According to the GDPR, ‘pseudonymisation’ means the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person. At least two aspects link pseudonymisation to Big Data. First, if implemented properly, it may be a way to avoid the need to obtain individual consent for Big Data operations not foreseen at the time of data collection. Second, paradoxically, Big Data operations combining potentially unlimited number of datasets also makes pseudonymisation more difficult to be an effective tool to safeguard privacy.


Big data has increased the demand of information management specialists so much so that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP and Dell have spent more than $15 billion on software firms specializing in data management and analytics. In 2010, this industry was worth more than $100 billion and was growing at almost 10 percent a year: about twice as fast as the software business as a whole. Developed economies increasingly use data-intensive technologies. There are 4.6 billion mobile-phone subscriptions worldwide, and between 1 billion and 2 billion people accessing the internet. Between 1990 and 2005, more than 1 billion people worldwide entered the middle class, which means more people became more literate, which in turn lead to information growth. The world’s effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 20073 and predictions put the amount of internet traffic at 667 exabytes annually by 2014. According to one estimate, one third of the globally stored information is in the form of alphanumeric text and still image data, which is the format most useful for most big data applications. This also shows the potential of yet unused data (i.e. in the form of video and audio content).

2 “Data, data everywhere”. The Economist. 25 February 2010. Retrieved 9 December 2012.

3 Hilbert, Martin; López, Priscila (2011). “The World’s Technological Capacity to Store, Communicate, and Compute Information”. Science. 332 (6025): 60-65. doi:10.1126/science.1200970. PMID 21310967.


While many vendors offer off-the-shelf solutions for big data, experts recommend the development of in-house solutions custom-tailored to solve the company’s problem at hand if the company has sufficient technical capabilities.


A patent is a set of exclusive rights granted by a sovereign state to an inventor or assignee for a limited period of time in exchange for detailed public disclosure of an invention. An invention is a solution to a specific technological problem and is a product or a process. Being so, Patents are a form of intellectual property.

A patent does not give a right to make or use or sell an invention.5 Rather, a patent provides, from a legal standpoint, the right to exclude others from making, using, selling, offering for sale, or importing the patented invention for the term of the patent, which is usually 20 years from the filing date6 subject to the payment of maintenance fees. From an economic and practical standpoint however, a patent is better and perhaps more precisely regarded as conferring upon its proprietor “a right to try to exclude by asserting the patent in court”, for many granted patents turn out to be invalid once their proprietors attempt to assert them in court.7 A patent is a limited property right the government gives inventors in exchange for their agreement to share details of their inventions with the public. Like any other property right, it may be sold, licensed, mortgaged, assigned or transferred, given away, or simply abandoned.

The procedure for granting patents, requirements placed on the patentee, and the extent of the exclusive rights vary widely between countries according to national laws and international agreements. Typically, however, a granted patent application must include one or more claims that define the invention. A patent may include many claims, each of which defines a specific property right.

4 WIPO Intellectual Property Handbook: Policy, Law and Use. Chapter 2: Fields of Intellectual Property Protection WIPO 2008

A patent is not the grant of a right to make or use or sell. It does not, directly or indirectly, imply any such right. It grants only the right to exclude others. The supposition that a right to make is created by the patent grant is obviously inconsistent with the established distinctions between generic and specific patents, and with the well-known fact that a very considerable portion of the patents granted are in a field covered by a former relatively generic or basic patent, are tributary to such earlier patent, and cannot be practiced unless by license

thereunder.” – Herman v. Youngstown Car Mfg. Co., 191 F. 579, 584-85, 112 CCA 185 (6th Cir. 1911)

6 Article 33 of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS).

7 Lemley, Mark A.; Shapiro, Carl (2005). “Probabilistic Patents”. Journal of Economic Perspectives, Stanford Law and

Economics Olin Working Paper No. 288. 19: 75.


relevant patentability requirements, such as novelty, usefulness, and non-obviousness. The exclusive right granted to a patentee in most countries is the right to prevent others, or at least to try to prevent others, from commercially making, using, selling, importing, or distributing a patented invention without permission.

Under the World Trade Organization’s (WTO) Agreement on Trade-Related Aspects of Intellectual Property Rights, patents should be available in WTO member states for any invention, in all fields of technology,9 and the term of protection available should be a minimum of twenty years.10 Nevertheless, there are variations on what is patentable subject matter from country to country.


European patent law covers a wide range of legislations including national patent laws, the Strasbourg Convention of 1963, the European Patent Convention of 1973, and a number of European Union directives and regulations in countries which are party to the European Patent Convention. For certain states in Eastern Europe, the Eurasian Patent Convention applies.

Patents having effect in most European states may be obtained either nationally, via national patent offices, or via a centralised patent prosecution process at the European Patent Office (EPO). The EPO is a public international organisation established by the European Patent Convention. The EPO is not a European Union or a Council of Europe institution.[1] A patent granted by the EPO does not lead to a single European patent enforceable before one single court, but rather to a bundle of essentially independent national European patents enforceable before national courts according to different national legislations and procedures.[2] Similarly, Eurasian patents are granted by the Eurasian Patent Office and become after grant independent national Eurasian patents enforceable before national courts.

8 Lemley, Mark A.; Shapiro, Carl (2005). “Probabilistic Patents”. Journal of Economic Perspectives, Stanford Law and Economics Olin Working Paper No. 288. 19: 75. doi:10.2139/ssrn.567883.

9 Article 27.1. of the TRIPs Agreement.

10 Article 33 of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS).


European patent law is also shaped by international agreements such as the World Trade Organization’s Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPs Agreement), the Patent Law Treaty (PLT) and the London Agreement.


