Search results
1 – 10 of over 20000
The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.
Abstract
Purpose
The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.
Design/methodology/approach
This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.
Findings
The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.
Originality/value
The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.
Details
Keywords
Worachet Onngam and Peerayuth Charoensukmongkol
The purpose of this study was to analyze the effects of social media analytics on firm performance using a sample of small and medium enterprises (SMEs) in Thailand. This study…
Abstract
Purpose
The purpose of this study was to analyze the effects of social media analytics on firm performance using a sample of small and medium enterprises (SMEs) in Thailand. This study also investigated whether entrepreneurial orientation (EO) moderated the effects of social media analytics on firm performance.
Design/methodology/approach
This study used SMEs listed in the Department of Business Development of Thailand as the sampling frame. Probability sampling was used to draw the sample. A questionnaire survey was used to collect data from 334 firms. The data were analyzed using partial least squares structural equation modeling.
Findings
The results supported the positive association between social media analytics practices on firm performance. Moreover, this study found that EO moderated this association significantly. In particular, the positive association between social media analytics practices on firm performance was higher for firms that exhibit a high EO than those that exhibit a low EO. This result indicated that firms that implement social media analytics practices achieved higher performance when they exhibited a high EO.
Practical implications
Social media data analytics should be implemented to strengthen the technological competence of firms. Moreover, firms should integrate EO practices into their implementation of social media analytics to increase their ability to generate substantial improvements in their strategic implementation, thereby enabling them to gain sustainable competitiveness in their market.
Social implications
Because SMEs are the driving force for economic growth and development in Thailand, their ability to achieve higher performance when they effectively integrate EO practices into their implementation of social media data analytics could be beneficial for the sustainable development of Thailand, especially in the current data-driven era.
Originality/value
The result that EO moderates the effect in enhancing social media analytics practices’ influence on firm performance provides new knowledge that extends the boundary of research on this topic. The authors provided a theoretical explanation to clarify the way the implementation of social media analytics practices should be integrated with EO to increase the level of performance that firms achieve from such practices.
Details
Keywords
Employee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s workplace…
Abstract
Purpose
Employee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s workplace. Embedded in this is the growing expectation of the human resource (HR) function to understand how workforce analytics informs the business and fuels success. This paper aims to explore how the HR function can achieve this.
Design/methodology/approach
The evolution of the “Future of HR” and how it is moving from “descriptive and diagnostic” to “prescriptive and predictive.”
Findings
According to KPMG’s 2019 Future of HR survey: 37 per cent of respondents feel “very confident” about HR’s actual ability to transform and move them forward via key capabilities such as analytics and artificial intelligence (AI). Over the next year or two, 60 per cent say they plan to invest in predictive analytics. Among those who have invested in AI to date, 88 per cent call the investment worthwhile, with analytics listed as a main priority (33 per cent). Despite data’s remarkable ability to deliver news insights and enhance decision-making, 20 per cent of HR believe analytics will be a primary HR initiative for them over the next one to two years, and only 12 per cent cite analytics as a top management concern.
Research limitations/implications
Taking a page from meeting customer needs, innovative technologies such as AI and the cloud, data analytics can give an organization the potential to gather infinitely greater amounts of information about customers.
Practical implications
Today’s workforce analytics focuses mostly on what happened and why. For instance, you might have tools for identifying areas of high turnover and diagnosing the reasons. But thanks to advancements in technology and data analytics capabilities, HR is better-positioned to be the predictive engine required for the organization’s success.
Social implications
There has never been a better time for HR to create greater strategic value, as the potential for meaningful workforce insights and analytics comes within reach. Even advancements in cloud-based systems for human capital management are coming packaged with analytics and visualization capabilities, enabling HR leaders to integrate people data with other data sources, such as customer relationship management, for a full view of the business.
Originality/value
This paper will be of value to HR leaders and practitioners who wish to use predictive analytics and emerging technology to drive performance improvement and gain the insights about their workforces.