11 Patent Analytics Solutions That Help Inventors Invent”, Outsell Inc, June 3 2016

Patent data is uniquely suited for big data tools and techniques, because of the high volume, high variety (including related information) and high velocity of changes. In fact, patents are leading the way with big data and analytics in many ways. “The patent space offers a fascinating insight into the potential of big data analytics, rich visualization tools, predictive and prescriptive analytics, and artificial intelligence”.11 Especially recently, big data tools and technologies are being used in several ways in the patent world to transform and improve patent analysis.

Patents and Intellectual Property are gradually gaining significance around the world. This is leading to a bottleneck-large databases and ever growing information. A new way around the innovation problem is to acquire patents. With examples such as Nokia, Motorola, Twitter, the patent purchases seem rather straightforward. Nokia sold a large chunk of its company to Microsoft, but held on to the crucial patents by signing a licensing deal. They can now earn a revenue using patents licensed to Microsoft. Google bought Motorola and its patents and later sold the company to Lenovo while holding on to the patents. There are ample such examples in the industry.

Transactions of Intellectual Property (IP) are rather complex. Per example, a basic component to be verified before a patent is granted, is novelty. In other words, if a priorart describing the invention is found, the application stands to be rejected. A prior-art could be in the form of a publication, a blog post, a lecture, a video, or a book. With a massive amount of information generated, that doubles every 18 months, it is extremely difficult to found prior-art. One way, some organizations follow, is crowdsourcing the prior art search. Details about the patent are published on a website asking IP professionals from around the world to find a prior-art. The emergence of Big Data analytics, on the other hand, has provided a clear solution. In addition, the outcomes through this method get better and precise with each operation.

Since Big Data analytics is still not commonly used by most government authorities, prior-art gets overlooked and many false patents are granted. This comes out when-in litigation-the opposing parties put all their efforts in looking for a prior-art to invalidate each other’s patents. More often than not, a prior-art is found or there is an out of court settlement. Hence, a concept called patent wall has gained traction. It is very common for companies to file as well as acquire a number of patents around the technology they are working on. This serves as a defence against litigators and allows the companies to market and sell their products/services without any fear of litigation.

The core value of patents is that the invention must be publicly disclosed in exchange for a time-limited monopoly on the invention. Patents are not only a legal asset that can block competitors, they are potentially a business and financial asset. For market participants, patents can provide direct insight into where competitors are headed strategically.

Big Data is the key to unlocking this inherent value. Patent information is comprised of vast data sets of textual data structures involving terabytes of information. When unlocked through Big Data techniques and analysis, the insights are compelling, revealing the direction a technology is headed and even uncovering the roadmap for a specific company’s product plans. But, deriving these insights from the proliferation of information requires truly sophisticated Big Data analysis.

While Big Data is quickly growing as a trend, what’s delivering more value these days are Big Data services that optimize specific data sets and create specialized analysis tools for that data. Technology teams that are dedicated to certain data sets will curate and improve the data, learn the specifics of that data and how best to analyze it, and create selfservice tools that are far more useful than generic Big Data technologies.

A key part of the Big Data service is a specialized analysis engine tailored to particular data. For example, a patent analysis engine must understand the dozens of metadata items on each patent in order to group patents correctly and traverse the references. To be most effective, Big Data services need to automatically keep up with the data updates, as patents are living documents that change over time. Even after the patent Big Data Patents is finalized and issued, it can be reclassified, assigned to a new owner, reexamined and updated, attached to a patent family or abandoned.

Most importantly, Big Data services are only as good as the insights they deliver – a Big Data service should provide a specialized user interface that allows real-time, userdriven analysis with search, correlations and groupings, visualizations, drill down and zooms. The patent data analysis must be presented in a manner that is compelling and consistent.

There are more than 22,000 published patent applications between 2004 and 2013 relating to big data and efficient computing technologies, resulting in almost 10,000 patent families. Patenting activity in this field has grown steadily over the last decade and has seen its highest increases in annual patenting over the last two years (2011-2012 and 2012-2013) of the present data set. The growth has continually been above the general worldwide increase in patenting, showing a small increase of 0.4% over worldwide patenting for the 2005-2006 period and showing a maximum increase of 39% for 2012-13.~

“Using” a patent effectively means suing a competitor to have them blocked access to market, or charge them a license for allowing them to sell. When a patent holder wishes to enforce a patent, the defendant often can invoke that the patent should not have been granted, because there was prior art at the time the patent was granted. And, while patent offices do not seem to have a clear incentive to take into account actual reality, including the exponentially available information created by Big Data, when reviewing the application, the situation is very different for a defendant in a patent lawsuit. They will have every incentive to establish that the patent should never have been granted, because there was pre-existing prior art, and the information in the patent was not new at the time of application. And one important consequence of Big Data will be that the information available to defendants in this respect, will also grow exponentially. This means that, the probability of being able to defend against a patent claim on the basis of prior art, will grow significantly. Because of the lag of time between patent applications and their use in court, the effect of the recent explosion of information as a result of Big Data is not very visible in the patent courts yet.

A patent is, of itself, an algorithm. It describes the process of a technical invention – how it works (at least, that’s what a patent is theoretically supposed to be doing). It is therefore quite possible that a lot of algorithms around analysis of Big Data will become patented themselves. It could be argued that this will act as a counterweight against the declining value and potential of patents.

Many of these algorithms are, in fact, not technical inventions. They are theoretical structures or methods, and could therefore easily fall into the area of non-patentable matter. Algorithmic patents are particularly vulnerable to the ability by others to “innovate” around them. It is quite unlikely that a data analysis algorithm would be unique, or even necessary from a technical point of view. Most data analysis algorithms are a particular way of doing similar things, such as search, clever search, and pattern recognition. There is, in actual fact, a commoditization process going on in respect of search and analytical algorithms. Patents are “frozen” algorithms. The elements of the algorithm described in a patent are fixed. In order to have a new version of the algorithm also protected, the patent will either have to be written very vague (which seriously increases the risk of rejection or invalidity) or will have to be followed up by a new patent, every time the algorithm is adapted. And the key observation around Big Data algorithms is that, in order to have continued business value, they must be adapted continuously. This is because the data, their volume, sources and behaviour, change continuously.

The consequence is that, even if a business manages to successfully patent Big Data analytical algorithms, such patent will lose its value very quickly. The reason is simple: the actual algorithms used in the product or service will quickly evolve away from the ones described in the patent. Again, the only potential answer to this is writing very broad, vague claims – an approach that does not work very well at all.

80% of all big data and efficient computing patent families (inventions) are filed by US and Chinese applicants, with UK applicants accounting for just 1.2% of the dataset and filing slightly fewer big data and efficient computing patents than expected given the overall level of patenting activity from UK applicants across all areas of technology.

Against this, however, it should be borne in mind that many of the potential improvements in data processing, particularly with regard to pure business methods and computer software routines, are not necessarily protectable by patents and therefore will not be captured by this report. UK patenting activity in big data and efficient computing has, on the whole, increased over recent years and the year-on-year changes are comparable to the growth seen in Germany, France and Japan.12

12 Intellectual Property Office, Eight Great Technologies Big Data A patent overview



ï‚· Herman v. Youngstown Car Mfg. Co., 191 F. 579, 112 CCA 185 (6th Cir. 1911)

ï‚· Hilbert, Martin; López, Priscila (2011). “The World’s Technological Capacity to

Store, Communicate, and Compute Information”. Science. (6025).

ï‚· Lemley, Mark A.; Shapiro, Carl (2005). “Probabilistic Patents”. Journal of

Economic Perspectives, Stanford Law and Economics Olin Working Paper No.


ï‚· Springer, New Horizons for a Data-Driven Economy –

ï‚· “Data, data everywhere”. The Economist. 25 February 2010. Retrieved 9

December 2012.

ï‚· Eight Great Technologies Big Data – A patent overview, Intellectual Property


ï‚· “Patent Analytics Solutions That Help Inventors Invent”, Outsell Inc, June 3 2016

ï‚· Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS).

ï‚· Article 33 of the Agreement on Trade-Related Aspects of Intellectual Property

Rights (TRIPS).

ï‚· 75. doi:10.2139/ssrn.567883.

ï‚· TRIPs Agreement.

ï‚· WIPO Intellectual Property Handbook: Policy, Law and Use. Chapter 2: Fields of

Intellectual Property Protection WIPO 2008

SWOT Analysis on Google’s Inc. approach to Culture Management


1. Evaluate the overall strengths and weaknesses of Google’s approach to culture management in relation to its vision and strategies (30% of total available marks)

2. Referring to the last section in the case on ‘Weaknesses in the Google Approach’, evaluate the specific problems identified by Sullivan and suggest ways that they might be dealt with in the future. Collectively, do they add up to a significant problem for Google (50% of total available marks).

3. What lessons can other companies learn from Google’s talent management practices? To what extent are they transferable to other contexts and time periods? (20% of total available marks)

A Look inside Google’s Talent Management Machine

Google Inc. is a US-based corporation that is best known for developing a widely-used internet search engine, e-mail system, online mapping, office productivity, social networking and video-sharing services. It earns its revenues from linking advertising to these services as well as selling advertising-free versions of them to clients seeking corporate solutions to their technology problems. The Google headquarters is located in its Googleplex at Mountain View California, but it has recently opened offices in New York, Ann Arbor and Pittsburgh in the USA, and in Europe, Asia and Australia. Since being founded by Larry Page and Sergey Brin in 2006 and going to public offering in 1998, it has grown remarkably to become one of the world’s most powerful brands and has been identified quite a few times as one of the ‘Best Places to Work’ by Fortune Magazine.   By December 2008 the company had 20,222 full-time employees.

Google, through its branding, PR and recruiting efforts, has made itself so well-known and attractive to professionals from every industry and university in the USA and in other countries in which it has offices that they have essentially changed the talent management ‘game’ of recruiting forever. Google attracts more than a million applicants per year for approximately 10,000 vacancies, which will double its size in a single year.   Because of its growth, vision and need for highly talented people it has developed a ‘recruiting culture’ and among the most innovative HR practices in the world, at least according to John Sullivan, a San Franciscan academic who studies them closely. The following paragraphs are a précis of Sullivan’s insights in Google’s people management and talent management practices coupled with insights into Google from the Economist and other sources.

Google current vision, as articulated by Brin in December 2008, is to make the world’s information ‘universally accessible and useful’. In addition, Google has recently established, its philanthropic arm. Its specific mission, through both for-profit investing and making charitable grants, is to ‘eclipse Google itself in overall world impact by ambitiously applying innovation and significant resources to the largest of the world’s problems’.

Google’s Talent Strategy – ‘the world’s first recruiting culture’

These visions of the future colour all aspects of Google’s sometimes ‘quirky’ management and leadership practices, no more so than talent management. According to Sullivan, Google has ‘accomplished something that no other corporation has ever accomplished’ to deliver the company’s ambitious aims.   Since 1998, it has developed a ‘recruiting machine’ to create a talent management culture that permeates the entire organization. This applies from senior leaders to the entry-level employees and has largely been done at the behest of the two founders, whose vision touches virtually all aspects of Google. Talent management, however, does not only refer to recruitment, which is obviously a key function in a rapidly growing company, but also to changing the ways in which employees work to ‘attract and retain the very best’.