Details
Keywords
Tobias Klatt, Marten Schlaefke and Klaus Moeller
Over the past few years, developments in business analytics have provided strategic planners with promising instruments for dealing with turbulent environments. This study aims to…
Abstract
Purpose
Over the past few years, developments in business analytics have provided strategic planners with promising instruments for dealing with turbulent environments. This study aims to reveal whether or not the application of business analytics in strategic planning contributes to better company performance, and to formulate recommendations on how to integrate business analytics in companies' performance management systems.
Design/methodology/approach
Based on a survey conducted with 89 respondents from high‐technology firms, a group comparison between firms with strong performance and those with weak performance reveals significant differences between the two groups' strategic planning processes and application of business analytics.
Findings
The empirical survey's results show that better‐performing companies are characterized by a more sophisticated analytical planning process. Lower‐performing firms acknowledge this competitive advantage. Based on these findings, the authors develop recommendations on how to integrate business analytics in performance management contexts.
Research limitations
The empirical study's results are limited to high‐technology industries in the cultural setting of Germany.
Practical implications
The empirical results emphasize the competitive advantage gained by applying business analytics. The recommendations concerning analytical performance management should help managers to sensibly integrate the analytical toolbox in performance management contexts.
Originality/value
This paper combines insights on the best usage of business analytics from the perspective of strategic planning experts, with recommendations for the integration of business analytics into the performance management framework from an academic perspective.
Details
Keywords
Anthony Marshall, Stefan Mueck and Rebecca Shockley
To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation…
Abstract
Purpose
To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation.
Design/methodology/approach
Researchers asked about innovation goals, barriers to innovation, metrics used to measure innovation outcomes, treatment and types of innovation projects and the role of big data and analytics in innovation processes.
Findings
Three distinct groups emerged: Leaders, Strivers and Strugglers. Leaders are markedly different as a group: they innovate using big data and analytics within a structured approach, and they focus in particular on collaboration.
Research limitations/implications
Respondents were from the 2014 IBM Innovation Survey. We conducted cluster analysis with 81 variables. The three cluster solution was determined deploying latent class analysis (LCA), a family of techniques based around clustering and data reduction for segmentation projects. It uses a number of underlying statistical models to capture differences between observed data or stimuli in the form of discrete (unordered) population segments; group segments; ordered factors (segments with an underlying numeric order); continuous factors; or mixtures of the above.
Practical implications
Leaders don’t just embrace analytics and actionable insights; they take them to the next level, integrating analytics and insights with innovation. Leaders follow three basic strategies that center on data, skills and tools and culture: promote excellent data quality and accessibility; make analytics and innovation a part of every role; build a quantitative innovation culture.
Originality/value
The research found that leaders leverage big data and analytics more effectively over a wider range of organizational processes and functions. They are significantly better at leveraging big data and analytics throughout the innovation process – from conceiving new ideas to creating new business models and developing new products and services.
Details
Keywords
Yixue Shen, Naomi Brookes, Luis Lattuf Flores and Julia Brettschneider
In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging…
Abstract
Purpose
In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.
Design/methodology/approach
We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.
Findings
The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.
Originality/value
Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.
Details
Keywords
Kojo Kakra Twum and Andrews Agya Yalley
The use of innovative technologies by firm employees is a key factor in ensuring the competitiveness of firms. However, researchers and practitioners have been concerned about the…
Abstract
Purpose
The use of innovative technologies by firm employees is a key factor in ensuring the competitiveness of firms. However, researchers and practitioners have been concerned about the willingness of technology end users to use innovative technologies. This study, therefore, aims to determine the factors affecting the intention to use marketing analytics technology.
Design/methodology/approach
This study surveyed 213 firm employees. The quantitative data collected was analysed using partial least squares structural equation modelling.