‘Working with 20 per cent time’
Sullivan points out that ‘many organisations have changed their pay or benefits in order to attract better workers, but few have changed every professional job in the company so that the work itself is the primary attraction and retention tool’. Rather than leaving work design, job design and job descriptions in the hands of out of touch people in corporate compensation, Google’s two founders, HR director Stacy Sullivan and the leadership team at Google have designed all professional job and features of the workplace so that all employees are working on interesting projects, learning continuously and are being constantly challenged. Without using the terms, Google is striving to become a learning organization ‘par excellence’.
The key element of changing the work so that the work itself becomes a critical attraction and retention force and driver of innovation and motivation is what Google calls ‘20 per cent work’. There is no concrete definition of what 20 per cent work means, but generally for professional employees it means working the equivalent of one day a week on their own, researching individually selected projects that the company funds and supports. This idea is not new, having been popularised by other innovative companies in the 1980s, including 3-M. However, it has been effective. Both the Google Groups and Google News products are reported to have started as a result of personal 20 per cent time projects. In addition to being an important attraction message for the kinds of people that Google seeks to recruit, it also keeps their attrition rate at, as one HR executive put it “almost nil”, (in reality around 5%) but its greatest value is that it drives innovation and creativity throughout the organisation.

‘The world’s largest recruiting budget’
Sullivan argues that Google recruiting is the ‘best-funded recruiting function in any major product-driven corporation’. The statistics he uses to evidence this are impressive and show how important this element of talent management is to a fast growing company in the technology sector. ‘Google recruitment has a ratio of one recruiter for every 14 employees (14:1). That ratio surpasses the average ratio of 100 employees to 1 HR recruiter in the USA.

‘The benefits are highly attractive’
Google’s attraction, motivation and retention strategy is based on ‘spectacular’ benefits, though they are not just designed for HR related reasons but to encourage collaboration, to break down barriers between functions and to stimulate individual creativity and innovation (see Table 1). The benefits package does attract and presumably helps retain a percentage of “wrong people”, that is, talented individuals who are seeking benefits rather than an opportunity to do their best work. The company recognises that such an employer of choice strategy is not without its problems, which creates a screening and retention challenge.

Table 1. Employee benefits at Google
A partial list of Google’s benefits include:·         Flexible hours for nearly every professional employee·         Casual dress everyday·         Employees can bring their dogs to work·         Onsite physician and dental care·         Health benefits·         Free massage and yoga

·         Stock options

·         Free drinks, snacks and meals, including breakfast, lunch and dinner

·         Three weeks’ vacation during the first year

·         Free recreation, including video games, foosball, volleyball and pool tables

·         Valet parking for employees

·         Onsite car wash and detailing

·         Maternity and parental leave (plus new mums and dads are able to expense up to US$500 for take-out meals during the first four weeks that they are home with their new baby)

·         Employee referral bonus program

·         Near site childcare centre

·         Backup childcare for parents when their regularly scheduled child care falls through

·         Free shuttle service to the main campus

·         Fuel efficiency vehicle incentive program

·         Onsite dry cleaning, plus a coin-free laundry room

·         A Friday TGIF all-employee gathering where the founders frequently speak

·         A “no tracking of sick days” policy

·         An onsite gym to work off all of the snacks



Google Recruiting Structure
Google has ambitious growth plans, which means doubling its workforce over the period of a few years. To achieve this aim the recruitment structure they have designed to enable such growth is, like most successful recruiting organisations, primarily a centralised operations model.

A key tenet of any successful recruiting function is that the function has the capability to handle in-house the most important and visible positions, that is, the search for future leaders. At Google, recruiting is responsible for filling both executive leadership and top-level technical positions, which makes it even more of a core function in delivering strategic aims.

According to Sullivan, ‘Because Google believes wholeheartedly in sourcing the best talent that is ferociously sought after by competitors, every element of the recruiting function is abundantly staffed with highly focused professionals’.

The recruiting model has been broken up into very distinct roles, each requiring specialised expertise. These activities, carried out in a highly choreographed manner by teams tied to divisions and business units, include: recruiting research analysts; candidate developers (‘sources’); process coordinators; candidate screeners; specialised recruiters for college; specialised recruiters for technical and leadership executive search; specialised international recruiters to be located in Asia and Europe; recruiting program managers; and recruiting project managers. Such specialisation enables the function to be managed in a way similar to a supply chain.

The company employs a range of standard and company specific recruitment tools, the latter of which it regards as key to its operations and something of a set of ‘signature’ practices.

Standard recruiting tools
Google has successfully implemented many of the standard best practice tools found at other companies:

  • Employee referral programmes: Google’s referral program is without any industry leading features, but the company’s strong brand coupled with its highly enthusiastic workforce makes up for weaknesses in the program.
  • University recruitment: Google hires a large number of PhDs on the premise that they enjoy exploring areas that no one else has explored. To accomplish this, they have developed a network of direct relationships with over 350 professors at major schools. In addition, Google has an outstanding internship program that has a very high conversion rate to permanent hires.
  • Professional networking: Google also effectively uses social networking groups like Linkedin and other live professional events to recruit top performers.
  • Recruiter training: Google is one of only a handful of companies that requires most newly hired recruiters to go through extensive recruiter training prior to starting.


Key ‘Signature’ Recruiting tools

Google employs a variety of impressive recruiting tools including:

  • AdWords as a recruiting tool: Because Google is recognised as the master of search, it’s not surprising that they utilise their own search tool to find top candidates without active resumes.
  • Contests as recruiting tools: The Google Code Jam, as they call it, is a global online software writing contest that can attract over 7,500 people each year. The top 25 finalists are invited to the Mountain View campus to compete for US$50,000 in prizes as well as a chance to work at Google.
  • Brain teasers as recruiting tools: Google has placed brainteaser billboards in the Silicon Valley and by Harvard Square. The math puzzles on these billboards challenge mathematics-oriented people and get them thinking. Although they do not specifically mention Google, the billboard puzzle does eventually lead interested participants to the Google site.
  • Friends of Google: This tool creates an electronic email network of people that are interested in Google and its products but not necessarily interested in working for the company. Thus, Google can build a relationship with thousands of people that like the firm.
  • Data-driven approach to candidate assessment. The latest innovation from is a new assessment tool that relies on an algorithm to more accurately identify candidates who resemble existing top performers. The algorithm enables Google to include candidates who might otherwise be overlooked as it evaluates a much wider range of potential success predictors than can normally be discerned from most resumes. It is argued that this innovation recognises and resolves a major flaw inherent to typical assessment methodologies that rely too heavily on academic grades, school scores, degrees from top schools, prior industry experience and subjective interview results.

‘Weaknesses in the Google approach’

Sullivan has recognised that Google recruiting is not without weaknesses, though qualifies this criticism by suggesting that they aren’t significant threats. However, in a company whose slogan is “great isn’t good enough”, he argues that it’s critical that HR and recruitment management spend some time and resources in the following areas:

  • Employment branding: Although Google is clearly well-known as a great employer, it is clear that much of that recognition has come as a result of programs and ideas that originated outside of HR. What can Google do to develop a distinctive employer brand?
  • Metrics: Both the HR and the recruiting function have dragged their feet on developing metrics to show that investment in talent management pays. He singles out on-the-job performance of new hires but flags up an important failing of HR generally, which is the failure to assess the effectiveness of what they do.
  • Recruiting strategy: Although Google recruiting obviously carries out lots of innovative recruiting activities, they seem to occur at random and in spite of the fact that there is no formal, well-communicated recruitment strategy.  Is there a need to formalise the informal? Or should they leave what works alone?
  • Speed: Like other companies where who you let in is critical, e.g. Apple and some of the management consulting companies, many candidates report on Google ‘comments’ how slow the screening, recruiting, and interview process is. How can this be speeded up, or should it be speeded up?
  • Contingent labour: The number of temporary workers and contractors in the recruiting function at Google is very high.  Google seems to be unwilling to give permanent jobs immediately to recruiters, which may reduce Google’s ability to employee experienced recruiters that look for a certain level of stability.
  • Emphasis on youth: Google’s emphasis on youth culture might hurt its ability to attract more senior and experienced personnel.



Question Instructions

  • These questions on how gender, culture and socioeconomic status impact on health will be discussed in the week 4 workshop (in class for internal students or online for external students).
  • Please use the answers discussed in your class AND your Health and Health Behaviour 130 textbook (Reading 4) for information on the impact of gender and culture on health, no other resources are required.
  • Cite and reference all information you use from the textbook. You do not need to cite ideas suggested in your class or your own opinions.
  • There are two parts to each question, ensure you address both parts of each question – examples of how gender, culture and socioeconomic status impact on health AND your opinion on how much is social structure and how much is social agency. You need to demonstrate your understanding of the concepts of social structure and social agency, don’t just say ’50/50′
  1. Describe examples of how gender impacts on health and wellbeing? Justify how much YOU think is human agency and how much is social structure? (Approx. 150-200 words)
  1. Describe examples of how culture impacts on health and wellbeing? Justify how much YOU think is human agency and how much is social structure? (Approx. 150-200 words)
  1. Describe examples of how socioeconomic status impacts on health and wellbeing? Justify how much YOU think is human agency and how much is social structure? (Approx. 150-200 words)

Workbook Activity (Week 5) – Psychological Influences on health and wellbeing

Weekly Learning Outcomes

  • Discuss how stress impacts on health and how it interrelates with social and biological determinants.
  • Discuss how the psychological processes of appraisal and coping can impact on health.

Textbook Reading

Reading 5: Understanding mind and body interactions

Question Instructions

  • As discussed in your textbook Reading 5 and the workshop for this week the way we think and feel are not separate from our physical wellbeing. This week’s workbook activity will require you to read the HHB130 textbook Reading 5 ‘Understanding mind and body interactions’ (in particular the case study ‘Martin O’Connor and Irritable bowel Syndrome pages 79, 83, 86) and answer the following three questions.
  • The case study brings together several concepts explored in Reading 5, don’t just look in the case study for answers, read the whole Reading and think about how what you are reading may apply to Martin.
  • You may need to apply your knowledge from week 4 and also week 8 (biological determinants) to answer these questions well.
  • Cite and reference all information you use from the textbook. You do not need to cite ideas suggested in your class or your own opinions. No other resources are required to answer these questions.
  1. Identify
    1. The psychological and physiological factors that may have contributed to Martin developing Irritable Bowel Syndrome (IBS) and
    2. How these factors can reinforce and maintain these symptoms .

(Approx 150-175 words)

  1. How will reducing Martin’s stress levels assist in improving his symptoms? Please consider the interactions between emotions and physical states when answering this question. (Approx 150-175 words)

Workbook Activity (Week 8) – Biological Influences on health and wellbeing

Weekly Learning Outcomes

  • Discuss the impact of the following biological influences on health: genetics, ethnicity, age, gender and physiological differences.
  • Discuss how the impacts of these are interrelated with each other and with environmental, social and psychological influences.

Textbook Reading

Reading 6: Human genetics and human inheritance: biological, social, cultural and environmental perspectives.

Question Instructions

  • The first four questions on epigenetics can be answered after viewing the 50 minute Horizon documentary ‘The Ghost in Your Genes’ which you can watch on You Tube (there are 5 parts to the documentary) which link to each other
  • The 5th question must be answered using your textbook Reading 6. No other resources are required. Remember to cite any information you use from the reading. You do not need to cite and reference the documentary
  1. List the environmental factors discussed in the documentary that may lead to a change in epigenetic “switches”? (Approx. 50 – 75 words)
  1. Discuss the psychological and social factors mentioned in the documentary that may lead to a change in epigenetic “switches”? (Approx. 50-75 words)
  1. What repercussions may this research have on individuals’ health behaviours? (Approx. 75-100 words)
  1. How may research into the field of epigenetics impact on the way health professionals work together? (Approx. 40-60 words)
  1. From your textbook Reading and class discussion describe multiple factors (social, psychological, environmental) that may influence an individual’s behaviour around predictive testing for a disease. (Approx. 100-150 words)