Findings
The results reveal that performance expectancy, facilitating conditions, attitudes and perceived trust have a positive and significant effect on intentions to use marketing analytics. Effort expectancy, social influence and personal innovativeness in information technology were found not to predict intentions to use marketing analytics.
Practical implications
This study has practical implications for firms seeking to enhance the use of marketing analytics technology in developing countries.
Originality/value
This study contributes to the use of UTAUT, perceived trust, personal innovativeness and user attitude in predicting the intentions to use marketing analytics technology.
Details
Keywords
Previous studies focus on the direct effects of marketing analytics on entrepreneurial performance, but few explore the underlying mechanisms. Drawing on affordance theory, this…
Abstract
Purpose
Previous studies focus on the direct effects of marketing analytics on entrepreneurial performance, but few explore the underlying mechanisms. Drawing on affordance theory, this study explores pathways through new product innovation (NPI) for the effects of marketing analytics on business performance. NPI is a market-based innovation concept comprising customer- and competitor-driven NPD and incremental innovation.
Design/methodology/approach
Using survey data collected from UK-based entrepreneurial firms operating in the IT and telecoms industries, we apply confirmatory factor analysis and a sequential structural equation model to test the mediating role of NPI in the effect of marketing analytics on market performance and financial performance.
Findings
The results show that marketing analytics enhances business performance through competitor-driven but not customer-driven NPD. Although using marketing analytics to generate customer knowledge for existing product innovation may enhance market performance, this positive effect becomes negative when competitor-driven NPD is undertaken to improve existing product innovation.
Originality/value
This study makes significant contributions to the innovation and NPD literature. It delves deeper into the existing view on the positive contributions of customer engagement to business value creation, revealing the significance of competitor knowledge to enhance business performance through marketing analytics, particularly in the context of IT and telecoms entrepreneurial firms.
Details
Keywords
For over a decade now, various stakeholders in accounting education have called for the integration of technology competencies in the accounting curriculum (Association to Advance…
Abstract
For over a decade now, various stakeholders in accounting education have called for the integration of technology competencies in the accounting curriculum (Association to Advance Collegiate Schools of Business (AACSB), 2013, 2018; Accounting Education Change Commission (AECC), 1990; American Institute of Certified Public Accountant (AICPA), 1996; Behn et al., 2012; Lawson et al., 2014; PricewaterhouseCoopers (PWC), 2013). In addition to stakeholder expectations, the inclusion of data analytics as a key area in both the business and accounting accreditation standards of the AACSB signals the urgent need for accounting programs to incorporate data analytics into their accounting curricula. This paper examines the extent of the integration of data analytics in the curricula of accounting programs with separate accounting AACSB accreditation. The paper also identifies possible barriers to integrating data analytics into the accounting curriculum. The results of this study indicate that of the 177 AACSB-accredited accounting programs, 79 (44.6%) offer data analytics courses at either the undergraduate or graduate level or as a special track. The results also indicate that 41 (23.16%) offer data analytics courses in their undergraduate curriculum, 61 (35.88%) at the graduate level, and 12 (6.80%) offer specialized tracks for accounting data analytics. Taken together, the findings indicate an encouraging trend, albeit slow, toward the integration of data analytics into the accounting curriculum.
Details
Keywords
Sean Mackney and Robin Shields
This chapter examines the application of learning analytics techniques within higher education – learning analytics – and its application in supporting “student success.” Learning…
Abstract
This chapter examines the application of learning analytics techniques within higher education – learning analytics – and its application in supporting “student success.” Learning analytics focuses on the practice of using data about students to inform interventions aimed at improving outcomes (e.g., retention, graduation, and learning outcomes), and it is a rapidly growing area of educational practice within higher education institutions (HEIs). This growth is spurring a number of commercial developments, with many companies offering “analytics solutions” to universities across the world. We review the origins of learning analytics and identify drives for its growth. We then discuss some possible implications for this growth, which focus on the ethics of data collection, use and sharing.
Details