Workbook Activity (Week 9) – How we influence health behaviours

Weekly Learning Outcomes

  • Discuss the role of behaviour change theories and how they assist health professionals to influence health behaviour.
  • Apply a behaviour change theory to a health issue

Textbook Reading

Reading 7: Understanding health behaviour

Question Instructions

  • Select 1 (one) risky health behaviour from the list below and describe how this risky behaviour impacts on the health of Australians.
  • Name the health behaviour you have chosen at question 2.
  • Avoid the same behaviour as you may have discussed so you do not inadvertently self-plagiarise information.
  • You must continue with the same risky health behaviour for the remainder of the workbook activities, do not change to a different behaviour.
  • Please use at least 3 credible, academic resources (no websites, media, fact sheets) to answer this question and cite appropriately. Credible resources are also recent and relevant to the behaviour.
  • All references need to be on the last page of the workbook along with any other references (including textbook chapters) you have used in completing the workbook assessment.
  • You will be working on this activity in the tutorials in weeks 10-12 so you do not need to start it until then.
  • Your answer should include statistics as well as the discussion of the impact on health. Remember that the definition of health includes physical, mental, social and emotional health, so include what is relevant to the chosen health behaviour.
  • The beginning of Reading 7 in your textbook (revised edition) describes risk behaviours as background information for you.
  1. Choose a risky health behaviour from the list below:
Not following safe food hygiene practices Incorrect taking of medication
Unhealthy eating in adulthood Being physically inactive
Drinking alcohol in pregnancy Unsafe driving
Not wearing sun protection Not vaccinating children
  1. Which risky health behaviour have you chosen?

3. Describe how this risky behaviour impacts on the health of Australians. (Approx. 300 – 330 words)

Workbook Activity (Week 10) – How we influence the health behaviours of individuals and families

Weekly Learning Outcomes

  • Applying behaviour change theories, discuss strategies to change the health behaviours of individuals and families.

Textbook Reading

Reading 8: How to change health behaviour

Question Instructions

  • Select one model of health behaviour:
    • Health belief model
    • Theory of planned behaviour
  • Apply the selected behaviour change model to your chosen health behaviour from the week 9 activity. You could either apply it as a risky health behaviour or as a risk reduction or health promotion health behaviour e.g. the behaviour could either be drinking during pregnancy or not drinking during pregnancy.
  • You can apply the model as a drawing (figure) or in text.
  • For question 3, briefly discuss how you have applied the model to your behaviour (as if you were explaining the how the model relates to the behaviour to a friend). Your discussion should reflect that you have understood both the model and the health behaviour.
  • You will need to include 1 citation and reference in the discussion for the model of health behaviour (question 3). As the original references for these models are old or difficult to access you can use more recent research which has applied the model you have chosen. A folder with links to suggested articles in e-reserve will be in the unit materials for week 10 and the Workbook folder in Assessments. You do not have to use these articles; you can use another one you find on the Curtin library catalogue.
  1. Which behaviour change model have you chosen?

Health belief model

Theory of planned behaviour

  1. In the space below, apply this model of behaviour change to your chosen behaviour from week 8 (you will do an example in the workshop). You can represent this as a diagram or in sentence form.
  1. Briefly discuss how you have applied the health behaviour model to your health behaviour. You will need to cite the model of health behaviour (see above instructions for details). (Approx. 200 – 220 words)

Workbook Activity (Week 11-12) – How we influence the health behaviours of communities and populations

Weekly Learning Outcomes

  • Discuss models to influence health at the community level.
  • Discuss the aims, essential services and principles of public health.
  • Discuss the 5 actions of the Ottawa Charter and demonstrate how it can be used as the basis for an interprofessional approach to health interventions.

Textbook Readings

Reading 9: Community developments and partnerships

Reading 10: Introduction to health promotion

Reading 11: Interprofessional learning: working in teams

Question Instructions

  • Describe 2 different strategies for changing the risky health behaviour you have discussed in previous two workbook activities, ideally Australian strategies, but international strategies can be used (specify which community / country).
  • A strategy can include a health promotion campaign or intervention, advocacy, training, policy, community action etc.
  • Note that media e.g. advertising, is often part of a larger campaign, it is this larger campaign we are looking for as one of the two strategies (not two individual actions of the same campaign).
  • The 2 strategies need to be different from each other, run by different organisations and not from the same source.
  • For each strategy you need to briefly describe (in 250-300 words for each strategy):
    • Who the strategy targeted at.
    • What the strategy is about.
    • The actions of the strategy.
    • When the strategy was in action or if it is current.
    • How the strategy or actions of the strategy is attempting to change the behaviour.
    • Which of the 5 actions of the Ottawa Charter this strategy addresses.
    • The part(s) of the model of health behaviour you described in the week 10 workbook activity week this strategy addresses and how it is being addressed.
  • Use and cite credible websites, journal articles or government reports for this information. Avoid media, fact sheets, blogs or any source that is not credible.

Strategy 1 (Approx. 250-300 words):

Please write your references used in the Workbook B here in APA format.

Strategy 2 (Approx. 250-300 words):


(List all references used to complete this workbook in APA format on this page).

Additional Files:


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Sexually Motivated Serial Killers

Sexually Motivated Serial Killers

Sexual serial killers are usually very charismatic and can easily persuade their victims to go with them no matter the situation. They usually start as sex offenders. These types of serial killers are usually married and have children. They have a history of sexual and physical abuse during their childhood. It is thought that sexual serial killers have “mommy issues,” since their father had left when they were young, leaving the mother in charge of the caring. It is unknown if sexual serial killers were obsessed with pornographic images, but they enjoy living out their sexual fantasies that lead to murder. They usually only commit their fantasies on women, mainly prostitutes (Pakhomou, 2004).

There are two types of sexual serial killers, organized and disorganized with both usually leave a signature (Knight, 2007). Organized sexual serial killers plan their kills, by choosing a particular victim. As if they are stalking their pray. Watching their soon victims closely, choosing a particular kill destination, and a place to leave the dead body; usually in areas they know well. The disorganized sexual killers do not take the time to plan everything. They usually choose their victims on a whim. They tend to kill in areas that may be unfamiliar to them.

Sexual serial killers live in a world of negative and positive. The negatives do outweigh the positives, but it is uncertain how much or if this is even true. Sexual serial killers grew up with constant trauma, and being born of prostitutes (Knight, 2007). These types of killers tend to hide their “weaknesses.” They hide behind a mask of psychopathic narcissism of severe aggression.

Sexual serial killers enjoy inflicting pain upon their victims, they enjoy watching their victims struggle and cry for help. These killers “get off” on the pain they inflict, but many times, just the act of the torture that sexually arouses the killer. In some cases, the killer starts by molesting, raping, and then murdering their victims. These killers usually have obvious underlying sexual conflicts. Most of the time the killing itself is sexually gratifying (Schlesinger, 2004).

According to Schlesinger (2004), many sexually motivated killings are hard to define. First, there is no general definition to sexual serial killing. Second, many murders are labeled as sexually motivated, but actually are not, vice versa. Third, many statistics of the actual number of sexual murders have not been kept. Fourth, many of these offenders’ records are not accurate since these killers refuse to cooperate when it comes to talking about their history.

To the naked eye sexual killings seem to be motiveless, but theorists believe that there was some sort of motive. In the research, many theorists stated the motive was a seeking or revenge. Since many of these serial killers were brutally dominated growing up, they sought out women who tend to look similar to their mothers and wanted the sense of dominating them. It is still uncertain if this is true for motivation since it is often incomplete and inaccurate. These killers tend to lie and manipulate the people who interview them (Schlesinger, 2004).

Impulsive and Ritualistic

According to Hazelwood and Warren (2000), sexual crimes can be committed against a person, object, or an animal. Most of the sexual serial killers chose to act out their violent sexual fantasies against children, the elderly, or victims around their own age. These people usually only commit homosexual crimes or heterosexual crimes, they usually do not mix between the genders. These killers tend to feel no remorse or guilt after their sexual killing. They develop fantasies that seem complex, once the fantasy in their head no longer turns them on; they tend to want to act out their aggressionsbrain-share.

The impulsive sexual serial killer is the most common, but seems to be the less successful. The opportunistic and angry offenders tend to have the situation unplanned and usually have little sexual fantasy behind their motives. The impulsive sexual serial killer is motivated by their sense of entitlement and that everything within his or her environment is for the taking. This form of sexual serial killer tends to collect pornographic videos of a theme of bondage or rape. They prefer acting out on their lovers or choosing prostitutes as their victims, until that no longer fulfills them. This sort just wants control over their victims, but it is uncertain if the impulsive sexual killer is involved with paraphilic behaviors (Hazelwood et al, 2000).

The ritualistic sexual serial killer is lest common, but has more success and more difficult to identify. They hold the same motivation as the impulsive killer, such as anger, power, and control. This form of sexual serial killer has paraphilic behaviors; they tend to be interested in sexual fantasies that involve some sort of control (bondage, voyeurism, and sadism). They tend to be socially withdrawn from society, and when they do speak with others, they have a form of awkwardness in the social interactions. Many tend to be very charming and gregarious, to make it easier to charm their way into their victim’s lives. Many of these forms of sexual killers are much-respected members in their communities (Hazelwood et al, 2000).

Many of these sexual serial killers prefer similar victims. Meaning they hold similar characteristics. They prefer younger victims between the ages of 18 to 22, thin, and usually with shorter hair. Many sexual serial killers preferred victims that no one would be missing. They would recreate the situations from their sexual fantasies, and keep their victims as sex slaves until they no longer wanted them, then they would kill their victims. Many of these killers have no prior criminal record, and if they do, it is usually for sexual offenses, such as child molestation (Hazelwood et al, 2000).

Relationship to Victims

In some cases, the offender’s relationship to the victim is asymmetrical; it can involve stalking and possible voyeurism. The offender usually does not know their victim personally, and the victim does not know the offender. There are cases where the offender and victim have a mild relationship, meaning they maybe neighbors, work friends, or even have chatted at a bar. Sexual serial killers tend to kill within their own race. Many prefer victims in the average age of twenty-five, with male victims being in their younger twenties and females in their mid-twenties (Pakhomou, 2004).

According to Pakhomou (2004), there are three categories to victim relationships with the offender. The first is the no established relationship, which is the most common. This is also referred to as the ‘stranger.’ The victim just met the offender for the first time, both are not aware of each other’s backgrounds. They also know nothing of each other’s current life situations. The victims are usually picked up in a bar situation.

The second is the rudimentary relationship. This is also known as the ‘acquaintance.’ The victim and the offender have only known one another for a few days, maybe a short time longer. They are aware of each other’s whereabouts. They may even know a little bit about each other’s history, weather it may be true or not. This makes it easier to locate the victim, when the time comes for the sexual killing.

The third is the established relationship, also known as ‘correlative.’ This is where the victim and the offender have known each other for more than a few months, maybe even years. The offender and victim know a great deal about each other’s backgrounds. The offenders know the details where they work and hang out on a regular basis. This makes it easier to perform the sexual killings, since the offenders know more details.

Many of these victims were solicited to have sex with the offenders and/or lured by an offer of drugs. They prefer to kill people they do not have feelings for or complete strangers. Sexual serial killers never kill the ones they are close to; they may get violent sexually on their loved sexual partner. Their partner may think it is just normal “kinky” sex and not expect any true harm. Usually after the killer does have sexual relations with their sexual partner, the killer will leave the house in search of a murder victim to perform their true sexual acts for murder (Pakhomou, 2004).

Location Choice

It is believed from movies that sexual serial killers tend to kill mostly in places unknown to them. This is far from the truth; many tend to commit crimes in the areas that are well known to them. There are two types of sexual serial killers when it comes to location: the outdoorsmen, who perform their crimes exclusively in the outdoors. There is also the indoorsmen, as one can guess, performs their crimes indoors. A few are both the outdoorsman and the indoorsman (Pakhomou, 2004).

According to Snook, Cullen, Mokros, and Harbort (2005) sexual serial killers prefer to select victims near their homes. On average, the victim was picked up about 2 miles from the offender’s home. The body usually recovered about 15 miles from the offender’s home. The offender’s would hardly exceed more than 13 miles from home to picking up a victim. They also would not exceed over 25 miles for the body drop off point. When the killings become more, the bodies seem to move closer to the offender’s home.

The majority of sexual serial killers are organized killers; they tend to use vehicles to pick up their victims and to drop off the bodies. The disorganized killers rarely use vehicles and drop off the bodies near their homes, maybe in their own house. Many offenders learn from their past criminal acts. Some tend to venture as far as they can from their homes so not to be recognized in their own neighborhoods. The younger offenders have concentrated locations to commit their crimes and tend to kill closer to home (Snook et al, 2005).

It is found that the more intelligent sexual serial killers will travel farther away from their homes to drop of the body than ones who had lower IQs. Many of the higher IQ offenders are more organized in their planning on where to commit the act and to dispose of the body. The older ones tend to leave the bodies much closer to their homes.

Examples of Sexual Serial Killers in History

serial_killersPeter Kurten, also known as the Vampire of Dusseldorf (1913-1929), started as a child sadist. He would strangle and rape his female child victims, then after he would cut their throats. He would be charged with nine murders and seven attempts. He was said he would kill and rape anything that would move. The defense counsel described him as “the king of sexual delinquents, uniting nearly all perversions in one person, killing everything he found” (Castleden, 2005).

Fritz Haarman, Vampire of Hanover (1919), would go out at night picking up homeless and jobless boys. He would feed then as if they were his personal pets, but then would sexually abuse them. He would only murder a few of them when they did not expect it, only after biting their necks. After murdering the boys, he would then butcher them and cook their flesh, making pies out of them. Haarman trusted one other person to help him in selecting the victims, Hans Gans. It took a long while before police would look at Haarman as a suspect in the killings, for he was a trustable informant for the police department. It is said that Haarman raped and killed between 30 and 40 young boys (Castleden, 2005).

Gilles de Rais (1420-1440), at the age of sixteen he started to commit sexual killings. His victims were mainly young boys, with a few female victims. He would sodomize these boys before he would decapitate them. Most of all he enjoyed watching his servants butchering the children’s bodies. He was charged with at least 800 sexual murders of children (Castleden, 2005).

Albert Fish (1930s) was one of the oldest sexual serial killers. In over a period of twenty some years it is estimated that Fish sexually molested over 400 children. Fish admitted he committed so many sexual murders that he could not remember how many he committed. He was known to be found, by his son, naked and beating himself with a board covered in nails. Doctors took x-rays of his body and found he had inserted needles into his body and left them there (Castleden, 2005).

John Wayne Gacy (1968-1978), better known as Pogo the Clown who thought he was four different people. He freely admitted to sexually molesting, raping, and murdering 33 boys and young men. He noticed a sexual attraction to young men after he married his first wife in 1968 and started to become an aggressive homosexual. After he would molest and rape these young men, he would strangle or stab them to death. He then would hide the bodies in a crawl space in his house using a strong chemical to decompose the bodies faster (Castleden, 2005).

Fred and Rose West (1967-1994) a husband and wife sexual killer team who started sexually assaulting young girls. In most of the cases, they were cleared of charges because key witnesses would not testify. The West’s would cut off the fingers and toes of most of their child victims, when it came to start killing their victims. Their first known victim was Fred’s own eight-year-old daughter from his first marriage; they would tie, gag, and rape her. After that, they would begin their killing after realizing their victims were telling about the gruesome details they would be put through. There were a possible thirteen sexually murdered victims (Castleden, 2005).


There are many similarities with sexual serial killers. Many serial killers have an average IQ, they grow up with some sort of abuse in the home, and they have mother issues. It is rare that sexual serial killers are older than middle age; they usually are aged between the mid-twenties to early thirties. Sexual serial killers start by molesting children before they start adding murder to the equation. It is more common for sexual serial killers to be organized, since they can go many years before they become caught, they take time to think about where to pick up the victim, and what to do with the body.

The impulsive sexual serial killer is the more common of killers, since they pick out victims that would never be missed or young children and need to act when the victim is available. They tend to invite them to their house with charming, persuasive manners. This does not make sense since to be impulsive one has to be disorganized. It is not clear how a killer can be organized and impulsive. One thing is for sure, sexual serial killers hide behind a mask of terror, to hide from their childhood pain and dis-acceptance from their society.

Many sexual serial killers tend to know their victims without their victims knowing who they are. It is more common for the offender and the victim to be complete strangers, with the offender just knowing certain details from stalking. They tend to kill victims who are either their own race or whatever race is available to them. If they know their victim, they tend to be just violent sexually with them in acts of bondage. They would usually never kill ones they care for; many of their lovers never knew they were committing such acts.

It is proven that sexual serial killers find their victims not far from their residence. A few will travel away from home in fear of being caught. Sexual serial killers will hide the bodies closer to home than they travel to retrieve them. The bodies are usually only at farthest a few miles away from where they reside, the closest kept in their own home. It is rare to be attacked by a serial killer, but one thing is for sure, they are madmen and madwomen who hide themselves in darkness, thinking killing will rid of their demons.

 Read Also: Innocent Children Essay